Page 4 – InMoment

How Customer Feedback Can Boost Your Bottom Line

Customer feedback is any information from customers about their experience with a product or service from a specific company. A clear method for gathering and acting on customer feedback is one of the most important steps in creating a complete customer feedback strategy since it shows where companies should focus their efforts to drive long-term growth.

What Is Customer Feedback?

Customer feedback can take many forms, but it is defined as any information from customers about their experience with a product or service from a specific company. This can include the customer’s opinions, complaints, suggestions, and compliments. The goal of customer feedback is to understand and meet customer needs and expectations to improve products, services, and overall customer satisfaction. Businesses can collect feedback actively and passively. But no matter how it is collected, it should drive change and action within the company.

What Is A Customer Feedback Loop?

A customer feedback loop is the complete process of getting feedback, analyzing it, and then responding to it or implementing the feedback into the business. An example of this would be a restaurant collecting customer feedback through surveys, analyzing the responses to identify common complaints about the menu, and then updating the menu items or recipes to better meet customer preferences.

This differs from customer feedback itself because it refers to the entire process of closing the loop, while customer feedback itself just represents the form in which the customer communicates with your organization.

Why Is Customer Feedback Important?

Customer feedback is so important because it connects you with your customers. It can help you identify areas for improvement, as well as highlight strengths. Furthermore, when customers see that their feedback is valued and acted upon, it fosters a sense of trust and loyalty, encouraging repeat business and positive word-of-mouth.

What Are the Benefits of Collecting Customer Feedback?

Businesses should collect customer feedback for several reasons, including measuring customer satisfaction and loyalty, improving products and services, and identifying new opportunities for growth or improvement. On the flip side, not addressing these issues could create negative business impacts, or even leave money on the table. Check out our VoC ROI calculator below to see how much benefit your organization could realize from investing in customer experience and customer feedback!

Collecting customer feedback can aid in many business initiatives such as:

Improve Customer Satisfaction

One of the most visible benefits of gathering and analyzing customer feedback is the improvement in customer satisfaction. Customer feedback can help businesses understand what customers like and dislike about their products or services. By addressing areas of dissatisfaction, businesses can improve the customer experience which increases customer satisfaction.

Improve Business

Customer feedback allows businesses to more quickly and effectively identify problems with their product, services, or experiences. Once the problem has been caught, the business can improve its offerings. By listening to customer feedback, businesses can make changes to their products or services to better meet customer needs.

Identify New Opportunities

Customer feedback can also bring to light new opportunities or ideas for the business, whether that is a new product or a better way of providing customer support. The direct and indirect feedback that a business receives from customers can help identify areas for expansion or new services that customers may be interested in.

Build Customer Loyalty

Customers like knowing that the companies they frequent are interested in hearing and acting on customer feedback. By actively seeking customer feedback and making changes based on that feedback, businesses can build customer loyalty and foster long-term relationships with their returning customers.

Calculate your business’ ROI using InMoment’s VoC tools.

Industry

Number of Customers

Annual revenue Per Customer ($)

Lead to Conversion Rate (%)

Number of calls per year

Percent of calls where issue is not resolved (%)

Estimated Revenue Growth

$
Use the calculator to find an estimated ROI

Total ICX ROI

Direct vs Indirect Customer Feedback

The two most common categories that customer feedback can be classified as are direct and indirect. While most customer service strategies focus on collecting direct feedback, indirect feedback can offer a more accurate picture of how customers feel about the company as a whole.

Direct Feedback

This type of feedback is provided directly by the customer to the business. It is most often provided through surveys, customer service interactions, and focus groups. Although it shortens the process of collecting feedback, it has the risk of gathering responses that are not as honest since customers may just say what they think the business wants to hear.

Indirect Feedback

Indirect feedback is provided by customers through their behavior and actions. It includes customer reviews, social media comments, and website analytics. Indirect feedback is usually an accurate depiction of how customers feel, but it does require a little more effort to gather and analyze.

Types of Customer Customer Feedback

Your customers can interact with your business in a number of ways. They will almost certainly communicate with you in more than one channel, so it is important to your customer feedback program that you understand all the different types of customer feedback.

Surveys

Surveys can be a powerful and useful tool for collecting customer feedback. A common survey use case would be a post-transactional customer satisfaction survey. Surveys can be used to measure aspects of the customer experience such as quality of the product purchased or the ease of the transaction. One of the great things about surveys is that they can be conducted online, over the phone, or in person. Most often, companies send e-mail surveys for customers to provide feedback about their experience.

Contact Center Interactions

The customer service department, often run out of an omnichannel contact center, is often the first point of contact for customers who have feedback or complaints. Contact center representatives can gather feedback and relay it to the appropriate teams so that the company can adjust its approach in response to customer feedback. A positive experience with the contact center can also improve the customer’s overall satisfaction if their concerns are addressed in a constructive and timely manner.

Social Media

Social media platforms and social media marketing tools are becoming increasingly popular and effective sources of customer feedback. Customers may use social media to share their experiences, both positive and negative. Although social media can be used to gather direct feedback from surveys and polls, most often the feedback will be indirect and qualitative in nature.

Customer Reviews

Finally, customer reviews can be a valuable source of feedback. Reviews can be collected directly on a business’s website or on third-party review sites. These reviews play a significant role in how potential customers view a company, so it is important for businesses to stay active and engaged in monitoring and knowing how to respond to Google reviews as well as any other types of reviews.

How to Analyze Customer Feedback

It is not enough to simply gather as much customer feedback as possible. The true value comes when businesses analyze customer feedback and turn it into action. There are, of course, a few ways to analyze customer feedback. Not all methods of analysis are appropriate for each type of feedback and insight that businesses are looking for, so companies can use any combination of the following based on their needs.

Text Analysis

Text analysis, specifically text analysis software, is a method of analyzing customer feedback that involves using natural language processing (NLP) to extract insights from text data. If a business sends out a survey that includes open-response questions, it may want to use text analysis to identify common themes, sentiments, and topics in customer responses.

Customer Experience Metrics

Many forms of customer feedback offer numbers and ratings that make up customer experience metrics. These metrics are measures used to track the overall satisfaction of customers. The most common customer experience metrics include the Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES).

Net Promoter Score: The NPS is one of the most straightforward metrics that measures how likely a customer is to recommend the brand to their friends and family. It is usually gathered by asking customers to rate their willingness to recommend the product or service on a scale of one to ten.
Customer Satisfaction Score: CSAT is also relatively straightforward and measures how happy customers are with the product or service they received. Customers rate their satisfaction with the product or service on a scale of one to five.
Customer Effort Score: A CES uses a scale from one to seven to determine how easy customers feel it is to interact with the company and use its products or services.

Customer Journey Mapping

The process of customer journey mapping can be a great way to analyze feedback at different touchpoints in the customer journey to identify potential bottlenecks and opportunities for improvement. If a retail business is getting a lot of feedback about poor customer service, customer journey mapping would help them find out if this is happening in-store, or in the post-purchase phase.

How to Develop A Customer Feedback Strategy

A customer feedback strategy works as part of the customer feedback loop. Your customer feedback strategy will be a detailed plan of how your business will go about collecting feedback from your customers, as well as how that feedback is implemented into the business.

1. Define Your Objectives

To develop an effective customer feedback strategy, you need to have a clear idea of what you want to achieve. It is important to start by defining your main objectives. Whether you want to improve customer retention, make product or service improvements, or identify new opportunities, these goals will help you keep a clear purpose when collecting feedback.

2. Identify Feedback Channels

Next, determine which feedback channels make the most sense for your business. These channels should represent the most common ways your customers communicate with you, and where you will have the biggest likelihood of hearing honest feedback. For example, a restaurant may choose to focus on the feedback received from post-transactional email surveys, while an e-commerce business may focus more on online reviews and social media posts. These channels cover areas where each respective business is most likely to hear from and communicate with their customer.

3. Design Effective Feedback Tools

Once your feedback channels have been selected, you need to focus on survey design. Ensure your surveys and questionnaires are concise and focused on specific aspects of the customer experience. When designing your feedback tools, it is useful to have a mix of qualitative and quantitative questions, such as rating scales combined with an open-ended question, to get a holistic view of the customer experience.

Screen capture of survey design software where the user can choose the types of and order of the questions.

4. Implement a Feedback Collection System

Choose the right tools and platforms to collect and manage feedback efficiently. You can use tools such as reputation management software to help streamline the feedback collection process. Regardless of the feedback collection system you use, remember that the most successful feedback methods are ones that can aggregate feedback from multiple sources in one place.

5. Analyze the Feedback

After the feedback is collected, it is time time to analyze what your customers have said. Arguably the most important step in the entire process, this allows you to identify patterns and trends that will lead to actions that will have a positive impact on your business. When analyzing the feedback, use techniques such as sentiment analysis to gauge customer emotions and categorize feedback to pinpoint specific areas of concern or opportunity. Your customers’ feedback is not something that is only analyzed once, it will change over time and will constantly need to be monitored. Be sure to have resources in place that can continuously keep up with what your customers are saying.

6. Close the Feedback Loop

Closing the customer feedback loop involves responding to customer feedback and making necessary changes based on the insights gathered. Knowing how to respond to reviews will help you communicate to your customers that their feedback has been received and is valued. This step also involves outlining to the customer how their feedback is being implemented and following up with them to make sure they are satisfied.

7. Prioritize Business Actions

When your customer or client feedback has been received and analyzed, you may end up with a lot of actions that need to be taken to achieve your previously established goal. List all of the actions you could take, and prioritize them based on the impact on customer satisfaction, the feasibility of implementation, and alignment with business goals. There needs to be a balance of quick wins that will drive immediate success as well as long-term strategic initiatives.

8. Monitor and Measure Results

At the beginning of developing your customer feedback strategy, you should have picked an objective or metric that you were going to measure. After customer feedback has been collected, analyzed, and implemented, it is now time to measure the results against your chosen objective to benchmark performance. As changes are continuously implemented, these metrics need to be regularly assessed to measure the impact of your strategy.

It is possible that you may not see the results you were hoping for, in which case your strategy might need to be updated. This may look like changing your feedback collection methods, your survey design, or the objective you are focused on.

9. Foster a Culture of Continuous Improvement

Customer feedback is most effectively collected and implemented when there is a company-wide culture that values it. Ensure all team members understand the importance of feedback and are equipped to contribute to the feedback process. Furthermore, be sure to celebrate the success of your customer feedback initiatives. Whether it is recognizing an employee mentioned in an online review, or a store location that realized increased customer satisfaction. Making all employees feel a part of the customer feedback process will ensure increased participation and success.

10. Iterate and Adapt

Regardless of your business or industry, customer needs and market conditions are always evolving. To make sure your business does not fall behind, it is important to regularly revisit and refine your customer feedback strategy. Be willing to implement any new feedback channels, tools, or methodologies as you see fit to ensure the continuous success of your customer feedback program.

Best Practices for Collecting Customer Feedback

Collecting customer feedback effectively is crucial for gaining valuable insights that can drive improvements and enhance customer satisfaction. Here are some best practices to ensure you gather meaningful and actionable feedback:

Ensure Anonymity

In some forms of feedback, such as online reviews or social media posts, the customer chooses to let themselves be identified. However, in other forms of feedback, such as email surveys, remaining anonymous is important to the customer and allows them to provide honest and complete feedback. Assure customers that their feedback will be confidential and used solely for improvement purposes.

Focus on Timing

Timing is one of the most critical factors in collecting customer or client feedback. It is best to ask for feedback soon after a customer interaction, such as a purchase or product delivery, while the interaction or experience is fresh in their mind. For feedback that isn’t necessarily based on a recent customer interaction, such as a biannual customer satisfaction survey, it is best to send the survey at a time when your customers will have time to respond.

Thank Customers for Their Feedback

Businesses should also thank customers for their feedback and express appreciation. This can help build customer loyalty and foster long-term relationships with customers. Additionally, repeat customers are more likely to provide honest feedback in the future if they feel that their responses are valued and used to make improvements.

Collect Customer Feedback with InMoment

InMoment’s XI Platform gives you the tools to gather and analyze customer feedback in whatever way works best for your business. InMoment’s tools enable you to stay ahead of your competitors and deliver exceptional customer experiences. Schedule a demo today to see what InMoment can do for you.

In this Post

What Is Customer Feedback?What Is A Customer Feedback Loop?Why Is Customer Feedback Important?What Are the Benefits of Collecting Customer Feedback?Direct vs Indirect Customer Feedback Types of Customer Customer Feedback How to Analyze Customer Feedback How to Develop A Customer Feedback Strategy Best Practices for Collecting Customer Feedback Collect Customer Feedback with InMoment

Watch the Video – Get More Out of Your CX Program

Watch this demo video to see how InMoment’s award-winning AI tech does the heavy lifting on analysis so you can spend more time lifting CX results.

Updated October 3, 2024

Published July 18, 2024

Written by Mike Henry

Reading time 12 minutes

5 Tips to Expertly Manage & Boost Your Online Reputation

Managing an online reputation has become a cornerstone for businesses striving to enhance customer acquisition, retention, and growth. In today's digital age, consumers are highly informed and interconnected, amplifying their voices and influence.

A staggering 94% of consumers avoid a business due to negative reviews. Platforms like X, Meta/Insta, YouTube, and Google shape brand perceptions and impact purchasing decisions, directly affecting your ROI. Understanding and responding to social data and reviews to drive actionable business outcomes is crucial. Customers expect not only to be heard but also understood and swiftly responded to.

With this in mind, we want to make it clear that it isn’t just about monitoring and management of these feedback channels, but actively engaging with and learning from your customers. By doing so, you meet their expectations and stay ahead of the curve by anticipating needs and adapting experiences in real-time. The result is a robust brand reputation, increased customer loyalty, and ultimately, improved business results. Overlooking it might spell your downfall.

The Role of Social Media and Reviews in Online Reputation Management

For years now, social media and online reviews have emerged as pivotal elements in shaping consumer behavior and business outcomes. Their role in managing an online reputation cannot be overstated, as they directly influence brand perception, customer trust, and purchasing decisions. This high level of reliance on social media for product information means that businesses must maintain an active and positive presence.

Positive feedback and high ratings can significantly bolster a brand’s reputation, making it more attractive to potential customers. For instance, glowing reviews and endorsements can act as powerful testimonials that sway undecided consumers. They provide social proof, reassuring potential customers about the quality and reliability of a product or service. Conversely, negative comments and low ratings can highlight areas needing improvement, presenting businesses with opportunities to refine their products and services. If managed well, even negative feedback can be valuable, offering insights into customer pain points and areas where the business can improve.

Moreover, the unstructured data generated from social media and reviews can be a goldmine for businesses. Analyzing this data provides deep insights into customer preferences, emerging trends, and common pain points. This information is invaluable for making informed decisions and tailoring marketing strategies to better meet customer needs. Advanced analytics tools, such as sentiment analysis and natural language processing, can further enhance the understanding of customer feedback, enabling businesses to respond proactively and strategically.

Calculate your business’ ROI using InMoment’s reputation management tools.

Industry

Number of Locations

Monthly Revenue per Customer ($)

Estimated Revenue Growth

$
Use the calculator to find an estimated ROI

Total ICX ROI

Tips for Managing Your Online Reputation

1. Implement Robust Data Collection Mechanisms

A comprehensive approach to managing online reputation begins with robust data collection. Utilizing an Integrated CX (Customer Experience) platform allows businesses to unify all types of customer data—structured, unstructured, and semi-structured—into a single dashboard. This consolidated view is crucial for monitoring social data, star ratings, customer sentiment, and reviews efficiently.

For example, a business looking to improve its retail customer experience can use a CX platform to track customer feedback from various sources such as Facebook, Google Reviews, Twitter, and email surveys. By aggregating this data into one dashboard, the business gains a holistic view of customer sentiment and can identify patterns or recurring issues. This comprehensive insight enables the company to make informed decisions and address potential problems before they escalate. Tools like InMoment or Medallia offer such platforms, providing businesses with real-time analytics and visualizations to streamline data management.

2. Respond Swiftly to Feedback

Business managing their online reputation by responding to negative feedback.

Timely responses to customer feedback are essential for maintaining a positive online reputation. Ensuring your team promptly addresses inquiries, complaints, and feedback can significantly enhance customer satisfaction and boost your brand’s reputation for attentiveness and service excellence.

Consider a scenario where a customer posts a complaint about delayed shipping on Twitter. If the company responds within minutes, offering an apology and a solution, it demonstrates a high level of customer care and responsiveness. This immediate engagement not only resolves the issue but also showcases the brand’s commitment to customer service. Companies like Zappos are well-known for their rapid and effective customer service responses on social media, which have helped build a loyal customer base.

3. Leverage Predictive Analytics

AI-driven predictive analytics can transform how businesses manage their online reputation by identifying actionable insights and optimizing customer experiences. Predictive analytics helps in anticipating customer needs and adapting strategies in real-time.

For instance, a hotel chain could use predictive analytics to analyze past guest reviews and predict future trends in customer satisfaction. By identifying patterns—such as frequent complaints about room cleanliness during peak seasons—the hotel can proactively address these issues before they arise, ensuring a better guest experience. Predictive analytics tools like IBM Watson and Google Analytics can provide these capabilities, allowing businesses to stay ahead of customer expectations and maintain a positive reputation.

4. Personalized Customer Interactions

Deepening the understanding of individual customer preferences and behaviors is key to personalizing communications and offers. Tailoring experiences based on current trends and insights enhances customer engagement and loyalty.

A personalized approach can be as simple as addressing customers by their names in emails or as sophisticated as recommending products based on previous purchases and browsing behavior. For example, Netflix excels at personalizing user experiences by offering content recommendations based on viewing history. This level of personalization makes customers feel valued and understood, fostering a stronger connection to the brand. Utilizing CRM tools like Salesforce or HubSpot can help businesses gather and analyze customer data to create tailored marketing strategies.

5. Establish Training and Policies

Equipping your team with the necessary training and policies is crucial for handling social interactions effectively. Training should focus on best practices for engaging with customers online, managing crises, and maintaining a consistent brand voice. Clear guidelines ensure all team members know how to handle sensitive situations appropriately.

For instance, a social media team should be trained on how to respond to reviews, both positive and negative feedback professionally and empathetically. During a crisis, such as a product recall, having predefined response protocols can help manage the situation more efficiently and reduce potential damage to the brand’s reputation. Establishing a social media policy that outlines acceptable responses, tone of voice, and escalation procedures ensures that all interactions align with the company’s values and objectives.

The Stakes of Ignoring Social Media and Reviews

Missed Engagement Opportunities

Ignoring social media and reviews means missing direct conversations with customers, which can significantly impact a business’s reputation and customer satisfaction. Social media platforms and review sites provide unparalleled opportunities for engagement, from addressing complaints to highlighting positive experiences. Engaging with customers on these platforms allows businesses to manage their brand narrative, build relationships, and foster loyalty.

For example, a restaurant that actively responds to Yelp reviews—both positive and negative—demonstrates to customers that it values their feedback. By thanking customers for positive reviews and addressing concerns raised in negative reviews, the restaurant can show its commitment to continuous improvement and customer satisfaction. Conversely, ignoring these reviews diminishes the brand’s voice, leading to a lack of control over the narrative. This passive approach can result in missed opportunities to turn negative experiences into positive ones, leaving customers feeling unheard and undervalued.

Similarly, social media platforms like Twitter and Instagram offer businesses the chance to engage in real-time conversations with their customers. When a customer tweets a complaint about a delayed product delivery, a quick response from the business acknowledging the issue and providing a solution can turn a negative experience into a positive one. This type of engagement not only resolves the immediate issue but also shows other potential customers that the business is attentive and responsive.

Reputation Damage

A brand is often judged by its worst review, especially if it goes unaddressed. Negative reviews can spread quickly across social media and review platforms, significantly damaging a brand’s reputation if not managed promptly and effectively. For instance, a single unresolved complaint about poor service can be shared widely, creating a negative perception of the brand that can deter potential customers.

Active management of social media and reviews can turn potential crises into stories of excellent customer service. For example, a hotel that receives a complaint about cleanliness can respond publicly, apologize, and offer a resolution, such as a discount on a future stay or immediate housekeeping service. This proactive approach not only resolves the issue for the affected customer but also demonstrates to other potential customers that the hotel takes feedback seriously and is committed to maintaining high standards. By addressing negative reviews head-on, businesses can mitigate the damage to their reputation and even improve their standing with customers.

Consider the example of United Airlines, which faced a major backlash after a video of a passenger being forcibly removed from an overbooked flight went viral. The initial response from the airline was widely criticized as inadequate and insincere, leading to significant reputational damage and a loss of customer trust. In contrast, when Southwest Airlines faced a crisis due to flight cancellations, their swift and transparent communication with customers, including frequent updates and compensation offers, helped mitigate the negative impact and preserved customer loyalty.

Competitive Disadvantage

Competitors who effectively leverage social media and reviews often enjoy enhanced customer loyalty and trust. They can engage with their audience, respond to feedback, and make informed improvements based on customer insights. By neglecting these platforms, a business risks falling behind, missing out on valuable insights that could drive innovation, and failing to connect with a tech-savvy customer base that values transparency and responsiveness.

For instance, consider two competing retail brands: one actively engages with customers on social media, responding to queries, resolving complaints, and thanking customers for positive feedback; the other remains silent. The brand that engages builds a community of loyal customers who appreciate the interaction and responsiveness. This brand is also more likely to attract new customers who see positive interactions and feel confident in the brand’s commitment to customer service.

Let Inmoment Help You Manage Your Online Reputation

Managing an online reputation requires a multifaceted approach that includes robust data collection, swift responses to feedback, predictive analytics, personalized customer interactions, and thorough training and policies. By implementing these strategies, businesses can maintain a positive online presence, enhance customer satisfaction, and drive long-term success.

By actively engaging with customer feedback across various platforms, you can significantly improve your brand reputation, increase customer loyalty, and drive business growth.

Schedule a demo with us today to advance your online reputation management strategy!

References

“Social Commerce Statistics.” Porch Group Media, (https://porchgroupmedia.com/blog/social-commerce-statistics/) Accessed 7 May 2024.

In this Post

The Role of Social Media and Reviews in Online Reputation Management Tips for Managing Your Online Reputation The Stakes of Ignoring Social Media and Reviews

Watch The Video – Make Online Reputation Your Acquisition Strategy

97% of consumers scope out online reviews before making a decision – watch this video to see how InMoment’s solution grows your business through online reputation management.

Updated September 20, 2024

Published July 16, 2024

Written by Dan Butler

Reading time 8 minutes

Speech Analytics: Turning Conversations into Actionable Insights

Speech analytics encompasses the transformation and analysis of audio recordings into text. This analysis provides businesses with key consumer insights, such as emotional tags and trending complaints. It can be used by businesses to understand the customer experience and make business improvements.

Speech analytics is quickly becoming a foundational aspect of successful experience improvement programs. Historically, it has been difficult to quantify metrics from customer calls. However, the rise of speech analytics has given businesses to understand their customers like never before.

What is Speech Analytics?

Speech analytics is the process of analyzing recorded calls to gather customer information to improve communication and future interaction. It uses advanced technology to transcribe and analyze audio recordings. In doing so, speech analytics gives businesses the ability to uncover insights into customer behavior, sentiment, and preferences. This allows companies to enhance their customer service, marketing strategies, and overall operational efficiency.

What is Contact Center Speech Analytics?

Contact center speech analytics specifically refers to the application of speech analytics technology in a call center environment. Usually working as part of contact center solutions, it involves analyzing the vast volumes of calls handled by contact centers to extract actionable insights from audio recordings.

Contact center speech analytics is useful for businesses looking to improve their contact center performance and overall customer experience. The insights from recorded calls help identify common issues and train agents, which helps improve key customer experience metrics.

By leveraging this technology, contact centers can turn every customer interaction into a valuable data point that drives continuous improvement.

How Does Speech Analytics Work?

Speech analytics combines several advanced technologies to analyze spoken language. The process involves multiple steps:

Capturing Audio: The first step is recording conversations between customers and agents. These recordings are stored for analysis.
Automatic Speech Recognition (ASR): ASR technology converts spoken words into text. This is a crucial step as it translates the audio data into a format that can be analyzed.
Natural Language Processing (NLP): Natural language processing algorithms process the transcribed text to understand the context, sentiment, and meaning behind the words. This involves parsing the text and identifying keywords, phrases, and patterns.
Machine Learning: Machine learning models analyze the processed text to extract insights. These models can identify trends, detect anomalies, and predict outcomes based on historical data.
Share Insights: The final step is generating reports and dashboards that present the insights in an easily digestible manner. These insights can be shared with managers, executives, or other stakeholders in order to make informed decisions.

Why is Speech Analytics Important?

Speech analytics is important because it gives you a way of understanding your customers that may not have been previously accessible. A recent study showed that 86% of consumers preferred to talk to a real agent rather than a chatbot or AI-based system regarding customer service inquiries. If you did not have speech analytics software, the only way you would be able to identify common themes and trends is to manually listen to recordings. This is a costly and time-consuming process.

With speech analytics and speech analytics software, you are still able to provide the consumer with the experience they are looking for, while also getting the analysis and data that is important to your business.

What are the Benefits of Speech Analytics?

The benefits of speech analytics extend beyond just sentiment analysis. These tools can help you train employees, develop marketing campaigns, and invest in business improvement that will make an immediate impact. Here are some common benefits of utilizing speech analytics:

Improved Agent Performance

Speech analytics can be a tool to help benchmark agent performance. The insights from speech analytics can help identify the strengths and weaknesses of agents. Businesses can identify best practices to be taught in onboarding and training sessions by analyzing interactions with multiple agents.

A dashboard showing a call center agent's performance in the current month comapred to the previous month.

Enhanced Customer Understanding

By utilizing speech analytics software, businesses can gain a deeper understanding of their customers by analyzing the context and content of their conversations. They have the ability to view a conversation in its entirety and identify specific emotional tags that arose throughout the course of the case. Identifying these themes will help companies meet customer expectations, which will lead to increased customer satisfaction and loyalty.

Real-time Problem Resolution

With real-time speech analytics, businesses can detect and address issues as they occur during customer interactions. This immediate insight allows supervisors to intervene when necessary, preventing escalation and improving the chances of resolving issues on the first call. Real-time analytics also helps in managing high-stress situations and ensuring that customer concerns are addressed promptly and effectively.

Speech Analytics Uses Cases & Examples

The applications of speech analytics are not limited to one industry or use case. This form of analytics has a wide range of capabilities that can be used from healthcare to e-commerce. The following examples highlight different speech analytics use cases

Speech Analytics in Healthcare

Consider a large healthcare contact center that handles patient inquiries, appointment scheduling, prescription refills, and other services. The nature of this contact center means that they handle thousands of calls daily, and manually listening to the recordings would be an ineffective use of time.

By implementing speech analytics software, all incoming and outgoing calls can be recorded and stored securely in compliance with HIPAA regulations. These calls are transcribed into text, analyzed, and categorized by common tags such as calls related to appointments, billing, or prescription refills.

After the calls are categorized, the trends can be analyzed. Perhaps this call center notices that over the last 90 days, there has been an increase in calls related to long wait times. These insights are shared with hospital management, which may result in an increase in staffing during certain hours to decrease wait times. By utilizing speech analytics, this healthcare organization was able to increase patient satisfaction.

Speech Analytics in E-Commerce

Imagine an e-commerce company that operates an omnichannel contact center. This contact center handles customer inquiries across multiple channels such as phone, email, online chat, and even review management. This contact center may already have a text analytics solution in place to tag and categorize customer feedback in the form of text data. However, being unable to effectively tag customer calls, these are left out of the data. This leads to an incomplete picture of the e-commerce customer experience.

By implementing speech analytics, customer calls can be recorded in a way that allows them to be added to the rest of the contact center customer data. This provides the business with a complete view of its customer experience and helps identify areas of improvement.

What is Next For Speech Analytics?

While speech analytics is not necessarily in its infancy, it has been no stranger to increased growth. The future of speech analytics is closely tied to advancements in AI and machine learning. These AI algorithms will enable better detection of emotions and sentiments. You can expect speech analytics to improve in two major ways in the coming future:

AI Speech Analytics

AI speech analytics can be used as another way to describe the current capabilities of speech analytics because of the use of machine learning. However, AI speech analytics will continue to grow by going beyond just categorizing audio recordings. After identifying different emotional tags and trends, AI speech analytics will be able to quickly summarize the findings and offer immediate areas for improvement. This, as opposed to presenting the data and requiring human input, will make the end-to-end process of speech analytics quicker.

Of course, as these capabilities grow, there will be errors, and all important data and decisions should be human-reviewed. That being said, the continued growth of speech analytics will most likely result in the further optimization and improvement of businesses.

Holistic Analytics

Holistic analytics represents the combination of speech analytics, text analytics, and video analytics. Putting these solutions together will make it easier for organizations to understand their customers, regardless of the channel they choose to communicate with them from. Also, as a packaged deal, it will be more cost-effective and most likely provide a better return on investment.

What to Look For in Speech Analytics Software

Not all speech analytics software comes with the same features. The features that are the most important will depend on the specific goals of your business. However, there are certain features that should be must-haves when looking for a speech analytics solution.

Diverse Analysis Capabilities

While some businesses may settle for a speech analytics solution that is limited to only analyzing the customer during the call. Advanced speech analytics solutions will have a wide range of analysis capabilities that are able to analyze customer emotions as well as how the agent is responding to the customer and how the two parties interact. By analyzing the conversation in its entirety, you gain access to a more complete analysis.

A conversation between a contact center agent and a customer, where AI speech analytics has highlighted customer sentiment.

Integration Capabilities

It is important to choose a solution that supports a wide range of integrations, such as integrating into your contact center analytics software. Being able to implement speech analytics software that works alongside your current systems will increase the adoption rate within the organization and get the most out of the software as soon as possible.

Support

It is important to choose speech analytics software that comes with a dedicated support team. This is important because having expert support ensures that any issues or challenges are promptly addressed.

Best Practices for Implementing Speech Analytics Software

In order to complete a smooth implementation of speech analytics software, it is best to have a plan. Having an implementation plan will make sure the organization can succeed with the software, and get all appropriate staff involved in the right order.

1. Define Objectives

By defining your objectives, you clearly outline what you are aiming to achieve. This will help you stay focused and avoid any distractions that come up in the early stages of adoption. For example, you may be tempted to set up a feature you did not fully understand in the demo. But, doing so wouldn’t allow your team to use the core functionalities they need. By having a goal, you can make sure your team has what they need as soon as possible.

2. Integrate with Existing Systems

During the implementation process, it is vital to ensure that the software works well within your current technology stack. If there is an issue, contact your support team as soon as possible to try and get the issue resolved.

3. Train Employees

As soon as the software is accessible to all employees, provide comprehensive training on how to use the software and interpret its insights. This step is crucial to the adoption across the company. Avoiding this will cause employees to make mistakes, which may take more time to resolve.

4. Monitor

After the initial implementation push is over, monitoring the chosen software is important to its continued success. As it becomes a more integral part of your business processes, certain discrepancies may arise that need to be addressed. Conversely, all early successes with the software need to be highlighted so they can be recreated in the future.

Choose InMoment as Your Speech Analytics Solution

InMoment’s conversation analytics software allows your business to have access to state-of-the-art speech analytics software, as well as other capabilities such as AI summarization, agent and coach scorecards, and more! See what InMoment can do for you by scheduling a demo today.

References

CGS. CGS Survey Reveals Consumers Prefer a Hybrid AI/Human Approach to Customer Service. Is there Chatbot Fatigue? (https://www.cgsinc.com/en/resources/2019-cgs-customer-service-chatbots-channels-survey). Accessed 6/27/2024.

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What is Speech Analytics?How Does Speech Analytics Work?Why is Speech Analytics Important?What are the Benefits of Speech Analytics?Speech Analytics Uses Cases & Examples What is Next For Speech Analytics?What to Look For in Speech Analytics Software Best Practices for Implementing Speech Analytics Software Choose InMoment as Your Speech Analytics Solution

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Updated September 25, 2024

Published July 11, 2024

Written by Mike Henry

Reading time 9 minutes

Predictive Analytics: Unveiling the Future with Data

Predictive analytics analyzes data to predict the likelihood of certain events happening in the future. Through predictive analytics software, businesses across all industries can understand their customers better and make more informed business decisions.

Organizations should take a closer look at predictive analytics to discover the myriad of ways that data and artificial intelligence (AI) can power more personalized customer experiences and enhance brand loyalty and customer retention. From a cost and ROI perspective, the impact and benefits of predictive analytics in customer experience management cannot be ignored.

It’s an opportunity that your company can capitalize on today. According to Forrester, fewer than 10% of enterprises are advanced in their insights-driven capabilities. By equipping your organization with predictive analytics tools, you can gain rich insights into customer behavior, make data-driven decisions, and optimize business operations.

What is Predictive Analytics?

Predictive analytics is a category of data analytics and the process of using data, statistical algorithms, AI, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Put simply: it involves analyzing current and historical data to make predictions about future events or trends.

Advancements in computing power, storage, and algorithms, along with the rise of AI, have made predictive analytics more feasible and accessible to businesses of all sizes. Machine learning algorithms can analyze large datasets quickly and efficiently, enabling businesses to derive insights in real time.

For example, predictive analytics can examine text reviews from customers and predict what steps they are likely to take. Predictive models trained on large datasets of similar text inputs can learn to recognize such patterns and predict future behavior, such as making a purchase or churning.

A conversation between a customer and a representative from the company. Predictive analytics predicts the customer wants to buy

Predictive Analytics vs Prescriptive Analytics

It can be easy to confuse predictive analytics and prescriptive analytics. While they sound similar, they also go hand in hand with each other in practice. These two types of analytics are both designed to provide a comprehensive approach to data-driven decision-making.

As mentioned earlier, predictive analytics is focused on forecasting future events, trends, or behaviors based on historical data. Conversely, prescriptive analytics goes a step further by not only predicting future outcomes but also recommending actions to achieve desired results.

Prescriptive analytics combines predictive models with optimization algorithms and business rules, employing techniques such as simulation, optimization models, and decision analysis. These methods evaluate various possible actions and their outcomes to suggest the best course of action.

Why is Predictive Analytics Important?

Predictive analytics is important because it empowers businesses to make informed decisions that enhance strategic planning and operational efficiency. By analyzing historical data to identify patterns and predict future outcomes, predictive analytics helps organizations anticipate trends, behaviors, and potential risks. This foresight enables businesses to proactively address issues before they become problems, optimize resource allocation, and improve overall performance.

For example, predictive analytics in healthcare enhances patient care by anticipating readmissions and improving diagnostic accuracy. This allows healthcare organizations to proactively manage patient outcomes, allocate resources more efficiently, and implement targeted interventions that reduce hospital stays and associated costs. By identifying at-risk patients early and providing personalized treatment plans, healthcare providers can improve overall patient health and satisfaction, ultimately leading to better clinical outcomes and a more sustainable healthcare system.

Benefits of Predictive Analytics in CX

Predictive analytics is also making an impact on the way companies manage the customer experience. By leveraging data-driven insights from predictive analytics, your company can foster meaningful connections with customers and achieve differentiation in today’s competitive marketplace. The wide-ranging benefits of predictive analytics applications in customer experience management include:

Enhanced customer loyalty and satisfaction. By predicting what customers want before they even ask for it, your company can provide a proactive and personalized experience that increases satisfaction and fosters loyalty.
Improve customer lifetime value. Predictive analytics helps identify the most valuable customers and understand their behavior, allowing you to implement strategies that maximize the value these customers bring over their lifetime.
Reduce customer churn. By identifying patterns that indicate a customer is at risk of leaving, you can take proactive measures to retain them, thereby reducing customer churn.
Enhance cross-selling and up-selling opportunities. With predictive analytics, marketers can identify which customers are most likely to be interested in additional products or services, creating more opportunities for successful cross-selling and up-selling.
Accelerate operational improvement. By enhancing the customer experience and making operations more efficient, predictive analytics contributes to accelerated business growth and increased profitability.

What is a Downside of Predictive Analytics?

While predictive analytics can be a powerful tool, organizations need to be aware of the potential downsides and take the proper steps to mitigate or eliminate them. Some of the possible downsides of predictive analytics include:

Incorrect predictions: Predictive analytics relies heavily on the quality and completeness of the data. Inaccurate, outdated, or incomplete data can lead to wrong predictions, which may result in misguided decisions.
Ethical and privacy concerns: Using personal data for predictive analytics raises significant ethical and privacy issues. Misuse or mishandling of sensitive information can lead to privacy violations and loss of customer trust.
False positives and negatives: Predictive models are not perfect and can produce false positives (incorrectly predicting an event will happen) and false negatives (failing to predict an event that does happen). These inaccuracies can lead to inappropriate actions, such as unnecessary interventions or missed opportunities.

These downsides can often be handled and resolved through proper planning, implementation, and maintenance of predictive models. While organizations should be aware of these happenings, they should not deter them from utilizing predictive analytics in their operations.

Examples of Predictive Analytics

Several predictive analytics examples show how the process is being applied by companies looking to better understand their customers, anticipate their needs, and deliver personalized and proactive experiences that drive satisfaction, loyalty, and ultimately, business success.

Predict Behavior and CLV

More and more retail brands are deploying predictive analytics software to forecast customer behavior and monitor market trends.

Retailers can personalize the retail customer experience and increase sales by analyzing information such as past purchase history, browsing behavior, and demographic data. Brands can also leverage predictive analytics algorithms to analyze historical data and market trends, helping predict the optimal price points for products in order to maximize revenue while remaining competitive.

By recommending relevant products, delivering personalized content, and identifying cross-selling and up-selling opportunities based on individual customer profiles and purchase history, brands can create highly personalized retail experiences that drive customer lifetime value (CLV).

A review of a product where the words "renewal" and "impressed" are highlighted.

The key is to connect customer experience data from every touchpoint and channel for a complete view of the customer journey. Jim Katzman, Principal of CX Strategy & Enablement for InMoment, suggests that companies should “expand the data sources that you use to understand what your customers are saying and how they perceive you. While surveys will continue to be important, they only give you part of the picture. Expanding your data repertoire to such sources as purchasing data, location-tracking data, web searches, social media, and online reviews is a must.”

The next step is to take a long view when looking at customer relationships. Adds Katzman, “You’ll be surprised at how many brands get caught up in the lure of ‘What can I sell you today?’ without considering what seeds to plant for even more success tomorrow.”

“Equally important is to understand how your competitors view this dynamic and what, if anything, they’re also doing to be proactive when it comes to building lifetime value.

Score Leads by Analyzing Customer Data

Another great application example of predictive is lead-scoring marketers leveraging historical data and machine learning algorithms to predict the likelihood of leads converting into customers. Today more than ever, marketers are empowered to make data-driven decisions when scoring and prioritizing leads, resulting in more effective lead management, higher conversion rates, and improved overall sales and marketing performance.

Identify Ideal Customer Profiles (ICPs). Predictive customer analytics tools can analyze historical data to identify patterns and characteristics common among high-value customers. By identifying these attributes, marketers can create an ideal customer profile (ICP) that serves as a benchmark for scoring leads based on their similarity to the ICP.
Assign predictive lead scores. Marketers are also utilizing statistical algorithms to analyze various data points such as demographics, firmographics, online behaviors, engagement with marketing content, and past purchase history to assign a predictive score to each lead. This score indicates the likelihood of a lead becoming a customer based on similarities to past successful conversions.
Prioritize sales efforts. Marketers can use predictive analytics to prioritize leads based on their likelihood to convert. Leads with higher predictive scores can be routed to sales teams for immediate follow-up, while leads with lower scores can be nurtured through targeted marketing campaigns until they demonstrate stronger buying signals.
Reduce sales cycle length. Predictive lead scoring enables marketers to identify leads that are further along in the buying process and more likely to make a purchase. By prioritizing these leads for immediate engagement, marketers can accelerate the sales cycle and shorten the time to conversion, leading to faster revenue generation and increased productivity for sales teams.

Harness NLP and Sentiment Analysis to Monitor Brand Reputation

Predictive analytics can also have a significant impact on brand reputation management efforts, helping companies anticipate, monitor, and respond to potential reputation threats more effectively.

Algorithms, for example, can analyze large volumes of data from various sources such as social media and online reviews to gauge customer sentiment toward the brand. By identifying patterns and trends in sentiment data, teams can proactively address emerging issues or negative perceptions before they escalate into major reputation crises.

These analytical techniques help crystallize information contained in reviews into insights — helping companies achieve a more accurate, complete, and unified view of the customer.

With online reputation management software, companies can also analyze customer feedback and sentiment data to identify areas for improvement and proactively address customer concerns. By identifying recurring themes or issues in customer feedback, brands can take corrective actions to improve products, services, and overall customer experience, which in turn enhances brand reputation.

InMoment’s approach is based on machine learning, a method of data analysis that allows companies to find patterns and unlock insights as it is exposed to new review and feedback data. This approach is fast, consistent, and programmable, helping teams quickly understand — at a glance and at scale — exactly what customers are saying. Proprietary relevancy scores for sentiment analysis also provide measurement of positive and negative language, with unparalleled accuracy.

Use AI to Improve Personalization

Predictive analytics empowers companies to better understand their customers, anticipate their needs, and deliver personalized experiences. It’s a particularly powerful tool for curating content based on historical customer data.

One of the best predictive analytics examples comes from streaming giant Netflix, which has a powerful personalized content recommendation engine. The company analyzes user data, including viewing history, ratings, and browsing behavior, to make predictions about what users might want to watch next. This is all reflected as soon as viewers land on Netflix’s home page, which displays content tailored to individual users, improving user engagement and satisfaction.

With predictive analytics, teams can dynamically customize website content, email marketing campaigns, and other communication channels based on individual customer preferences and behaviors. By delivering content that is relevant and timely, businesses can improve personalization, create more engaging customer experiences, and drive higher conversion rates.

Extract Insights from Reviews and Social Media Data

Online reviews and social media data provide a wealth of insights for a business but can be labor-intensive to read through and digest. There are many ways to try to automate this task. Currently, the leading approaches use deep learning models that extract many different kinds of keywords, predict their sentiment, and classify them into relevant categories. This allows companies to improve operations, make better decisions, and elevate the customer experience with data.

Using AI and advanced machine learning techniques, predictive analytics tools can read through thousands of reviews, comments, and other forms of customer feedback in the time it would take a human to read through just a few. The right technology will provide valuable insights, summaries, trends, and statistics that can be applied to support data-driven decision-making and customer-centric innovations.

Rural King, a family-owned farm supply store with 128 stores across 13 states, is no stranger to leveraging predictive analytics in order to create memorable customer experiences. The company regularly analyzes massive amounts of unsolicited feedback to unlock the potential of all its stores’ review data.

“We are hearing directly from customers about the store experience as well as pricing and product challenges,” says Kirk Waidelich, VP of Marketing for Rural King. “This allows us to narrow in on the stores that are experiencing issues — and to target and understand these issues versus simply guessing.”

What to Look for in Predictive Analytics Software

Predictive analytics software allows users to complete predictive analysis. This software can be used by different professionals across many different industries. Predictive analytics software will come with different features, and which specific features will work best for you depends on the goal of your business. However, there are a few foundational features that any successful software will have.

Data Collection and Integration

Data collection and integration is a crucial aspect of predictive analytics software. The feature facilitates the collection of data from various sources, ensuring comprehensive coverage for analysis. It allows users to connect to databases, extract data from APIs, import data from spreadsheets, and integrate data from different systems within the organization.

Data Preprocessing and Cleaning

Another fundamental feature of predictive analytics software is the ability to preprocess and clean data. This allows users to address common data quality issues such as missing values, outliers, duplicate records, and inconsistencies. This feature can also provide automated mechanisms to detect and handle missing values, either by imputing them using statistical techniques or by removing them based on predefined rules. This ensures that the data used for predictive modeling is complete and accurate.

In addition, these features should support outlier detection and treatment. Outliers are data points that deviate significantly from expected patterns. Outlier detection features can identify these outliers and remove them, transform them, or treat them as separate categories based on previously implemented rules or requirements.

Machine Learning Algorithms

Effective predictive analytics software incorporates a wide range of machine learning algorithms, which provides users with powerful tools to build accurate and reliable predictive models. These algorithms form the backbone of a software’s capabilities and enable users to leverage the predictive power of their data.

Model Training and Evaluation

Predictive analytics software should also provide robust functionalities for model training and evaluation, enabling users to build accurate predictive models and assess their performance effectively.

To ensure optimal model performance, software should have options to fine-tune the model’s parameters and settings. Users can experiment with different configurations and optimize the model to achieve the best possible results. This customization capability allows users to adapt the model to their specific use case, maximizing its predictive accuracy and relevance.

Once the model is trained, the software facilitates a thorough evaluation of its performance. Users can assess how well the model generalizes to unseen data by employing various evaluation techniques, such as cross-validation. Cross-validation involves splitting the data into multiple subsets, training the model on a portion of the data, and evaluating its performance on the remaining subset. This process helps estimate the model’s predictive accuracy and identify any potential overfitting or underfitting issues.

Visualization and Reporting Capabilities

Lastly, predictive analytics software should offer robust visualization and reporting capabilities to help users understand and communicate insights effectively, which helps transform complex data into intuitive visual representations and actionable reports.

Users should be able to easily create visual representations of their data, allowing for quick and comprehensive analysis. Visualization options often include bar charts, line charts, scatter plots, heat maps, and geographic maps, among others. These visualizations enable users to identify patterns, trends, and relationships within the data, facilitating deeper insights and understanding.

Furthermore, predictive analytics software should support interactivity in visualizations, allowing users to explore data from different perspectives and drill down into specific subsets of information. Users can interact with the visualizations, apply filters, and dynamically adjust parameters to gain more detailed insights and make data-driven decisions.

Predictive Analytics Implementation and Best Practices

Implementing predictive analytics involves a structured approach to ensure that the data-driven insights generated are accurate, actionable, and aligned with business goals. Here are some key steps and best practices for successful predictive analytics implementation:

1. Define Clear Objectives

Before embarking on a predictive analytics project, it’s essential to clearly define the objectives. Determine what specific outcomes you want to achieve and how predictive analytics will help you reach these goals. Whether it’s improving customer retention, optimizing inventory management, or reducing operational costs, having a clear objective will guide the entire process.

2. Assemble the Right Team

Successful implementation requires a team with diverse skills, including data scientists, data engineers, domain experts, and IT professionals. Data scientists and engineers are crucial for building and maintaining the predictive models, while domain experts ensure that the insights generated are relevant and actionable. IT professionals play a key role in integrating predictive analytics tools with existing systems.

3. Foster a Data-Driven Culture

For predictive analytics to be truly effective, it must be embraced across the organization. Encourage a data-driven culture by promoting the use of data in decision-making processes. Provide training and resources to employees to help them understand and leverage predictive analytics insights.

Jumpstart Your Predictive Analytics Solution With InMoment

The world’s top brands partner with InMoment AI, the leading predictive customer analytics solution, to facilitate the discovery of real-time insights, drive individual customer recovery, and turn unstructured feedback into a predictable source of business growth. To see how what predictive analytics can do for your business, schedule a demo today!

References

Forrester. “Data Governance Unlocks The Impact Of Analytics: Data Strategy & Insights 2023” (https://www.forrester.com/blogs/data-governance-unlocks-the-impact-of-analytics-data-strategy-insights-2023/). Access 03/16/2024.

In this Post

What is Predictive Analytics?Predictive Analytics vs Prescriptive Analytics Why is Predictive Analytics Important?Benefits of Predictive Analytics in CX What is a Downside of Predictive Analytics?Examples of Predictive Analytics What to Look for in Predictive Analytics Software Predictive Analytics Implementation and Best Practices Jumpstart Your Predictive Analytics Solution With InMoment

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Updated August 9, 2024

Published July 1, 2024

Written by Mike Henry

Expert reviewed by Sandeep Garg

Reading time 14 minutes

The Power of Brand Equity: Why It Matters for Your Business

Brand equity is the unique value people give to a business because they recognize and trust it. With positive brand equity, a company can charge higher prices since consumers perceive its products as of higher quality than those of its competitors. Thus, building brand equity is a crucial strategy for any company.

What is Brand Equity?

Brand equity is the measure of the perceived worth of a brand’s product, especially when compared to a generic equivalent product. Brand equity is a measurement of how much customers trust your brand’s product over another similar product, which can indicate how much more likely a customer is to pick your product over others.

For example, customers often turn to companies they trust when looking for a solution. Sometimes, they choose that company even if the product is slightly more expensive because they recognize and trust the name. For example, a customer looking for a quick OTC pain reliever may turn to Tylenol over a drugstore generic alternative because they know the brand and trust it. That is the essence of brand equity.

Why is Brand Equity Important?

Theoretically, the better your brand equity, the better your company will perform in sales and public perception compared to other brands. If your brand consistently impresses customers and reaches their expectations, you will develop more positive brand equity. If your brand fails to satisfy your customers because of negative experiences or perceptions, your brand will experience more negative brand equity.

Positive Brand Equity vs. Negative Brand Equity

When discussing brand equity, the terms positive and negative brand equity will arise. Understanding the difference between positive and negative brand equity is crucial for businesses aiming to build a strong, reputable brand.

Positive brand equity leads to high levels of customer loyalty and advocacy. Customers who perceive a brand positively are more likely to make repeat purchases and recommend the brand to others. This loyalty translates into stable revenue and organic growth through word-of-mouth marketing.

Conversely, negative brand equity results in customer distrust and attrition. Customers who have negative perceptions of a brand are likely to switch to competitors, leading to a decline in market share. Negative experiences, such as poor product quality or inadequate customer service, can severely damage a brand’s reputation. For example, scandals involving product safety can lead to widespread distrust and loss of customers.

Benefits of Brand Equity

Brand equity can have a meaningful impact on your company. While it’s obvious that brand equity improves public perception and recognition, there are several other benefits of nurturing brand equity that can help your company.

Increased Customer Loyalty

One of the primary benefits of strong brand equity is increased customer loyalty. Customers who trust and have positive associations with a brand are more likely to become repeat buyers. This loyalty not only ensures steady revenue but also reduces marketing costs, as retaining existing customers is often more cost-effective than acquiring new ones.

Enhanced Marketing Effectiveness

Marketing efforts are more effective when a brand has strong equity. Consumers are more receptive to marketing messages from brands they know and trust, leading to higher engagement and conversion rates. Additionally, positive customer feedback such as Google reviews from loyal customers can amplify marketing campaigns, extending their reach and impact without additional cost.

Competitive Advantage

Strong brand equity provides a significant competitive advantage. It differentiates a company’s products and services from those of competitors, making it harder for new entrants to capture market share. Established brands with strong equity enjoy top-of-mind awareness among consumers, making them the preferred choice in their respective categories. This competitive edge is crucial for long-term success and market leadership.

Increased ROI

When customers trust your brand, they are more likely to make future purchases from your company. If customers continue to purchase from your company, you are going to see a return on investment for what you put into improving your brand equity. Brand equity isn’t something that will leave your company without visible results. The ROI for your efforts can be seen in product lines. If you want to see how other efforts, such as a strong reputation management strategy, can provide you with ROI, check out our ROI calculator!

Overall, brand equity is an important measurement of the perceived worth of your company’s products and services over generic alternatives. Brand equity can be cultivated through many aspects of your business, including awareness and building relationships. When your company is nurturing brand equity, your brand can extend product lines and see ROI on investments made in brand equity.

What Makes Up Brand Equity?

Good brand equity comprises several elements. Nurturing each of these elements will help create full and balanced brand equity that can reach customers and improve public perception of your brand.

Brand Perception

Brand perception is how customers view and regard a product or service. This is separate from what a company is saying about its product. Essentially, brand perception is what a customer believes your product or service does—not what a marketing department publishes about the product. While it’s completely possible that brand perception of a product lines up with how a company discusses its product, it’s not a given.

There are two sub-stages of brand perception development: Brand recognition and brand awareness. Brand recognition is when products are identifiable as belonging to a particular brand. If a customer sees a product from your company, they would easily be able to identify that it belongs to your brand. Logos and jingles can all be a part of brand recognition. Brand recognition can help your brand become a household name and improve your brand equity.

Brand awareness is knowing what a brand stands for. While brand recognition means customers recognize your brand, brand awareness shows they understand your brand. Brand awareness is about knowledge, values, and beliefs. A way to think about brand awareness is to think about a customer choosing which brand of laptop to buy. If a customer has a lot of knowledge about how a company crafts their laptops, they have brand awareness for that company. That brand awareness may sway their choice of which laptop to buy, potentially over something like price. While one laptop may be cheaper, the customer may be more likely to buy the laptop they feel they understand better and can trust.

Customer Experience

Customers who have a positive experience with your company are more likely to trust your brand, which can increase your brand equity. Any time a customer comes into contact with your brand is an opportunity to improve their experience and ultimately their perception of your brand. That quality experience with your brand can create a positive impression of your company—and hopefully improve brand equity. The reverse can also be true. Bad experiences with a company can create a negative association with the brand. Doing what you can to improve your customer experience can go a long way with brand equity.

Quality

Your brand is associated with more than just a product or service. Brands are also associated with the supply chain, brand reputation, and trust. Quality across all of these parts of your brand can affect your brand equity. For example, a company that is effective at shipping quickly, restocking, and supplying vendors could increase the quality of its brand equity. In addition, a company with strong leadership, good financial performance, and excellent innovation will also have brand quality—ultimately creating more brand equity.

Customer Preference

Customers have preferences for brands they buy from, and that can come into play with your brand equity. For example, customers who grew up on a certain brand of cereal are more likely to choose that brand of cereal even as an adult. They simply have a preference for it—as well as more trust and experience with the brand. The same can be said for any brand in any industry. Working with customer preferences as part of your brand equity can help draw and keep customers in the long run.

How to Build Brand Equity

Actively investing in each component of brand equity can improve your brand equity. Refining customer experience, improving quality, and working with customer preferences can help build good brand equity. In addition, building brand awareness, emphasizing positive associations, and forming good relationships with customers are also important to building good brand equity.

1. Build Brand Awareness

It’s hard to have positive brand equity when potential customers aren’t sure what your brand is or what it stands for. When customers understand your brand and your products, they are more likely to consider buying them—even when there’s a price difference. You can build brand awareness with strong advertising and marketing, as well as make your brand’s values very clear and visible.

2. Emphasize Positive Associations

Making sure your brand is associated with positive things is an important part of improving your brand equity. To do this, ensure that your business is using responsible and ethical business practices. Those go a long way in giving your company a positive association with customers. In addition, emphasize any time your brand comes into contact with something positive or makes a positive connection or collaboration with an influential organization or person.

3. Form Good Relationships

In the end, good relationships with customers are what will truly strengthen your brand equity. Stay in touch with your customers on social media and through any other viable channels. In addition, provide them with excellent customer service through every step of the customer journey. Keep track of negative feedback and use it to smooth out problems in your customers’ experiences with your brand. Ultimately, be authentic with your customers and foster those relationships.

How to Measure Brand Equity

It can be difficult to definitively measure your brand equity, but there are a few ways to gain further insight into how your brand is doing. These are some of the quantitative methods that reflect your brand equity:

Profit margins
Price sensitivity
Profitability
Growth rate
Market share percentage
Purchasing frequency

Interviews, social media presence, and customer satisfaction surveys are another way to gauge how your brand equity is performing.

Brand equity can help increase your profit margins and how customers view your brand, so it’s an important aspect of your business to nurture.

You may also be able to measure your brand equity by tracking your business’ core customer experience metrics, as these indicators reflect how customers perceive and interact with your brand.

A collage of different reports such as CSAT, metrics, and areas to improve

For example, an increase in customer satisfaction over a measured time may reflect that your efforts to improve product quality, customer service, or brand communication are resonating well with your audience, thereby enhancing your brand’s perceived value and trustworthiness.

Brand Equity Examples

Understanding brand equity can be significantly enhanced by looking at real-world examples of companies that have successfully built and leveraged their brand equity. These examples illustrate how strong brand equity can lead to customer loyalty, market dominance, and the ability to extend product lines.

Foot Locker

Foot Locker has built its brand equity on a foundation of trust within the athletic and sneaker communities. Known for its wide selection of athletic footwear and apparel, Foot Locker has leveraged endorsements from popular athletes and collaborations with top brands to strengthen its market position. By actively engaging with its target audience through social media, events, and sponsorships, Foot Locker has developed a loyal customer base that views the brand as an integral part of sports and street culture.

Rural King

Rural King’s brand equity is rooted in its reputation for being a reliable and community-focused retailer. Known as the “RK Family of Stores,” Rural King has earned the trust of customers in rural and farming communities by providing a wide range of essential products, from agricultural supplies to home goods. The company’s focus on customer service, community events, and a friendly shopping experience has cultivated a loyal customer base that views Rural King as a dependable and integral part of their local communities.

La-Z-Boy

La-Z-Boy has established strong brand equity through its reputation for comfort, quality, and durability in furniture. Known for its iconic recliners, La-Z-Boy has expanded its product line to include a variety of home furnishings. The brand’s commitment to providing high-quality, comfortable furniture has earned it a loyal customer base. La-Z-Boy’s emphasis on innovation in comfort technology and stylish design has further reinforced its brand equity, allowing it to maintain a premium positioning in the market.

Improve Your Brand Equity with InMoment

InMoment’s customer experience platform gives you the power to monitor feedback from every touchpoint, across every stage of the customer journey. Using the XI platform, your business will be able to track the progress of your business’ main metrics, which correlates to the growth of your brand equity. See what we can do for you by scheduling a demo today!

In this Post

What is Brand Equity?Why is Brand Equity Important?Positive Brand Equity vs. Negative Brand Equity Benefits of Brand Equity What Makes Up Brand Equity?How to Build Brand Equity How to Measure Brand Equity Brand Equity Examples Improve Your Brand Equity with InMoment

Watch The Video – Make Online Reputation Your Acquisition Strategy

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Updated August 9, 2024

Published June 27, 2024

Written by Mike Henry

Reading time 10 minutes

How to Read the Forrester Wave for Text Analytics: A Guide for Buyers

With the increased adoption of AI in business across all industries, there has also been a rise in text mining and analytics. This software, which exists as an extension of AI and natural language processing (NLP), is used to gather insights from unstructured text data in order to make informed business decisions.

If your business has reached the need to purchase text analysis software, you are more than likely comparing third-party evaluations as part of your research process. Understanding these third-party evaluations is crucial to choosing the right software for your business. Among these evaluation tools are evaluative Analyst reports such as The Forrester Wave, Gartner Magic Quadrant, or IDC MarketScape.

The Forrester Wave™ is a valuable resource that evaluates and ranks vendors in a particular market, but understanding how to read and interpret the Wave report can be daunting. By reading this guide, you will understand how to navigate reports like the Forrester Wave and make informed decisions from the reports’ implications.

What is the Difference Between Gartner and Forrester?

The Forrester Wave™ and the Gartner Magic Quadrant™ are widely recognized and influential market research reports evaluating technology vendors. While both serve to help buyers make informed decisions, they differ in methodology, structure, and focus.

In the Forrester Wave, vendors are ranked based on criteria such as their strategy and current offering, which represent the x and y axes. They are also ranked on market presence, which is represented by the size of the dot on the graphic. For each of these three categories, there are subcategories that vendors are scored on. These scores are taken into account and then vendors are positioned in segments such as Leaders, Strong Performers, Contenders, and Challengers on the Wave graphic.

Conversely, the Gartner Magic Quadrant™ offers a high-level overview, evaluating vendors based on their Completeness of Vision and Ability to Execute. Vendors are placed in one of four quadrants: Leaders, Challengers, Visionaries, and Niche Players. The Magic Quadrant is useful for quickly comparing vendors and understanding the overall market landscape and strategic positioning.

Ultimately, the Forrester Wave™ is best for buyers seeking a detailed, customizable evaluation, while the Gartner Magic Quadrant™ is suited for those needing a quick, strategic overview of vendor capabilities and market trends. Understanding these differences helps buyers select the right tool for their specific needs.

Understanding the Forrester Wave™ Methodology

The Forrester Wave™ is a comprehensive evaluation of technology providers in a specific market. For text analytics, it assesses vendors based on a detailed set of criteria to provide a comparative analysis. Here’s how it works:

Vendor Selection: Forrester selects the most significant vendors from the preceding Landscape report which acts as a precursor to the evaluative Wave and outlines market dynamics, top business use cases, and provides a list of ‘players’
Criteria and Weightings: Forrester defines a set of criteria that comprise the two categories of strategy and current product offering. Weightings of these criteria (how much each one is worth) are not shared with vendors until post-publication… Each criterion is assigned a weight based on its importance to the overall evaluation.
Data Collection:There are three inputs into a Forrester Wave evaluation: a questionnaire, a strategy and product demo session, and customer references.
Scoring: Each vendor is scored on a scale (0 to 5) for each criterion. These scores are then weighted and combined to produce an overall score for each category.
Wave Graphic: The scores are plotted on a wave graphic, with vendors positioned in different segments: Leaders, Strong Performers, Contenders, and Challengers. The size of the dots are representative of the vendors’ market presence, which is determined by revenue.

Decoding Forrester Wave™ Classifications

The Forrester Wave graphic visually represents the relative strengths and weaknesses of each vendor. Here’s what the graphic for each Wave looks like as well as what each classification means:

An example of the Forrester Wave graphic showing how vendors are represented.

Leaders: These vendors have the highest scores in the evaluation criteria. They exhibit strong current offerings, robust strategies, and a significant market presence. Leaders are generally the safest choice for most buyers.
Strong Performers: Vendors in this segment have solid offerings and strategies but may lack in some areas compared to leaders. They are still viable options, especially if they meet specific needs or have unique strengths.
Contenders: These vendors may have competitive offerings but are often limited by weaker strategies or lower market presence. They can be suitable for buyers with specific requirements that align with the vendor’s strengths.
Challengers: Vendors in this category typically have lower scores across multiple criteria. They may be newer to the market or lack certain features. They are riskier choices but might offer innovative solutions or cost advantages.

Key Components of the Forrester Wave for Text Analytics

The full Forrester Wave report will consist of three main sections: current offering, strategy, and market presence. Each category will cover different aspects of an organization’s presence in the marketplace.

Current Offering: This category evaluates the product’s features and capabilities. Key criteria might include:

AI: ML-based, knowledge-based, or symbolic
Generative AI: Pre and post-processing
Deployment options
Omnichannel data integration
Security and regulatory compliance

Strategy: This category assesses the vendor’s vision and roadmap. Key criteria might include:

Innovation: The vendor’s commitment to innovation and staying ahead of market trends.
Product Roadmap: The planned future developments and improvements.
Pricing flexibility and transparency

What This Means for Buyers

As a buyer, the Forrester Wave for Text Analytics provides a comprehensive and unbiased assessment of the market. Here’s how you can use it:

Identify Your Needs: Determine what’s most important for your organization. Are you looking for a platform with advanced NLP capabilities? Or is integration with existing systems more critical?
Compare Vendors: Use the Wave graphic to compare vendors at a glance. Focus on the Leaders for well-rounded options, but don’t overlook Strong Performers if they align better with your specific needs.
Dive Deeper: Read the detailed vendor profiles and scores for a deeper understanding of each vendor’s strengths and weaknesses. Pay attention to how vendors perform in areas that matter most to your organization.
Evaluate Market Trends: Consider the market trends and how vendors plan to adapt to them. For example, vendors will no longer differentiate themselves on text mining functionality alone, it is the pre and post-processing processes that will set them apart.
Consider Future Needs: Look at the strategy scores and product roadmaps to ensure the platform you choose will continue to meet your needs as your organization grows and evolves.

InMoment’s Placement in the Forrester Wave

InMoment was recently recognized as a Leader in the Forrester Text Mining & Analytics Wave ‘24. This achievement highlights the capabilities of the XI Platform such as knowledge-based AI, document-level text mining, natural language understanding, and more!

To learn more about InMoment’s platform, schedule a demo today!

In this Post

What is the Difference Between Gartner and Forrester?Understanding the Forrester Wave™ Methodology Decoding Forrester Wave™ Classifications Key Components of the Forrester Wave for Text Analytics What This Means for Buyers InMoment’s Placement in the Forrester Wave

Watch the Video – See InMoment in Action

Check out a quick demo video to learn more about how our solutions can help your team grow your business.

Updated September 18, 2024

Published June 24, 2024

Written by Mike Henry

Reading time 5 minutes

CSAT: What You Need to Know About Customer Satisfaction Score

The ability to track customer happiness with one number can almost sound too good to be true. However, the CSAT score is used by businesses across the world to track their customers’ happiness and make strategic decisions.

What Does CSAT Stand For?

Customer Satisfaction score, commonly referred to as CSAT, is an attempt at capturing how satisfied customers are with a company’s goods and services. A survey asks a customer to rate their satisfaction, typically on a scale from 1 to 5.

Why CSAT Is Important

What’s most important about customer experience metrics such as NPS, CES, and CSAT is their predictive power. While financial metrics are past indicators of a company’s performance, customer satisfaction metrics are leading indicators. After all, if your customers are not happy with what you have to offer today, one can predict it will reflect in the financial statements later in the form of churn, decreasing share of wallet, etc.

Knowing and monitoring your company’s CSAT score is one reason to implement a CSAT program. The score can be segmented, benchmarked, and tracked over time. More importantly, setting up a CSAT program is an opportunity for a systematic solicitation and collection of actual customer feedback that can be used across the organization for continuous improvement. Product teams can use it to plan their product roadmap. Account executives who become aware of problems with one client can remedy, or conversely, fully realize the value another customer is getting from the product, price accordingly, or cross-sell; engineering teams can design better products, and fix problems if they have access to that feedback, and service teams can improve on the service they provide.

In brief, CSAT is an easy to understand metric, versatile enough that it can be used in many scenarios and touchpoints–and low CSAT can predict churn.

How to Measure Customer Satisfaction?

Measuring Customer Satisfaction is done through surveys. It sounds simple and straightforward enough, however getting to an accurate number involves some statistical science.The main question asked in the customer satisfaction survey is how satisfied are they with the business or product, along with a rating scale.

An example of a CSAT survey. The heading reads "How satisfied are you with SaaSy.co?" And there is a scale from 1-5 below it.

The most common customer survey questions all have different rating scales which can become easily confusing: the CSAT is a simple 1 to 5, the Customer Effort Score (CES) question is from 1 to 7, and the NPS question is from 1 to 10. In the end, the scale does not quite matter as much and some organizations create their own custom metrics. However, having a standard helps professionals have a consistent frame of reference when measuring customer satisfaction against CSAT, NPS, and CES.

How to Calculate CSAT Score?

Calculating the CSAT score involves interpreting the responses gathered from a standardized CSAT survey. Below are the steps to determine the CSAT score:

1. Choose a Scale:

CSAT surveys typically use a scale, commonly ranging from 1 to 5 or 1 to 10, with higher values indicating greater satisfaction. Ensure that the scale used in your survey is clearly communicated to respondents.

2. Collect Survey Responses:

Gather responses from your CSAT survey. Each respondent will provide a numerical rating based on their satisfaction level.

3. Count Positive Responses:

Identify the number of positive responses, typically represented by the highest rating on your scale. For example, if you are using a 1 to 5 scale, responses of 4 and 5 are considered positive.

4. Total Number of Responses:

Determine the total number of survey responses received. This is the sum of positive and non-positive (neutral or negative) responses.

5. Calculate CSAT Score:

Use the formula below to calculate the CSAT score as a percentage:

CSAT Score = (Number of Positive Responses/Total Number of Responses)×100

For example, if you had 20 responses and out of the 20 there were only 8 that rated your business or product a 4 or a 5. Then you would take 8 divided by 20 and multiply that by 100, your CSAT score would be 40%.

What is a Good CSAT Score?

Customer Satisfaction (CSAT) scores play a pivotal role in understanding how well a business meets its customers’ expectations. Determining what constitutes a “good” CSAT score is not a one-size-fits-all endeavor; it involves considering various factors specific to the industry, company goals, and customer base.

1. Benchmarking for Context:

CSAT scores gain significance when viewed in the context of industry benchmarks. Industry standards and best practices can serve as a yardstick for what is considered a good CSAT score. These benchmarks provide a reference point for businesses to gauge their performance relative to competitors or similar service providers. A good CSAT score should not only meet industry standards but ideally surpass them.

For example, the American Customer Satisfaction Index reported that full-service restaurants had an average score of 81 in 2023. Finding the average score in your industry will be an important metric on which to compare your organization’s customer satisfaction performance. As a general rule, here are the overall statistics regarding CSAT scores:

A CSAT score of 80% and above is generally considered excellent.
Scores between 70% and 79% are considered good.
Scores between 50% and 69% may indicate room for improvement.
Scores below 50% may signal significant issues requiring urgent attention.

2. Interpreting the Scale:

The scale used to measure CSAT scores varies across businesses, commonly ranging from 1 to 5, 1 to 10, or 1 to 100. Understanding the nuances of the scale is crucial; what might be considered a good score on a 1 to 5 scale may differ from what’s considered good on a 1 to 10 scale.

3. Aligning with Customer Expectations:

The essence of customer satisfaction lies in meeting or exceeding customer expectations. A good CSAT score reflects a positive alignment with the expectations of your specific customer base. Knowing your customers and their needs is necessary for interpreting the significance of your CSAT scores.

4. Consistency Over Time:

It is essential to monitor the trend of CSAT scores over time. A consistent upward trend suggests improving customer satisfaction, while a decline may signal the need for closer attention to customer concerns.

5. Qualitative Feedback Analysis:

Numbers only tell part of the story. Beyond the numerical score, businesses should delve into the qualitative feedback provided by customers. Understanding the reasons behind the scores provides valuable insights into areas that require improvement.

The Advantages and Disadvantages of CSAT

While the CSAT score is a valuable tool for measuring customer satisfaction and can provide actionable insights for improving customer experience, it should be used in conjunction with other feedback mechanisms to ensure a holistic understanding of customer sentiment.

Balancing the simplicity and immediacy of CSAT with deeper, more comprehensive feedback methods can help businesses achieve a more accurate and effective approach to customer satisfaction. Regardless, the CSAT score is very useful and comes with its own set of pros and cons.

Advantages of CSAT

Simplicity and Ease of Implementation: CSAT surveys are straightforward to create and administer. They typically consist of a few questions that can be quickly answered by customers, making it easy to gather feedback without overwhelming them.
Immediate Feedback: CSAT surveys are often conducted immediately after an interaction or transaction, providing timely insights into customer satisfaction. This allows businesses to quickly address any issues and improve their services.
Quantifiable Results: The results from CSAT surveys are easy to quantify and analyze. Businesses can track changes in satisfaction over time and identify trends, making it easier to measure the impact of any changes implemented.
Actionable Insights: Since CSAT surveys focus on specific interactions or transactions, the feedback is often highly relevant and actionable. Businesses can use this information to make targeted improvements in their customer service or product offerings.
Benchmarking: CSAT scores can be benchmarked against industry standards or competitors, providing a clear picture of where a business stands in terms of customer satisfaction. This can be a valuable tool for strategic planning and competitive analysis.

Disadvantages of CSAT

Limited Scope: CSAT surveys typically focus on recent interactions and may not capture the overall customer experience or long-term satisfaction. This can result in a narrow view of customer sentiment.
Subjectivity: Customer satisfaction is inherently subjective, and responses can be influenced by factors outside of the business’s control, such as the customer’s mood or external circumstances. This can sometimes lead to skewed results.
Lack of Depth: While CSAT surveys are great for capturing immediate feedback, they often lack the depth needed to fully understand the reasons behind customer dissatisfaction. Additional qualitative methods may be necessary to gain a complete understanding.
Survey Fatigue: Frequent CSAT surveys can lead to survey fatigue, where customers become less likely to respond or provide thoughtful feedback. This can reduce the effectiveness of the survey and the quality of the data collected.
Overemphasis on Scores: Relying too heavily on CSAT scores can lead businesses to focus on improving the score itself rather than addressing the underlying issues that impact customer satisfaction. It’s important to use CSAT as one of many tools in a comprehensive customer feedback strategy.

Why Sample Size Is Important When Measuring Customer Satisfaction

Another consideration in measuring customer satisfaction is how many surveys need to be sent out, and how many responses need to be received to obtain a reliable score? If you’ve ever taken a statistics class, you might remember that the survey sample size n to reach is:

n = (z*𝜎/𝜀)^2

Where 𝜀 is the margin of error, z is the z-score for a given confidence level and 𝜎 the standard deviation.

Interestingly, the number of survey responses you’d have to reach does not depend on how many customers you have in the first place. More customers do not mean you have to poll more of them. How many customers you need to poll has more to do with how much variance (standard deviation 𝜎 is the square root of the variance) there is in between their answers to the CSAT question. It somewhat intuitively makes sense. If the first responses that come in are all 4’s and 5’s, you’ll expect future responses to be in the same ballpark. If you sometimes get 1s, sometimes 4s, you’ll want to continue polling to narrow in on a score. In summary, when measuring customer satisfaction, variability requires larger samples.

The second factor that impacts sample size is the margin of error. For a CSAT scale of 1 to 5, let’s say you’d be comfortable with +/- 0.5 points from the mean, a 10% margin of error.

Taking a common 95% confidence interval, z = 1.96, and let’s say a previous survey showed a 1.4 point standard deviation, then the sample size would be n = (1.96*(1.4/0.5))^2 = 30 responses. Now, if a margin of error of +/- 0.25 points was required, it would drive up the sample size to 120.

The Type of Survey Matters When You Measure Customer Satisfaction

The old-fashioned way for a company to measure customer satisfaction would be to mass email, and/or direct mail a subset of customers twice a year. They’d use a formula like this one to calculate the sample size needed to reach statistical significance.

However, by the time the survey is conducted and results compiled, the company’s products, services, and processes will have evolved a bit, and the CSAT score would already be obsolete. And closing the loop on any negative feedback would be less than timely and not as effective in reducing customer churn.

Finally, email or direct mail could be two useful types of surveys if that is the best way to engage with your customers, but often requires higher samples given the low response rates of those channels.

Measuring Customer Satisfaction in a Timely Way

The demand for more accurate and timely customer insights has created a niche for software vendors to develop tools that can handle the surveying, the sampling, computation, and measuring of standard customer experience metrics such as CSAT, CES, and NPS. As more and more companies have moved their products and services online, some of these vendors offer the ability to survey customers directly during the customer’s web or mobile experience. Measuring customer satisfaction about their experience right as they’re engaging with the brand at key journey points generates more contextual feedback and a chance to remediate.

Measuring customer satisfaction twice a year is not enough to manage a business proactively. Tools are now available to get a real-time CSAT for anyone in the company to see and rally around.

Customer Satisfaction Use Cases

By far, the most common use case of CSAT is as a departmental metric in managing contact centers, with customer service and support teams. A CSAT survey is triggered after an interaction with a support or service representative or agent. Modern CSAT tools will have an integration with common helpdesk and CRM software so that the survey can get triggered upon a case closure or issue resolution. These departments might track other metrics: the volume of low CSAT ratings (3 or lower) (if the survey is triggered after each interaction), or the time it took to respond to the opening of an issue, and the percentage of issues successfully resolved.

Product teams also use CSAT to understand what makes the customer tick, or where the product might fall short. They might have heard anecdotes and qualitative feedback from front-line employees, but CSAT surveys are a great way for product teams to get access to first-hand feedback right at their fingertips.

How to Improve Customer Satisfaction

Start measuring. Invest in a survey tool that can quickly and simply get that CSAT metric right into your hands in real time, and provides the ability to track the metric over time.
Measure CSAT at various touchpoints along the buyer journey.
Tag issues as they come in. Some tools will have auto-tagging, and even natural language processing to interpret and classify the feedback.
Build a process to close the loop with customers in real time.
Take action. Route the issues, customer insights, and feedback themes to the appropriate teams for resolution.
Monitor CSAT trends, in aggregate, at various touchpoints, product lines, or any segments relevant to the business, and build a plan for making systemic change.

Focusing on efforts to improve your organization’s customer satisfaction can help you increase loyalty and even grow your bottom line. Check out the potential ROI your organization could see from investing in CX with our handy calculator!

Calculate your business’ ROI using InMoment’s VoC tools.

Industry

Number of Customers

Annual revenue Per Customer ($)

Lead to Conversion Rate (%)

Number of calls per year

Percent of calls where issue is not resolved (%)

Estimated Revenue Growth

$
Use the calculator to find an estimated ROI

Total ICX ROI

Improve Your Customer Satisfaction with InMoment

At InMoment, we specialize in survey software for customer experience. We have helped across multiple industries take the necessary steps to improve their customer satisfaction. Whether you’ve got 10 locations or 1000 locations, we can help you too. Schedule a demo today to see our platform in action!

In this Post

What Does CSAT Stand For?Why CSAT Is Important How to Measure Customer Satisfaction?How to Calculate CSAT Score?What is a Good CSAT Score? The Advantages and Disadvantages of CSAT Why Sample Size Is Important When Measuring Customer Satisfaction The Type of Survey Matters When You Measure Customer Satisfaction Measuring Customer Satisfaction in a Timely Way Customer Satisfaction Use Cases Improve Your Customer Satisfaction with InMoment

Updated September 20, 2024

Published June 17, 2024

Written by Mike Henry

Reading time 12 minutes

Text Mining: Everything You Need to Know

Text mining is the process of extracting useful information from large amounts of text using computational techniques. It involves analyzing and transforming unstructured text into structured data for insights.

In today’s data-driven world, businesses generate and accumulate vast amounts of text data from various sources, including customer feedback, social media, emails, and internal documents. However, extracting meaningful insights from this unstructured data can be challenging.

This is where text mining comes into play. By transforming unstructured text data into valuable information, text mining enables businesses to uncover hidden trends, sentiments, and relationships within the data. This process is crucial for making informed decisions, enhancing the customer experience, and maintaining a competitive edge.

What is Text Mining?

Text mining, also known as text data mining, is the process of analyzing unstructured text data to extract meaningful patterns and insights. This process involves using techniques from natural language processing (NLP), machine learning, and statistics to transform textual information into a structured format that can be easily analyzed. By doing so, organizations can uncover hidden trends, sentiments, and relationships within the data, which can inform strategic decisions and drive business growth.

Text Mining Examples and Use Cases

Consider a business interested in contact center optimization. They could implement text mining to enhance operations and improve customer satisfaction. The center can identify common customer issues and frequently asked questions by analyzing transcripts of customer service calls, emails, and chat interactions.

From those insights, the contact center can pinpoint areas where agents need additional training and identity processes that require streamlining. For instance, text mining might reveal that a significant number of calls were related to the same few technical issues. This discovery can lead to bug fixes as well as a more comprehensive knowledge base for agents, which can significantly reduce call resolution times.

Why is Text Mining Important?

In an era where data is considered the new oil, the ability to analyze and derive insights from unstructured text data is invaluable. Text mining is important for several reasons:

1. Extracting Valuable Insights: Text mining enables businesses to sift through large volumes of unstructured text data and extract valuable insights. Whether it’s customer feedback, social media comments, or internal documents, these insights can reveal trends, sentiments, and patterns that are crucial for strategic decision-making.

2. Enhancing Customer Experience: By analyzing customer feedback and sentiment, companies can better understand their customers’ needs, preferences, and pain points. This understanding allows businesses to tailor their products, services, and interactions to meet customer expectations, thereby enhancing overall customer satisfaction and loyalty.

3. Improving Operational Efficiency: Text mining can help identify inefficiencies and areas for improvement within an organization. For example, analyzing support tickets and emails can reveal common issues that need addressing, enabling companies to streamline their operations and improve service quality.

4. Supporting Data-Driven Decision Making: Text mining transforms unstructured data into structured data that can be easily analyzed and visualized. This transformation supports data-driven decision-making processes by providing actionable insights that are grounded in actual data rather than intuition or guesswork.

5. Gaining Competitive Advantage: By leveraging text mining, businesses can stay ahead of the competition by quickly identifying market trends, customer preferences, and emerging issues. This proactive approach allows companies to adapt and innovate faster than their competitors.

6. Enabling Predictive Analytics: Text mining can also be used in conjunction with predictive customer analytics to forecast future trends and behaviors. For instance, sentiment analysis of customer reviews can predict future product success, while topic modeling can identify emerging trends in customer interests.

Difference Between Text Mining and Text Analytics

While text mining and text analytics are often used interchangeably, they have distinct focuses and processes. Understanding the difference between the two can help businesses leverage the right techniques for their specific needs.

Text Mining

Text mining is the process of discovering patterns and extracting useful information from unstructured text data. It involves transforming text into a structured format, which can then be analyzed. The primary goal of text mining is to uncover hidden insights and trends that are not immediately obvious.

Key Components of Text Mining:

Data Collection: Gathering text data from various sources such as websites, social media, emails, and internal documents.
Preprocessing: Cleaning and preparing the text data by removing noise, normalizing text, and tokenizing.
Transformation: Converting text into a structured format using techniques like vectorization.
Analysis: Applying NLP, machine learning, and statistical methods to identify patterns and extract insights.

Text Analytics

Text analytics is the application of text mining techniques to solve specific business problems. It involves analyzing the structured data produced by text mining to gain actionable insights and inform decision-making. Text analytics often integrates text mining results with other types of data analysis to provide a comprehensive understanding of the data.

Key Components of Text Analytics:

Integration: Combining text data with other data sources to provide a holistic view.
Visualization: Presenting the findings in a comprehensible format using graphs, charts, and dashboards.
Reporting: Generating reports that highlight key insights and recommendations.
Actionable Insights: Using the analyzed data to inform business strategies and decisions.

Consider a company analyzing customer reviews to improve its products. Text mining would involve processing the reviews to identify common themes and sentiments. Text analytics would then take these findings and integrate them with sales data to understand the impact of customer feedback on product performance and make strategic recommendations.

How Text Mining Works

Text mining involves several steps that transform unstructured text data into structured data, which can then be analyzed to extract meaningful insights. Here is a detailed look at the key steps involved in the text mining process:

1. Data Collection: The first step in text mining is gathering text data from various sources. This can include customer feedback, social media posts, emails, online reviews, internal documents, and more. The data collection process may involve web scraping, database extraction, or API integration to aggregate the text data into a single repository.

2. Preprocessing: Once the data is collected, it needs to be cleaned and prepared for analysis. Preprocessing involves several sub-steps:

Tokenization: Splitting the text into individual words or tokens.
Stop Words Removal: Eliminating common words (e.g., “and”, “the”, “is”) that do not contribute to the analysis.
Stemming and Lemmatization: Reducing words to their root form (e.g., “running” to “run”).
Normalization: Converting text to a standard format, such as lowercase all words and removing punctuation and special characters.

3. Transformation: After preprocessing, the text needs to be transformed into a structured format. This often involves:

Vectorization: Converting text into numerical vectors that represent the frequency or presence of words or phrases. Common techniques include Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings like Word2Vec.
Feature Extraction: Identifying and extracting relevant features from the text that can be used in subsequent analysis.

4. Analysis: With the structured data in hand, various analytical techniques are applied to extract insights:

Natural Language Processing (NLP): Techniques such as named entity recognition (NER), part-of-speech tagging, and dependency parsing to understand the structure and meaning of the text.
Machine Learning: Applying algorithms to classify, cluster, and predict outcomes based on the text data. Common methods include sentiment analysis, topic modeling, and text classification.
Statistical Analysis: Using statistical methods to identify patterns, correlations, and trends within the text data.

5. Visualization: The final step is to present the findings in an easily understandable format. Visualization tools and techniques are used to create graphs, charts, word clouds, and dashboards that highlight key insights and trends. Effective visualization helps stakeholders quickly grasp the results and make informed decisions.

A compilation of images showing a word cloud and analysis produced from text mining

Text Mining Best Practices

Implementing text mining effectively requires adherence to several best practices to ensure accurate, actionable insights and optimal outcomes. By following these best practices, organizations will be set up for success in utilizing text mining effectively.

1. Define Clear Objectives

Set clear, specific goals for what you want to achieve with text mining. Whether it’s enhancing customer experience, identifying market trends, or detecting fraud, having well-defined objectives will guide your project and measure success.

2. Select the Right Tools

Choose tools and software that align with your project requirements and team expertise. It is important to find text mining software that has all the necessary features to complete the projects you are working on.

3. Data Quality and Diversity

Ensure that the text data you collect is relevant, high-quality, and diverse, drawing from sources such as customer feedback, social media, emails, and internal documents. Gathering data from multiple sources can decrease the chances of voluntary response bias, or other biases that can damage the integrity of your data. Comprehensive preprocessing is equally important; this includes cleaning the data to remove noise, normalizing text formats, and applying techniques like tokenization, word removal, and stemming/lemmatization to prepare the data for analysis.

4. Effective Data Preprocessing

Preprocess your text data meticulously. Clean the data by removing noise, standardizing text formats, and applying tokenization, stop-word removal, and stemming/lemmatization to prepare the text for analysis.

5. Ethical Considerations

Adhere to ethical standards and data privacy regulations. Anonymize sensitive information, obtain necessary consent, and address biases in your text data and models to ensure fairness and compliance.

Common Use Cases of Text Mining

Text mining has a wide range of applications across various industries. Here are some common use cases where text mining can provide significant value:

1. Customer Feedback Analysis

Businesses receive feedback from customers through various channels such as surveys, reviews, and social media. Text mining helps analyze this feedback to identify common themes, sentiments, and areas for improvement. For example, a company can use text mining to detect recurring complaints about a product feature and take corrective action.

2. Sentiment Analysis

Sentiment analysis involves determining the sentiment behind a piece of text, whether it’s positive, negative, or neutral. This is particularly useful for brands to monitor their reputation online. By analyzing customer reviews, social media posts, and other textual data, businesses can gauge public perception and respond accordingly.

3. Topic Modeling

Topic modeling is a technique used to discover the underlying topics within a large corpus of text. It helps in organizing and summarizing large collections of textual information. For example, a news organization can use topic modeling to automatically categorize articles into topics like politics, sports, and entertainment.

4. Fraud Detection

In sectors like finance and insurance, text mining is used to detect fraudulent activities. Text mining can identify suspicious patterns and flag potential fraud by analyzing claims, transaction records, and customer communications. This proactive approach helps in preventing fraud before it causes significant damage.

5. Market Research

Companies use text mining to analyze consumer opinions and market trends. By examining social media posts, reviews, and forums, businesses can gain insights into consumer preferences and behaviors. This information is valuable for product development, marketing strategies, and competitive analysis.

Implement Text Mining with InMoment

InMoment’s XI Platform has been recognized as one of the premier text-mining software solutions. Having recently been named a Leader in the Forrester Wave™: Text Mining and Analytics, the XI platform was noted as having capabilities that outperform competitors such as Qualtrics, AWS, and Google. To see what our text mining capabilities can do for you, schedule a demo today!

In this Post

What is Text Mining?Why is Text Mining Important?Difference Between Text Mining and Text Analytics How Text Mining Works Text Mining Best Practices Common Use Cases of Text Mining Implement Text Mining with InMoment

Watch the Video – See InMoment in Action

Check out a quick demo video to learn more about how our solutions can help your team grow your business.

Updated August 28, 2024

Published June 13, 2024

Written by Mike Henry

Reading time 9 minutes

Hotel Reputation Management: Boost Guest Satisfaction and Online Ratings

Hotel reputation management can offer you a competitive advantage by increasing bookings, repeat customers, and public perception of your brand.

Hotel reputation management has never been more important. With the increased usage of online review platforms and social media, guests hold unprecedented power to shape public perception. A bad review or post gone viral could deter potential guests from ever getting to your website. With these stakes in mind, your hotel reputation management strategy must be an integral part of your business strategy to keep your hotel competitive with the top brands.

Why Hotel Reputation Management Matters

Your hotel’s reputation is its most valuable asset. Positive reviews and glowing recommendations can drive bookings, while negative reviews can deter potential guests. In fact, research shows that a one-star increase in a hotel’s online rating can lead to a 9% increase in revenue.

Moreover, your reputation directly impacts brand loyalty and repeat business in an industry where word-of-mouth recommendations carry significant weight. A positive reputation not only attracts new guests but also fosters trust and loyalty among existing clientele.

Hotel Reputation Management Strategies for Success

There are various ways to improve the hospitality experience for your guests, but every great hotel reputation is built upon similar strategies. These strategies represent a commitment to the guest experience and lay the groundwork for continuous reputation success.

Proactive Engagement

Proactively engage with guests throughout their journey, starting from the moment they express interest in your hotel. Respond promptly to inquiries and provide personalized recommendations based on their preferences. During their stay, encourage guests to share their experiences on social media and review platforms. Engage with guests post-stay to thank them for their feedback and encourage them to return.

Transparency and Authenticity

Transparency builds trust and credibility with guests. Be upfront about any limitations or challenges your hotel may face, whether it’s related to amenities, renovations, or external factors like weather conditions. Authenticity in your interactions and communications fosters genuine connections with guests and helps mitigate the impact of negative feedback.

Cultivate a Positive Online Presence

Actively manage your hotel’s online reputation by monitoring reviews and mentions across multiple platforms. Respond promptly to all reviews, both positive and negative, in a professional and courteous manner. Knowing how to respond to Google reviews is a cornerstone of an effective reputation management strategy. Showcase positive feedback on your website and social media channels to highlight your strengths and reassure potential guests. Encourage satisfied guests to share their experiences online and provide guidance on where and how to leave reviews.

How to Improve Your Hotel Reputation

While reputation management software can be useful and should be considered for hotels looking to take their online reputation seriously, it is not required to begin improving your reputation. There are things you can start doing today to get a head start on ensuring your reputation makes your business an option for guests.

Respond Promptly and Professionally

Timely responses to guest feedback demonstrate your commitment to customer satisfaction. Monitor review platforms and social media channels regularly to address guest concerns in a timely manner. Craft personalized responses that acknowledge the guest’s feedback, express empathy, and outline steps taken to address their concerns.

Leverage Guest Feedback

Act on guest feedback to drive continuous improvement in your operations. Identify recurring themes and prioritize areas for improvement based on guest feedback data. Share feedback insights with relevant departments and empower staff to take ownership of guest satisfaction.

Optimize Online Presence

Optimize your hotel’s online presence across review platforms, social media channels, and search engines to maximize visibility and positive sentiment. Claim and update your business profiles on major review sites, ensuring accuracy and consistency of information. Encourage satisfied guests to leave reviews and ratings, and make it easy for them to do so by providing direct links and clear instructions. By improving your local SEO, you will make your business more prevalent in online searches.

Invest in Training and Development

Invest in ongoing training and development programs for your staff to equip them with the skills and knowledge needed to deliver exceptional service. Provide regular training sessions on topics such as communication skills, conflict resolution, and guest engagement. Empower your staff to take ownership of guest satisfaction and empower them to go above and beyond to create memorable experiences.

Examples of Proactive Hotel Reputation Management

Improving your hotel reputation does not have to be as complicated as creating in-depth sentiment analysis reports, it can be as simple as a brochure outlining your hotel’s amenities, or recommending must-see sights around town. Here are a few examples of hotel reputation management in action.

Personalized Welcome Packages

Leading hotels leverage guest data to curate personalized welcome packages that cater to individual preferences and interests. For instance, a luxury resort might greet guests with a handpicked selection of local delicacies or customized amenities based on previous stay history. By anticipating guest needs and preferences before arrival, hotels create a memorable first impression that sets the tone for the entire stay.

Proactive Issue Resolution

In the event of a service lapse or guest complaint, proactive hotels take swift and decisive action to address the issue before it escalates. For instance, if a guest reports an issue with their room during their stay, a proactive hotel might immediately offer a room upgrade or complimentary amenity as a gesture of goodwill. By addressing guest concerns proactively and empathetically, hotels not only salvage guest satisfaction but also showcase their commitment to service excellence.

Post-Stay Follow-Up

After guests depart, proactive hotels continue to engage with them through thoughtful post-stay follow-up communications. For example, a luxury hotel might send a personalized thank-you email to guests along with a survey to gather feedback on their experience. By soliciting post-stay feedback and expressing gratitude for their patronage, hotels demonstrate a commitment to continuous improvement and guest satisfaction.

Features to Look for in Hotel Reputation Management Software

Hotel reputation management software is essential for hotels looking to proactively manage their online reputation, enhance guest satisfaction, and drive long-term success in an increasingly competitive market. Reputation management software will offer various services, and the software you choose will depend on what is the best fit for your business. Regardless, certain features represent the core functionality of reputation management software, and how it should be used.

Review Monitoring and Aggregation: Choose a reputation management platform that aggregates reviews from major review sites like TripAdvisor, Google, Booking.com, and Expedia. A centralized dashboard allows you to monitor and respond to reviews from one location, which will save time and ensure consistency in your responses.
Sentiment Analysis: Look for software with advanced sentiment analysis capabilities that can automatically categorize reviews as positive, neutral, or negative based on the language used. This feature enables you to quickly identify trends and prioritize responses to reviews that require immediate attention.
Customizable Reporting: Robust reporting tools are essential for tracking key customer experience KPIs and measuring the impact of your reputation management efforts. Seek software that offers customizable reports with metrics such as review volume, average rating, sentiment trends, and competitor comparisons. The ability to schedule automated reports and receive alerts for significant changes in your reputation metrics is also beneficial.
Competitor Analysis: Gain insights into your competitors’ reputation performance to benchmark your performance and identify areas for improvement. Look for software that provides competitive analysis features, such as comparison tools, benchmarking reports, and market insights. Understanding how your hotel stacks up against competitors can inform your strategic decision-making and help you stay ahead of the competition.
Integration with CRM Systems: Seamless integration with your existing customer relationship management (CRM) system enhances the effectiveness of your reputation management efforts. Choose software that integrates with popular CRM platforms like Salesforce, HubSpot, or Microsoft Dynamics to ensure smooth data flow between systems. This integration enables you to leverage guest data to personalize your interactions and tailor your reputation management strategies to specific guest segments.

REPORT

Hospitality Reputation Benchmarks Report 2024

InMoment’s 2024 Hospitality Online Reputation Benchmarks Report provides a top-level view of the state of reviews today for the hospitality and restaurant industries — and how these reviews reflect the experiences patients want to have. The benchmarks serve as a valuable tool for organizations looking to assess their reputational performance, set goals, make informed decisions, and drive continuous customer experience improvement.

Download the Report

Improve Your Hotel Reputation with InMoment

No matter the size of your hotel or hotel network, InMoment’s dynamic XI Platform gives you all the tools you need to improve, monitor, and manage your hotel reputation! See what we can do for you by scheduling a demo today.

References

Stayntouch. How Online Reviews Impact Hotel Revenue. (https://www.stayntouch.com/blog/how-online-reviews-impact-hotel-revenue/). Accessed 5/6/2024.

In this Post

Why Hotel Reputation Management Matters Hotel Reputation Management Strategies for Success How to Improve Your Hotel Reputation Examples of Proactive Hotel Reputation Management Features to Look for in Hotel Reputation Management Software Improve Your Hotel Reputation with InMoment

Watch The Video – Make Online Reputation Your Acquisition Strategy

97% of consumers scope out online reviews before making a decision – watch this video to see how InMoment’s solution grows your business through online reputation management.

Updated September 20, 2024

Published June 7, 2024

Written by Mike Henry

Reading time 6 minutes

InMoment named Leader in Forrester Wave™: Text Mining and Analytics, Q2 2024: A Comprehensive Breakdown

InMoment is excited to announce its recognition as a Leader in the Forrester Wave™: Text Mining and Analytics, Q2 2024. This placement highlights our fierce commitment to innovation, customer satisfaction, and delivering future-proof technological solutions—no matter your business needs.

For some context, The Forrester Wave™ Text Mining and Analytics, Q2 2024 report is a rigorous evaluation of the top text mining and analytics providers. It assesses vendor scores based on three pillars: current product offering, strategy, and market presence, with subcategories that make up each pillar.

With top scores in 11 subcategories and the second-highest overall score, InMoment continues to set the standard for integrated customer experience (CX) solutions, empowering businesses to harness the full potential of their unstructured data. This recognition validates our approach, reinforcing our position as a trusted partner for enterprises seeking to enhance their CX initiatives. Let’s go over where we stand apart.

InMoment’s Standout Performance

These are the 11 subcategories we scored the highest possible scores in:

AI: knowledge-based or symbolic – Strong customization capabilities, transparency, and multitudes of available industry models, coupled with machine learning and generative AI-assisted adjustments.
AI: GenAI – pre-processing – Strengths focused on our ability to go beyond our standard filtering and search techniques to appropriately guide our generative AI solutions, such as AI Active Listening and Smart Summaries, to return high-quality results without common problems such as hallucinations.
AI: GenAI – post-processing – Multiple methods to check the validity of generative AI results, provide robust structures to allow our users to appropriately tailor the generative AI output to suit their brand voice, and log all generated text to understand what took place.
DevOps – Applications accessible to a broad set of users with low/no code options for customizations, extensions, automations, and integrations, plus APIs and Git integration for maximum flexibility.
Document level text mining/NLP – Supporting all industry-standard NLP functions, including classification (via ml models, boolean queries, or against pre-trained taxonomy based on Wikipedia categories), NER, theme/concept extraction, sentiment measurement, summarization, intention, effort, and emotion measurement.
Globalization – NLP is available natively in 30 languages using a mixture of linguistic and ML models for each supported language. Currently hosted in North America, EU, Australia and Japan, and available in all regions served by AWS.
Natural language understanding/NLU – InMoment supports 8 emotions, 11 intents, and has an effort model. Depending on the use case, these are either clause-based or deep-model based.
Support for All Relevant Use Cases – some of these are:
- General Purpose Text Mining: Analyzes training transcripts, regulatory updates, and client-specific documents.
- Omnichannel CX Analytics: Processes text from surveys, social media, voice, reviews, chat, email, and more to understand VOC, trends, and emerging topics, often paired with metrics to analyze impact and churn.
- Contact Centers: Analyzes call transcripts for categorization, QA, alerting, and enhanced CX with tools like Conversational Intelligence and dashboards for agents and managers.
Innovation – Following the Lexalytics acquisition, InMoment led text analytics innovation, which is recognized with industry awards. In 2023, we invested significantly in R&D, delivering 126 new features. Our strategy includes enhancing the NLP stack and integrating traditional and deep-learning models, emphasizing user-driven customizations and advanced NLP techniques.
Road Map
- Listening: Shifting from static to conversational feedback solicitation.
- Understanding: Summarizing data for rapid insights and integrating various data types.
- Automated Action: AI-generated coaching and smart responding combine with historical context for enhanced decision-making.
Number of Customers – Approximately 3,000 clients globally leverage our text mining and analytics capabilities and platform.

Our approach is founded on meeting the diverse needs of our clients, providing them with the tools and insights necessary to drive impactful CX improvements.

Comparison with Major Competitors

InMoment’s performance in the Forrester Wave™ report sets us apart from other big CX companies like Qualtrics. We beat out giants like AWS, Google, and IBM in our product offering, and Qualtrics in product offering and strategy. Unlike our competitors, InMoment offers a truly integrated experience that combines data from multiple sources, providing a holistic view of customer feedback.

Comprehensive Use Case Support

Our integrated CX analytics capabilities capture, analyze, and unify feedback from multiple customer feedback channels, including surveys, social media, voice interactions, and more. This integrated, holistic approach empowers businesses with a complete understanding of their customer journey, identifying key pain points and opportunities for improvement.

Extended Use Cases InMoment’s platform is not limited to traditional CX analytics. We also support extended use cases such as:

Employee Experience (EX) Analytics

Our tools help businesses understand and improve employee engagement, onboarding processes, and workplace satisfaction.

Social Listening

We provide valuable insights into brand perception and customer sentiment by analyzing sentiment and trends from social media feeds.

Market Intelligence

Our platform supports ad-hoc research, brand awareness studies, and industry trend analysis, helping businesses stay ahead of market changes and consumer expectations.

Customer service icon to represent contact centers.

Contact Center Intelligence

We offer templatized solutions that pull text analytics and revenue-specific insights, enabling businesses to drive sales and improve financial performance.

Client Testimonials

The use of InMoment AI will allow us to easily analyze feedback in all of its forms to receive more detailed and immediate insight from a wider variety of guest experiences.

Tony Darden

Chief Operating Officer, Jack in the Box

In today’s tumultuous world, we understand the importance of creating a company that is dedicated to delivering products that contribute to positive “me” time and “we” time for family and friends. Our collaboration with InMoment allows us to understand, in greater detail, our customers' wants and needs and helps us know how they feel so we can capture the hearts of our customers by creating superior experiences and products.

Jorge Calvachi

Director of Insights, La-Z-Boy

Through our ongoing collaboration with InMoment, we continue to drive toward a deeper understanding of the customer experience. InMoment’s flexible survey platform coupled with their expertise in intelligently bringing together customer feedback has allowed us to efficiently expand our listening program globally and understand key drivers to customer’ loyalty and satisfaction. We now have a better understanding of our customers’ diverse needs and expectations, and our regional teams can make smarter business decisions that benefit each customer and every experience.

Deborah Battaglia

Senior Vice President, Customer Experience at Assurant

What’s Up Next?

InMoment’s vision for the future focuses on continuous innovation and improvement. Our roadmap includes significant advancements in AI and NLP, particularly enhancing feedback solicitation and data integration. Here’s how these initiatives will empower your brand:

Conversational Listening: Moving from static to dynamic, conversational feedback solicitation will significantly improve the quality and quantity of customer insights. For instance, real-time conversational analytics will allow your support teams to address issues more promptly and accurately, enhancing customer satisfaction and loyalty.

LLM-Powered Listening: Leveraging large language models to enhance the understanding of customer feedback will enable deeper, more nuanced insights. This can help your marketing team create targeted campaigns based on precise customer sentiment and preferences, driving higher engagement and conversion rates.

Non-Text Data Ingestion: Integrating non-textual data sources will provide a more complete view of the customer experience. For example, combining text analytics with visual or behavioral data can give your product development team a holistic view of how customers interact with your products, leading to more user-friendly designs and features.

Automated Action: Implementing AI-assisted frontline coaching and content recommendations will drive efficient customer recovery and engagement. Imagine your customer service platform automatically suggesting the best responses to inquiries based on historical data and context, reducing response times and improving the overall customer experience.

These advancements will position InMoment at the forefront of the CX industry, providing our customers with the toolkit they need to drive customer acquisition, retention, and growth. By integrating these cutting-edge technologies, your brand can stay ahead of the competition, continuously adapt to evolving customer needs, and achieve sustainable business success.

Conclusion

InMoment’s leadership in the Forrester Wave™: Text Mining and Analytics, Q2 2024, is a testament to our relentless pursuit of excellence and innovation, and while we may be smaller than some of the giants—Qualtrics, Google, AWS, IBM—our product offering is even mightier. Our comprehensive platform, advanced AI capabilities, and customer-centric approach set us apart, empowering businesses to achieve significant ROI through enhanced customer experiences.

Discover the power of InMoment’s text mining, analytics, and general CX solutions. Visit our website to learn more about our offerings and why the world’s most customer-centric brands trust InMoment. Schedule a demo with us today and take the first step towards transforming your customer experience program.

In this Post

InMoment's Standout Performance Comparison with Major Competitors Comprehensive Use Case Support Client Testimonials What’s Up Next?Conclusion

Watch the Video – Get More Out of Your CX Program

Watch this demo video to see how InMoment’s award-winning AI tech does the heavy lifting on analysis so you can spend more time lifting CX results.

Updated August 29, 2024

Published June 5, 2024

Written by Dan Butler

Reading time 5 minutes

Choosing the Best Text Analysis Software for Your Business

In a business landscape that has become digital-first, where consumers’ expectations can change overnight, it has never been more important to the success of your business to be able to understand and leverage data. One of this environment’s most challenging yet rewarding aspects is making sense of unstructured text data such as online reviews or customer emails. Most businesses achieve this by utilizing text analysis software. Text analysis software, also known as text analytics software, has become indispensable for businesses aiming to extract actionable insights from textual data to improve the customer experience.

What is Text Analysis Software?

Text analysis software utilizes natural language understanding (NLU), a subset of natural language processing (NLP), to analyze text data. Given the unstructured nature of text, these tools process textual input to generate labels, tags, and insights. Text analysis has become a critical component of analytics and business intelligence, enabling companies to derive meaningful information from unstructured data sources such as emails, social media, online reviews, and more.

Whether it’s analyzing online reviews, customer feedback, or any other form of unstructured data, these tools can sift through large amounts of information and highlight important elements. By converting raw data into actionable insights, businesses can improve their strategies and make more informed decisions.

Why Your Business Needs Text Analysis Software

Most businesses are already collecting vast amounts of data, whether in the form of surveys, reviews, order history, contact center complaints, or other forms of data. However, collecting this data can be time-consuming and inefficient without the proper tools. By utilizing text analysis software, your business can be more efficient and realize benefits such as increased customer insights, improved operational efficiency, and others.

Types of Text Analysis Software

There are various types of text analytics software, each with its unique strengths. Some focus on sentiment analysis, which gauges emotions behind the text data. Others specialize in predictive analytics, using past data to forecast future trends. Text analytic software also prioritizes text mining, digging deep into the data to find hidden insights.

Regardless, when it comes to the kinds of text analysis software you’ll see in the market, they will all fall under two categories: self-service text analysis software and traditional text analysis software. The one that you end up choosing will depend on a variety of factors, as each type of software serves different purposes for different types of users.

Self-Service Analysis Tools

Self-service text analysis software is designed for users with little to no technical knowledge of text analysis tools. These platforms often feature drag-and-drop interfaces, prebuilt templates, and interactive dashboards, making them accessible to just about anyone.

Examples:

Analyzing customer emails for sentiment trends
Creating dashboards to track marketing campaign performance

Traditional Text Analysis Tools

Traditional text analysis tools are geared toward technical professionals such as data analysts or data scientists. They offer extensive customization options, allowing users to create and fine-tune their algorithms and models. This flexibility is ideal for complex, large-scale projects that require specific, tailored solutions.

Due to their complexity, these tools may require a higher level of involvement or technical expertise, such as managed services or a dedicated representative. They may require ongoing support from IT departments or data science teams to maintain and update.

Examples:

Ingesting social media data to detect emerging trends.
Developing custom models to predict customer churn.

By carefully evaluating these factors, businesses can select the text analysis tool that best fits their specific requirements and enhances their ability to leverage text data for strategic advantage.

Things to Look for in Text Analysis Software

Selecting the right text analysis software is crucial for leveraging unstructured data to derive actionable insights. While features may vary from one software product to another, there are some key features you should consider when evaluating a software product for your business.

Natural Language Processing Capabilities

Language Identification: The ability to detect and process multiple languages, ensuring the software can handle diverse data sources.
Tokenization: Breaking down text into smaller components, such as words or phrases, for detailed analysis.
Part of Speech Tagging: Identifying the grammatical components of text (e.g., nouns, verbs, adjectives) to understand the structure and context.

Sentiment Analysis

Sentiment Scoring: Determining the sentiment (positive, negative, neutral) of the text to gauge customer opinions or feedback.
Emotion Detection: Identifying specific emotions, such as happiness, anger, or sadness, for more nuanced insights.

Text Classification and Categorization

Automated Tagging: Automatically assigning tags or labels to text based on predefined categories or machine learning models.
Custom Taxonomies: Allowing users to create custom classification schemes to fit specific business needs.

Data Visualization

Interactive Dashboards: Providing visual representations of text analysis results through charts, graphs, and other visualization tools.
Real-time Analytics: Offering real-time updates and visualizations to monitor ongoing trends and changes.

Integration and Compatibility

API Access: Offering APIs for seamless integration with other business systems and data sources.
Data Source Connectivity: Connecting with various data sources such as social media, CRM systems, emails, documents, and more.

Benefits of Text Analysis Software

Incorporating text analysis software into your business operations can offer a multitude of advantages. Here are some key benefits that can significantly enhance your organization’s efficiency, decision-making, and overall performance:

Enhanced Customer Insights

Text analysis software can process vast amounts of customer feedback from sources such as social media, emails, and online reviews. By determining the sentiment behind customer communications, businesses can gain valuable insights into customer satisfaction and loyalty, allowing them to address issues proactively and improve customer relationships and uphold their brand reputation.

Businesses can also identify recurring themes and patterns in customer interactions, which allows them to uncover emerging trends and shifts in customer preferences. This enables them to adapt their products, services, and marketing strategies to better align with customer needs.

Improved Decision-Making

With text analysis software, businesses can transform unstructured text data into structured insights that inform strategic decisions. Furthermore, text analysis can also be used to predict future trends based on historical data.

For instance, analyzing past customer reviews and feedback can help businesses anticipate future customer behavior and preferences, allowing for more accurate forecasting and planning. This data-driven approach helps managers and executives make more informed and effective decisions, leading to better business outcomes.

Increased Operational Efficiency

Text analysis software automates the extraction and classification of key information from large volumes of text data, significantly reducing the time and effort required for manual data processing.

By efficiently categorizing and prioritizing text data, text analysis software helps organizations allocate resources more effectively. For example, customer service teams can prioritize inquiries based on sentiment and urgency which ensures that critical issues are addressed promptly.

Improved Employee Engagement

Internally, text analysis software can be used to gauge employee sentiment through surveys, feedback forms, and other communications. Understanding how employees feel about various aspects of their work environment can help management make informed decisions to improve employee satisfaction and engagement.

Text analysis software can also be used to analyze text data from performance reviews and other employee interactions to provide insights into workforce performance and highlight areas for development and training.

The benefits of text analysis software extend across various facets of business operations, from customer service and marketing to compliance and employee engagement. By leveraging the power of text analysis, organizations can unlock valuable insights from their unstructured data, leading to improved decision-making, increased efficiency, and a competitive edge in the market.

Who Uses Text Analysis Software?

Text analysis software is a versatile tool that can be employed across various departments within a business, each leveraging its capabilities to enhance its specific functions. Here are some key areas of business where text analysis software is commonly used, along with how professionals in those areas utilize it:

Data Scientists

Data scientists are primarily responsible for analyzing and interpreting complex data to help businesses make informed decisions. They can use text analysis software for:

Algorithm Development: Data scientists use text analysis software to develop and refine natural language processing (NLP) algorithms that can classify, tag, and extract meaningful insights from text data.
Sentiment Analysis: They apply sentiment analysis to gauge customer sentiment from reviews, social media posts, and other text sources, helping the company understand public perception and improve customer experience.
Predictive Modeling: By analyzing historical text data, data scientists can create predictive models that forecast trends and customer behavior, aiding in strategic planning.

Marketing Teams

Marketing professionals are tasked with promoting products and services, understanding market trends, and engaging with customers effectively. They use text analysis software for:

Campaign Analysis: Marketers use text analysis software to assess the performance of marketing campaigns by analyzing customer feedback, social media interactions, and online reviews.
Audience Segmentation: Text analysis helps in segmenting audiences based on their interactions and sentiments, allowing marketers to tailor their messages for different customer groups.
Content Optimization: By analyzing which keywords and phrases resonate most with their audience, marketers can optimize their content for better engagement and conversion rates.

Customer Service Teams

Customer service representatives are responsible for managing customer inquiries, resolving issues, and ensuring customer satisfaction. They can use text analysis for:

Sentiment Detection: Text analysis software can detect the sentiment of customer emails, chat messages, and social media posts, helping representatives prioritize and address negative feedback promptly.
Trend Identification: By identifying common issues and complaints through text analysis, customer service teams can proactively address recurring problems and improve service quality.
Automation: Automated text analysis can classify and route customer queries to the appropriate departments or representatives, enhancing response times and efficiency.

Sales Teams

Sales professionals focus on generating leads, closing deals, and building customer relationships to drive revenue.

Lead Qualification: Text analysis can help sales teams analyze and prioritize leads based on the language and sentiment used in customer interactions, improving the likelihood of conversion.
Customer Insights: By analyzing past communications and feedback, sales teams can gain insights into customer preferences and pain points, enabling more personalized and effective sales pitches.
Pipeline Management: Text analysis software can track and analyze sales emails and call transcripts to identify trends and patterns, helping sales managers forecast pipeline health and performance.

Human Resources

Human resources (HR) professionals manage recruitment, employee relations, and organizational development. They use text analysis for:

Employee Sentiment Analysis: Text analysis tools can evaluate employee feedback from surveys, performance reviews, and internal communications to gauge overall sentiment and engagement levels.
Recruitment: Analyzing text from resumes and cover letters can help HR teams identify the best candidates for open positions based on specific keywords and experience levels.
Policy Effectiveness: HR can use text analysis to assess the effectiveness of company policies by analyzing employee feedback and identifying areas for improvement.

Finance Teams

Finance professionals manage the company’s financial planning, analysis, and reporting. They can use text analysis for:

Expense Analysis: Text analysis can help categorize and analyze free-text data in expense reports, uncovering spending patterns and identifying cost-saving opportunities.
Risk Management: Finance teams can use text analysis to monitor financial news and reports for early warning signs of market changes or potential risks.
Compliance: By analyzing communications and transaction records, finance teams can ensure adherence to regulatory requirements and detect potential compliance issues.

Text analysis software is a valuable asset for various departments within a business, from data science and marketing to customer service and HR. Each professional group utilizes the software to extract actionable insights from unstructured text data, enabling better decision-making, enhanced efficiency, and improved overall performance. By integrating text analysis into their workflows, businesses can harness the full potential of their data and drive success across all areas.

Challenges with Text Analysis Software

While text analysis software offers numerous advantages, its deployment, and effective use come with several challenges that organizations need to address to maximize its potential. These challenges include:

Data quality and preprocessing
Language and content understanding
Integration with existing systems
Scalability
Privacy and security concerns

These challenges can be avoided through strategic planning, advanced technology, and cross-functional collaboration. It is also important to ensure that the software is implemented with users being trained on it in the most efficient manner.

How to Purchase Text Analysis Software

Selecting the right text analysis software for your business is a critical decision that can significantly impact your organization’s ability to harness insights from unstructured text data. Here’s a step-by-step guide to help you navigate the purchasing process effectively:

1. Define Your Requirements

Start by understanding the specific needs of your business. Are you looking to improve customer service, enhance marketing strategies, monitor brand reputation, or gain insights from internal communications?

Next, list must-have features such as sentiment analysis, entity recognition, keyword extraction, language support, integration capabilities, and user-friendliness. Ensure the software can scale with your business and adapt to evolving needs. Flexibility in terms of deployment (cloud-based or on-premises) is also important.

2. Conduct Market Research

Research the available text analysis software solutions in the market. Use review sites, industry reports, and technology forums to gather information on the top contenders.

Compare vendors based on features, pricing, customer reviews, and industry reputation. Look for case studies or success stories from businesses similar to yours.

Arrange for product demonstrations to see the software in action. Pay attention to accuracy, speed, and value of insights during these demos.

3. Evaluate Total Cost of Ownership

Consider the upfront costs, including licensing fees, setup costs, and any required hardware or infrastructure investments. Factor in maintenance fees, subscription costs, potential costs for updates or additional features, and support services. Be aware of any hidden costs, such as training, customization, and integration with existing systems.

4. Assess Integration and Compatibility

Integration with Existing Systems:

Ensure the software can integrate seamlessly with your current IT infrastructure, including CRM, ERP, and other data management systems.

Data Import and Export:

Check if the software supports easy data import/export to facilitate smooth data migration and interoperability with other tools.

5. Consider User Experience

Choose software that is user-friendly and requires minimal training. A good user interface can significantly enhance adoption rates among employees. Evaluate the vendor’s customer support services. Look for comprehensive training programs, documentation, and community forums.

Purchasing text analysis software is a strategic investment that requires careful consideration of your business needs, budget, and technical requirements. By following these steps—defining your requirements, conducting thorough research, evaluating costs, ensuring compatibility, considering user experience, ensuring security, pilot testing, and negotiating terms—you can select the right solution that will provide valuable insights and drive business success.

Implementation of Text Analysis Software

Implementation of text analysis software should be done in a systematic and planned manner. It’s important to train your team on how to use the software and understand the insights it provides. You should also regularly evaluate the software’s performance and make adjustments as needed to ensure it continues to meet your business needs.

Text Analysis Software Trends

In today’s digital age, text analytics solutions continue to evolve. One emerging trend is the increasing use of AI in text analytics, enhancing the software’s ability to understand and interpret human language. Another trend is the growth of real-time analysis, enabling businesses to respond quickly to emerging trends or issues. The future of text analysis software looks promising, with new advancements on the horizon that will further enhance its functionalities.

Text Analysis Software with InMoment

If you are looking to improve your text analysis, consider InMoment as your partner. InMoment was recently named a leader in the Forrester Text Mining & Analytics Wave. Recognized for our knowledge-based AI and best-in-class text extraction, the InMoment platform is the best choice for enterprises looking to take their text analytics to the next level. You can read the full report here!

REPORT

InMoment Named a Leader in The Forrester Wave™: Text Mining And Analytics Platforms, Q2 2024

Learn how InMoment is pioneering innovative solutions for businesses to extract insights and drive meaningful change from their unstructured text data.

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In this Post

What is Text Analysis Software?Why Your Business Needs Text Analysis Software Types of Text Analysis Software Things to Look for in Text Analysis Software Benefits of Text Analysis Software Who Uses Text Analysis Software?Challenges with Text Analysis Software How to Purchase Text Analysis Software Implementation of Text Analysis Software Text Analysis Software Trends Text Analysis Software with InMoment

Watch the Video – See InMoment in Action

Check out a quick demo video to learn more about how our solutions can help your team grow your business.

Updated August 13, 2024

Published May 30, 2024

Written by Mike Henry

Expert reviewed by Sandeep Garg

Reading time 13 minutes

Securing Brand Loyalty In The Third Wave Of CX

Maintaining customer loyalty has become increasingly challenging in today’s digital-first environment. According to Stanford Swinton, Executive VP at Bain & Company and Founder at NPSx by Bain in Company, we’re witnessing the dawn of the “Third Wave of CX”. But what does this mean for CX leaders?

In a recent podcast featuring Simon Fraser, VP Insights and Consultancy at InMoment, and Stanford Swinton, key strategies to secure brand loyalty amidst the evolving landscape of customer experience (CX) were discussed. Here are the key highlights:

Understanding the Third Wave of CX

The evolution of customer experience (CX) functions mirrors the changing landscape of customer expectations. The “third wave” of CX represents a paradigm shift in how businesses approach customer experience. Over the past few decades, CX practices have evolved significantly. Initially, the focus was on identifying the most suitable customer experience metrics, such as Net Promoter Score (NPS) or customer satisfaction scores. However, this approach has matured, shifting towards a deeper understanding of customer behaviour and feedback loops. Now, in the third wave, the landscape is changing once again. Best practices from five years ago are no longer sufficient. Personalisation has become paramount, necessitating a move towards customer-centric, predictive machine learning. This shift emphasises understanding customer value and leveraging AI to predict outcomes.

Personalisation and Ethical Differentiation

In the third wave, businesses must combine personalisation with values to stand out. While digital strategies and omnichannel experiences were once differentiators, customers now seek tailored experiences and align themselves with brands that stand for something meaningful. There’s a growing trend for companies to differentiate themselves based on their ethical principles and social missions. By prioritising customer enrichment and ethical practices, brands can establish a deeper connection with consumers, fostering long-term loyalty.

Predictive CX and Value Unlocking

Predictive CX goes beyond traditional surveys; it correlates customer sentiment with financial outcomes, providing actionable insights for business growth. Predictive CX leverages AI and machine learning to understand customer behaviour beyond survey responses and in turn, businesses can design targeted experiences that drive value and loyalty. Understanding who your customers are and categorising their needs, journeys, and drivers of experience is essential. This involves creating a robust customer taxonomy to structure data sets effectively. Both structured and unstructured data play crucial roles in predictive CX. By harnessing predictive analytics, businesses can unlock value from their customers.

Challenges and Opportunities for Businesses

Transitioning to the third wave of CX presents both challenges and opportunities for businesses. While legacy CX practices may pose initial hurdles, organisations can leverage existing frameworks and garner support from key stakeholders. By aligning CX initiatives with broader business objectives and demonstrating the potential for top-line growth, CX leaders can drive organisational change effectively.

Embracing the Third Wave

In conclusion, securing brand loyalty in the third wave of CX requires a proactive approach to adaptation and innovation. The shift to the third wave of CX is not just a trend; it’s a necessity for businesses looking to thrive in the digital age. By embracing personalised experiences, ethical differentiation, and predictive analytics, companies can secure brand loyalty and drive positive economic outcomes.

Ready to explore the full discussion on securing brand loyalty in the third wave of CX? Listen to the full podcast below!

In this Post

Understanding the Third Wave of CX Personalisation and Ethical Differentiation Predictive CX and Value Unlocking Challenges and Opportunities for Businesses Embracing the Third Wave

Watch the Video – Get More Out of Your CX Program

Watch this demo video to see how InMoment’s award-winning AI tech does the heavy lifting on analysis so you can spend more time lifting CX results.

Updated August 1, 2024

Published May 17, 2024

Written by The InMoment Team

Reading time 2 minutes

Build an Integrated CX Program

Build an Integrated CX Program

2024 Online Reputation Benchmark Report

How Customer Feedback Can Boost Your Bottom Line

What Is Customer Feedback?

What Is A Customer Feedback Loop?

Why Is Customer Feedback Important?

What Are the Benefits of Collecting Customer Feedback?

Improve Customer Satisfaction

Improve Business

Identify New Opportunities

Build Customer Loyalty

Direct vs Indirect Customer Feedback

Direct Feedback

Indirect Feedback

Types of Customer Customer Feedback

Surveys

Contact Center Interactions

Social Media

Customer Reviews

How to Analyze Customer Feedback

Text Analysis

Customer Experience Metrics

Customer Journey Mapping

How to Develop A Customer Feedback Strategy

1. Define Your Objectives

2. Identify Feedback Channels

3. Design Effective Feedback Tools

4. Implement a Feedback Collection System

5. Analyze the Feedback

6. Close the Feedback Loop

7. Prioritize Business Actions

8. Monitor and Measure Results

9. Foster a Culture of Continuous Improvement

10. Iterate and Adapt

Best Practices for Collecting Customer Feedback

Ensure Anonymity

Focus on Timing

Thank Customers for Their Feedback

Collect Customer Feedback with InMoment

In this Post

Watch the Video – Get More Out of Your CX Program

Get More Out of Your CX Program

The 2024 Consumer Trends Report

10 Steps to Easily Build An Integrated Customer Experience Program

5 Tips to Expertly Manage & Boost Your Online Reputation

The Role of Social Media and Reviews in Online Reputation Management

Tips for Managing Your Online Reputation

1. Implement Robust Data Collection Mechanisms

2. Respond Swiftly to Feedback

3. Leverage Predictive Analytics

4. Personalized Customer Interactions

5. Establish Training and Policies

The Stakes of Ignoring Social Media and Reviews

Let Inmoment Help You Manage Your Online Reputation

In this Post

Watch The Video – Make Online Reputation Your Acquisition Strategy

Make Online Reputation Your Acquisition Strategy

3 Proven Methods to Improve Your Brand Reputation

How to Grow Your Business with Reputation Management

Speech Analytics: Turning Conversations into Actionable Insights

What is Speech Analytics?

What is Contact Center Speech Analytics?

How Does Speech Analytics Work?

Why is Speech Analytics Important?

What are the Benefits of Speech Analytics?

Improved Agent Performance

Enhanced Customer Understanding

Real-time Problem Resolution

Speech Analytics Uses Cases & Examples

Speech Analytics in Healthcare

Speech Analytics in E-Commerce

What is Next For Speech Analytics?

AI Speech Analytics

Holistic Analytics

What to Look For in Speech Analytics Software

Diverse Analysis Capabilities

Integration Capabilities

Support

Best Practices for Implementing Speech Analytics Software