Delivering exceptional customer experiences is a fundamental and necessary business practice that also can significantly impact your bottom line – but up until this point, it wasn’t a requirement. The recent “Time is Money” White House initiative, launched just last week, represents a significant escalation of onus on businesses to address subpar customer experiences and comply with these new directives.
Delivering exceptional customer experiences is a fundamental and necessary business practice that also can significantly impact your bottom line – but up until this point, it wasn’t a requirement. The recent “Time is Money” White House initiative, launched just last week, represents a significant escalation of onus on businesses to address subpar customer experiences and comply with these new directives.
In an effort to assist organizations in the process of reevaluating their customer experience platform and conversation intelligence infrastructure – including front-line staff training and backend processes – InMoment conducted a market pulse – a deep dive of consumer sentiment – that combines survey and non-survey data to surface common consumer pain points and provide steps organizations can take to elevate conversational intelligence efforts and get in front of the issues for a competitive advantage – and avoid fines and other costly, negative business impacts.
Key Insights from the Study
Accessibility to Live Support: 70% of customers struggle to reach a live representative, with 39.6% experiencing this frustration frequently. One in four customers expressed dissatisfaction with long hold times and repeated transfers. This inefficiency drives customers away, leading to increased churn.
Automated Systems: Nearly 68.5% of customers are dissatisfied with automated customer service, feeling trapped in systems that don’t resolve their issues. A significant one in three customers highlighted the need for smarter, more responsive automation.
Omnichannel Communication: Customers demand seamless transitions across communication channels, with 94.2% valuing the ability to reach companies through their preferred method. However, nearly one in five customers are frustrated by having to repeat their issues when switching channels.
Accountability and Transparency: Trust is eroded when promises are broken. One in four customers cited poor follow-up and lack of communication as key frustrations, leading to higher churn rates.
Actionable Strategies to Enhance Customer Experience
Invest in Conversational Intelligence: Elevating your conversational intelligence efforts is becoming more of a necessity in today’s landscape. Use a conversational intelligence software to analyze millions of interactions across call centers, chat, and digital channels to identify and resolve bottlenecks and enhance the contact center experience. By reducing wait times and improving first-call resolution, you can decrease churn by 10% and retain an additional $10 million in revenue annually.
Enhance Automated Systems: Improve your chatbots and phone menus to better understand and address customer needs. The consumer market is evolving – as an example, GenZ is looking for a more self-service, seamless solution – if you aren’t leveraging data to evolve those experiences, you are missing out. A 5% increase in retention due to improved automation can result in $5 million in additional revenue.
Embrace Omnichannel Engagement: Customer centricity is key; as stated above, 70% of customers struggle with live support, and nearly 68.5% feel trapped by automated systems. If your organization isn’t doing whatever you can to learn, gauge insights, and be proactive – millions of dollars of revenue could be at risk. Integrate all customer interactions across channels, including Google Reviews and social media, to ensure seamless service. By doing so, you can reduce churn by 7% and retain $7 million in revenue.
Ensure Accountability: In today’s landscape, consumer trust and accountability are key. Not focusing on these elements can cause your organization to lose loyalty. Creating a proactive closed-loop approach is more important than ever, especially with customer demands changing so rapidly. Implement robust follow-up systems to keep customers informed and fulfill promises. Resolving 95% of complaints within 24 hours can lead to a 12% decrease in churn, retaining $12 million in revenue annually.
The results are clear: brands that fail to address these issues risk losing valuable customers, while those that take proactive steps can secure long-term loyalty and substantial revenue gains.
At InMoment, we don’t just identify problems—we offer the solutions you need to drive real results. Our advanced AI capabilities, combined with actionable insights from customer feedback, empower your brand to take decisive action. By focusing on the entire customer journey, we help you reduce revenue at risk, enhance customer loyalty, and secure long-term growth.
Ready to transform your customer service and see the impact on your bottom line?Contact InMoment today or check out a preview of our platform to learn how we can help you turn insights into actions that drive retention, reduce churn, and protect your revenue.
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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.
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).
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.
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.
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Customer experience metrics are crucial to tracking the success of a customer experience program. They help prioritize actions, benchmark against competitors, and more. Learning how to utilize these metrics is important to create long-lasting CX success.
As competition and buyer empowerment compound, customer experience (CX) is proving to be the only truly durable competitive advantage. Organizations must learn how to measure, manage, and act on customer experience KPIs and metrics so that they can deliver experiences that lead to increased loyalty, lower churn, more referrals, positive word of mouth, and higher-value customers.
Companies that earn $1 billion annually can expect to earn on average an additional $700 million within 3 years of investing in customer experience.
Customer-centric companies are 60% more profitable than companies that don’t focus on customers.
As your organization starts your customer experience management efforts, you need to consider how to measure it. CX is a multi-layered concept, and to truly understand customer experience at scale, you need to have a good understanding of customer experience KPIs and metrics.
How to Measure Customer Experience
Measuring customer experience is a strategic imperative that helps your company build strong, long-lasting relationships with your customers, stay competitive, and adapt to changing market dynamics.
Measuring CX requires a layered approach that can include in-depth user interviews and gathering data at key points of contact, as well as tracking customer experience metrics like NPS, CSAT, and CES, among others. It is an ongoing process that requires attention to customer feedback, continuous improvement, and a commitment to delivering value.
It also involves collecting and connecting customer experience data from every touchpoint and channel for a complete view of the customer journey. After all, customers use a variety of channels to interact with your brand, such as your store, website, mobile app, contact center, social media, online review websites, and so much more.
As your company looks to measure customer experience KPIs and metrics, it’s important to integrate data from all the touchpoints and channels that matter to your business so that metrics are not analyzed independently of the broader experience.
Keep in mind that no single customer experience KPI or metric will give you a complete picture, and you will have to discover how to adapt the metrics you’re tracking to your business case. Nonetheless, various customer experience survey methodologies and metrics are used across industries and serve as a great place to start as you grow your program.
What Are Customer Experience KPIs and Metrics?
Customer experience KPIs and metrics are indicators that enable your organization to gain a comprehensive understanding of your customer experience performance. Regularly tracking and analyzing these metrics can guide your business as you look to make informed decisions that enhance customer satisfaction and loyalty.
Net Promoter Score (NPS)
Net Promoter Score (NPS) is a CX metric that surveys customers based on one question: “On a scale of 0 to 10, how likely are you to recommend us to a friend or colleague?”
Promoters (score 9-10) are loyal, satisfied customers who will help fuel your business growth by buying and referring other customers to your business.
Passives (score 7-8) are also satisfied customers, but their lack of enthusiasm may render them vulnerable to offerings from the competition.
Detractors (score 0-6) are unhappy customers who may impede your growth and spread negative word of mouth about your business.
To calculate your Net Promoter Score, simply subtract the percentage of Detractors from the percentage of Promoters.
NPS Survey Powered by InMoment
NPS is a valuable tool for measuring not just customer experience, but also customer loyalty, since it transcends single experiences. It is often referred to as a brand or relationship metric. The NPS question asks the customer to draw on the sum of their experiences with your company, not just the most recent, making it a good indicator for repurchasing (and growth). As a result, it is often considered a “board-level” metric.
NPS is a great place to start when you’re looking to measure customer experience. However, if you would like to learn more about the experience you provide at specific touchpoints or transactions along your customer’s journey, Customer Satisfaction Score (CSAT) should be one of the customer experience KPIs to track.
Customer Satisfaction Score (CSAT)
Customer Satisfaction Score (CSAT) is the most popular transactional metric. A customer satisfaction survey asks a customer how satisfied they are with a recent interaction — often a purchase or a customer service call — on a rating scale. CSAT is flexible and highly customizable. In some cases, emojis (smileys, frowns) are used instead of numerical scales to overcome any language barrier.
Above is a 2-step CSAT survey on a mobile app powered by InMoment
In the realm of CX, a short CSAT survey is most often used to gauge customer satisfaction with interactions with support personnel. It’s a great tool for identifying support agents who may need more training or for quantifying the impact of your last team-wide training effort. You will need to dig into the qualitative feedback you receive to understand which attributes of satisfaction are most important to your customers and which areas require improvement.
A related survey metric is the PSAT or Product Satisfaction Score. This is an adaption of the CSAT survey that is popular with software developers and advocates of product-led growth. An example is an in-app survey that asks a software user, “How satisfied are you with this product or feature?” The specific, contextual feedback that users provide in a PSAT survey helps to prioritize a roadmap of product improvements.
Customer Effort Score (CES)
Customer Effort Score (CES) surveys ask the customer, “How much effort did you have to expend to handle your request?” This is scored on a numeric scale. It’s a customer service metric that is typically used to improve systems that may frustrate customers.
Customers may then respond on a 5- or 7-point scale, and scores are calculated simply by getting the average of all the collected responses. Reducing customer effort can be a valuable marketing investment that makes your brand stand out in a sea of unaccommodating, not-very-helpful competitors.
CES advocates believe that when it comes to customer service or support, “effortlessness” is the most relevant attribute of customer satisfaction. Tracking Customer Effort Score helps you identify and remove obstacles, and solve problems, so your customers can find success with ease. According to the Harvard Business Review, CES can predict repurchasing even better than CSAT, making it a go-to critical metric for companies that depend heavily on successful onboarding and customer success to lay the foundation for repeat purchases.
Churn Rate
Customer churn or attrition is defined as the loss of clients or customers and is also one of the first and most obvious indicators of customer dissatisfaction. This makes churn rate one of your most important customer experience metrics; it is especially critical if your business model is subscription-based (for example: software companies and membership-based services).
To calculate the customer churn rate, first determine the period for which you want to calculate the churn rate (e.g., monthly, quarterly, or annually). Then count the customers at the start of the period, as well as the number of customers lost during the period. Use the following formula to calculate the churn rate:
Churn Rate (C) = (Number of Customers Lost % Number of Customers at the Start of the Period) × 100
This formula expresses the churn rate as a percentage.
It’s important to note that while calculating the churn rate provides valuable insights, understanding the reasons behind customer churn is equally crucial. Analyzing customer feedback, conducting surveys, and identifying patterns can help you take proactive measures to reduce churn and improve overall customer satisfaction.
Tracking churn rate will also allow you to see and apply new ways to handle the challenging situation of customers canceling their plans or subscriptions as well as to overcome other roadblocks to fostering customer loyalty. Organizations that consistently keep an eye on this customer experience KPI are also better at predicting if and when a customer is at risk of churning so that they can take the next step and close the loop with at-risk customers.
Retention Rate
Customer retention rate is a customer experience KPI that measures the percentage of customers your business retains over a specific period. It’s a great way to assess your customer experience performance as well as the effectiveness of your customer retention strategies. A higher customer retention rate typically indicates that a company is keeping its existing customers satisfied.
To calculate your retention rate as a percentage, simply follow this formula: customer retention Rate = (Retained Customers % Number of Customers at the Start of the Period) × 100.
Monitoring and improving customer retention rates are essential for the long-term success of your organization. A high retention rate is often associated with increased customer lifetime value (CLV) and reduced customer acquisition costs. To make the most out of this metric, you should complement retention rate analysis with customer feedback to continuously enhance the customer experience and address potential areas of concern.
Customer Lifetime Value (CLV)
Customer lifetime value (CLV) is a metric that represents the predicted net profit your company can expect to earn from a long-term relationship with a single customer. This will tell you what a single customer is worth to your business throughout the course of the relationship.
Learning how to measure and increase customer lifetime value will help your company forge stronger customer relationships and achieve a competitive edge in the market. Increasing CLV will also improve the long-term profitability of your business, allowing your company to identify which customers are most valuable over time and determine how to allocate resources more efficiently to serve and retain those customers.
The simplest formula for calculating customer lifetime value is: CLV = customer value (average purchase value x average purchase frequency) x average customer lifespan.
First Response Time (FRT)
First Response Time (FRT) is a crucial metric for measuring customer experience, particularly in customer support and service environments. It represents the time it takes for a customer to receive an initial response after making a query or reporting an issue.
This CX metric matters because quick responses contribute to higher customer satisfaction. Customers appreciate the timely acknowledgment of their concerns, which demonstrates that the company values their time and is committed to addressing their needs promptly. First Response Time can even influence your company’s reputation: brands that are known for quick and efficient customer service are likely to be perceived positively, while a reputation for slow response times can harm your overall brand image.
Average Resolution Time
Average Resolution Time is a customer experience KPI that provides insights into customer service efficiency and directly impacts customer experience and satisfaction. By tracking this actionable CX metric, your company can set goals to reduce the time it takes to resolve issues, while also continually improving support processes, leading to an enhanced customer experience over time.
When quick and efficient issue resolution contributes to a positive overall experience, your company is better equipped to foster customer loyalty. Understanding how much time is typically spent on resolving customer issues can even help your company allocate resources appropriately. This can involve adjusting staffing levels, providing additional training, or implementing new technologies to improve efficiency.
CSAT vs NPS vs CES
There are always conversations around the three most popular customer experience KPIs: CSAT vs NPS vs CES. Which of these CX metrics are most suitable for your company? What are the advantages and limitations of each? These metrics differ in terms of the insights they provide and the areas of customer satisfaction they focus on.
CSAT captures satisfaction with specific interactions, NPS evaluates loyalty and advocacy, and CES assesses the ease of the customer experience. By utilizing a combination of these metrics, businesses can gain a more comprehensive understanding of customer satisfaction, identify areas for improvement, and develop strategies to enhance the overall customer experience.
Advantages
Net Promoter Score (NPS) is a useful customer experience KPI because it’s an easy-to-calculate metric that provides actionable insights. Your team can follow up with detractors to understand the reasons for their dissatisfaction and take corrective action. NPS also allows you to benchmark your score against competitors and industry standards, providing context for your overall performance.
Meanwhile, Customer Satisfaction Score (CSAT) surveys are relatively easy to administer and understand. They often use a simple scale and provide immediate feedback, with some surveys being conducted right after a specific customer interaction or transaction, providing real-time feedback.
Advocates of Customer Effort Score (CES) often highlight the ease with which they can gather actionable insights from responses. CES results often offer specific areas for improvement, helping companies identify and address high-impact pain points in the customer journey.
Limitations
For larger organizations, NPS on its own may be too simplistic of a metric. It helps you understand that customers have had a positive or negative experience, but not necessarily why that’s the case. NPS works best if paired with other customer experience metrics, or when you utilize a secondary follow-up question to investigate the customer experience in greater detail.
CSAT, meanwhile, may not provide the most comprehensive view of the overall customer experience since it focuses on satisfaction at a particular point in time. The interpretation of satisfaction scores can also vary between individual customers, and what one considers satisfactory might not be the same for another.
The same goes with CES: it may not necessarily capture the entire customer experience and may focus more on transactional aspects. Interpreting CES scores on their own also often requires a broader understanding of the context of the customer experience, and a low score doesn’t always indicate a systemic issue.
Should You Measure All the CX Metrics?
Given the advantages and limitations of the customer experience KPIs listed above, should you measure all these CX metrics?
The short answer is: no. While tracking and measuring as many CX metrics as possible can be helpful in understanding and improving your performance, it’s not always necessary or practical to monitor every possible KPI or metric. The choice of metrics is not even as important as you might think, since driving improvement is what’s important.
With that in mind, choose metrics based on what you’ll be able to do with the data they provide. Are the results going to enable you to take action? If not, don’t spend time on them. The selection of specific metrics should align with your business goals, industry, and the nature of your customer interactions. It’s also important to regularly reassess your customer experience KPIs and metrics to ensure they remain relevant as your business evolves.
3 Most Common CX Metrics Questions
Customer experience (CX) metrics are a CX program’s bread and butter. NPS, CSAT, and CES have historically been the main tools every program utilizes to have a systematic way of establishing a voice of customer (VoC) source and leveraging those findings to improve customer experiences. But it’s not easy—a CX metric score alone can’t create transformation.
CX metrics aren’t one-size-fits-all. Certain CX metrics are more fitting for specific industries—and even then your brand might not need to use the same metrics as your direct competitors. There’s no one golden method for measuring CX metrics, which is actually why many businesses struggle to create a success framework.
What CX Metrics Can You Use to Determine Industry and Organizational Maturity?
Before you can answer this question for your company, there are three things you should answer first:
How are you measuring the customer experience? Is it through satisfaction or NPS, or is it another metric that aligns with the goals you have?
What are you doing with the metric? How are the metrics of field services, retail, call center, first-contact resolution, etc. used?
From an employee perspective, are you doing something beyond a basic employee engagement study? Or do you have an employee pulse metric by division, region, or queue?
There’s no one-size-fits-all metric that determines maturity? Instead, focus on where your company is toward your specific goals. Success is determined by how close you are to achieving that goal, instead of a set of objective metrics that may not even relate to your business. Truly mature organizations are aligned on specific business goals and have customer experience KPIs directly attached to those individual goals. They frequently check in on those metrics and take action to move the needle and tie that success back to their experience programs.
How Can You Tie Metrics Directly to CX and VoC Programs Versus Other Internal and External Factors?
The important thing is to look at your organization and how they talk about success as well as learning to speak that language. Are you a finance, operations, or retention-focused organization? And how are you integrating operational, technical, and financial data with customer survey data?
Organizations that are technically or engineering-focused often look for an extreme amount of precision. But survey data doesn’t always lead to one answer or the answer you expect. The real question is, “How do you pull information together and communicate that collectively?” As much as the mathematical connections are crucial, so are the practical ones.
Ultimately, metrics can be tied either statistically or practically, but the latter is much more realistic for a business. For example, the broader benefits when enabling an entire organization are hard to quantify but there could be specific benefits your program has contributed to making a project more successful. Maybe the insights your program provided can take accountability for 10% of the project’s effects. Then you can say, “It wasn’t all from our VoC or CX program but we get credit for that 10%.”
How to Analyze Which Attributes Influence NPS in Survey Analysis?
Let’s say your organization’s leadership is focusing in on NPS—where you are, what drives it, and so on. So, you try structural equation modeling, key driver analysis, or heavy-duty analytics. That approach is equatable to killing a mosquito with a sledgehammer. Instead, you want to break methodology down to core factors—using statistical analysis or text analytics—to see what themes come out and categorize them according to where your organization is.
Now, at an executive level, you might not want to communicate the “R-squared” of the modeling. Usually, executives just want answers to the questions, “What’s driving NPS?” and “What should I do with it?” Your job is to articulate the answers in a clear and simple way throughout the organization. That will drive your success from the top down. But of course, you should still have in-depth analyses prepared in your back pocket if you encounter someone who is statistically oriented.
How Can I Use KPIs to Improve Customer Experience?
Improving customer experience starts with tracking your current CX metrics, listening to customers, and analyzing data for insights that will be essential to forming an action plan. Keep in mind that this is a cycle that your organization has to do consistently and regularly.
Instead of sending out feedback or customer satisfaction surveys only once or twice a year, you may consider investing in a customer experience management software platform that enables your organization to achieve always-on listening. This means that you’re able to capture CX data and feedback from various touchpoints and channels, such as social media, website and mobile app analytics, call center transcripts, and online reviews, as well as from targeted surveys that deliver results in real time.
After you take these actions, you measure the response to your improvements and determine the success of your efforts. And once you’ve completed the cycle, you must do it all over again.
Tracked and managed the right way, the customer experience KPIs and metrics listed above should help support your organization’s commitment to putting customers first. They’re useful for collecting valuable sentiment data, generating actionable insights, and predicting future behavior. Most importantly, these metrics enable your organization to take the guesswork out of your strategy, accurately measure customer experience, and inspire more moments of customer delight.
Your Next Steps: Taking Action
Customer experience involves multiple touchpoints and interactions across various channels and stages of the customer journey. Capturing and measuring the entire, multi-faceted experience requires a comprehensive approach that takes into account diverse customer interactions.
This is where InMoment comes in. Combining award-winning technology with expert services in customer experience measurement and management, InMoment helps today’s top brands deploy programs that are designed to measure customer experience KPIs and metrics in ways that are simple, speedy, and scalable.
References
SuperOffice. 32 Customer Experience Statistics You Need to Know for 2024 (https://www.superoffice.com/blog/customer-experience-statistics/). Access 12/1/2023.
Forbes. 50 Stats That Prove The Value Of Customer Experience (https://www.forbes.com/sites/blakemorgan/2019/09/24/50-stats-that-prove-the-value-of-customer-experience/). Access 12/1/2023.
Harvard Business Review. Stop Trying to Delight Your Customers (https://hbr.org/2010/07/stop-trying-to-delight-your-customers). Access 11/30/2023.
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These days, understanding your customers isn’t a “nice thing to do”; it’s an absolute necessity. To truly understand your customers, you need to spend some quality time listening to them and understanding the voice of the customer. That’s why mastering the art of the voice of the customer survey can be a game-changer for any business seeking to better understand its customers. The power of listening to your customers transcends beyond just collecting feedback; it serves as a strategic compass, guiding your decision-making, shaping your product development, and, most importantly, building strong, lasting customer relationships.
Read on to learn more about voice of the customer surveys, why they’re so important, how to create them, and some sample questions that can get you started creating your VoC survey today.
What Is a Voice of the Customer Survey?
At its core, a voice of the customer (VoC) surveys captures customers’ expectations, preferences, and aversions toward products, services, or your company in general. A VoC survey involves gathering both quantitative and qualitative feedback from customers about their various touchpoints with a company. Touchpoints could be anything from an interaction with your website, chatting with your customer service representatives, or actually using your products and services. By exploring customers’ experiences with these touchpoints, you’ll gain a holistic understanding of your customer’s journey and experience with your company.
Why Are Voice of the Customer Surveys So Important?
A VoC is more than just another survey to worry about. The benefits of VoC surveys extend far beyond mere data collection. They provide invaluable insights that can shape product development, fine-tune marketing strategies, and enhance customer service. Hopefully with all of these pieces in place, you’ll experience improved customer satisfaction and loyalty. Thus, the power of VoC surveys lies not just in listening to what customers have to say but in using those insights to create a better, more personalized customer experience.
Let’s dive into three specific benefits from utilizing VoC surveys as the powerful they are:
Understanding Customer Needs and Wants
At the heart of any successful business strategy lies a deep understanding of customer needs and wants. VoC surveys are literally the voice of your customers. They serve as a way to pull out real and salient insights into your customers’ needs and wants. These insights are the pulse of the market, reflecting real-time customer sentiment and demand. By tuning into the voice of the customer, you can identify what truly matters to your customers and adjust your strategies, products, and services appropriately. Your customers love it too.. VOC surveys provide customers with the opportunity to communicate their needs and wants directly to businesses.
Improving Customer Satisfaction
You created your business to solve a problem, and you want to make sure your products and services are actually doing that. The way to see if you’re reaching your customers the way you want is to evaluate your customer satisfaction. A VoC survey gives you the chance to do just that. Plus using these surveys can also help you boost your customer satisfaction on its own. Customers love the opportunity to be heard and understood, so when you’re actively working toward that, they’re going to notice and appreciate that.
Improving the Business Overall
VoC surveys are not just about improving customer experience; they offer significant benefits for the overall health and growth of the business. These surveys help businesses pinpoint potential issues from the customer’s perspective, allowing them to proactively address these concerns before they escalate and cause significant damage. Whatever customers aren’t liking, with a VoC, you have a chance to stop that in its tracks. This proactive approach not only improves the customer experience but also strengthens the company’s reputation, enhances operational efficiency, and drives overall business success.
Designing Your Voice of the Customer Survey
Voice of the customer surveys are powerful. How do you go about creating your own? Let’s dive into some of the basic steps for designing a highly effective VoC survey.
Define Objectives
Before crafting your VoC survey, it’s crucial to define clear, specific objectives. What are you trying to do or understand? Are you looking for insights on a specific product or service you want to refine? Or are you looking to improve your customer satisfaction overall? Knowing what your objectives are will help you design a survey that gathers data to help with your goal. You can ensure the data you collect is actionable and relevant to your overall business goals.
Choose the Right Types of Questions
Once you have clear objectives, you need questions that achieve your overall goals. The choice of questions in your VoC survey can significantly impact the quality and type of feedback you receive. To gain a comprehensive understanding of your customers’ experiences, it’s advisable to use a mix of multiple choice questions, scales (like the Likert scale), and open-ended questions. Multiple choice questions and scales are excellent for collecting quantitative data, offering clear, easily analyzable feedback. On the other hand, open-ended questions allow customers to express their opinions and experiences in their own words, providing rich qualitative data that can offer deeper, nuanced insights. A mix of question types will give you deeper insights overall.
Keep It Simple
While it’s important to gather as much valuable feedback as possible, your customers won’t complete a long survey. It’s much better to have fewer questions and more complete surveys than the other way around. Aim to keep it simple and keep your surveys no longer than 10 minutes. The simpler and more streamlined your survey, the more likely customers are to complete it and provide honest, thoughtful responses.
Start Broad, Then Get Specific
When structuring your VoC survey, a useful approach is to start with general questions before delving into more specific ones. Starting broad helps your customers “warm up” to providing you with feedback. Broad, initial questions can pertain to overall satisfaction, general experiences, or perceptions of your brand. Essentially, your early questions should be easy to answer without too much extensive thought. You can narrow as you go to get more detailed feedback.
Questions to Ask in a Voice of the Customer Survey
Those strategies can help you get started. To really take your VoC survey to the next level, we have some starter questions to help you write your own voice of the customer surveys. The questions we provide are broken up into general categories that you may want to consider on your surveys.
Value-Based VoC Questions
Did you find everything you were looking for today?
Is there anything you were looking for that we didn’t have?
On a scale of 1-10, how would you rate the value of your purchase?
What are the most important qualities you look for in a product or service? (This question is particularly poignant as a multiple choice question)
Did our customer service help you resolve any issue you came across?
Brand Loyalty VoC Questions
How likely are you to recommend our brand to a friend or colleague on a scale of 1–10?
When thinking about our brand, product, or service, what is the first thing that comes to mind?
What might prevent you from doing business with us in the future?
How likely are you to switch to a different brand, product, or service?
Customer Satisfaction Questions
How would you describe your experience with us today?
How satisfied are you with the product or service you received?
Was your customer service agent able to handle any issue you had?
What could we have done to improve your experience?
Final Thoughts
Overall, VoC surveys are powerful tools to better understand your customers and how they really perceive your company. Utilizing these surveys, you can further refine your products and services, enhance your customer satisfaction, and better meet the needs of your customers.
Learn more about how a voice of the customer survey can help you build a better brand with InMoment today!
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Customer experience (CX) leaders from utilities brands are facing unprecedented challenges in 2022. Increased government regulation and new market entrants with unique service-based offerings are creating a disruptive wave of change that traditional utilities need to respond to. But here at InMoment, we don’t like to merely dwell on obstacles and complexities. We like to provide you with strategies and solutions.
With that being said, Graham Tutton, InMoment’s Global Head of Consumer Products, has put some thoughts together around some of the biggest challenges facing the utilities sector, and what customer experience leaders can do about these for our latest webinar. And to save you some time, we’ve taken those and compiled them into this quick article.
Let’s dive in!
Challenge #1: Disparate Data
Utilities companies typically have a lot of data spread across multiple silos across the business. The challenge is combining the operational, technical, financial, and even the metadata (like weather data) that is currently sitting in legacy systems or different departments, and is also aggregated with feedback data. Additionally, brands have not figured out how to tap into 85% of data—the unstructured kind—so they miss out on the bigger picture.
Solve the Challenge: Combine Data Sources
Many CX leaders in this space find it challenging to stitch together holistic customer feedback in one place, and know how to take action from it. At the end of the day, you need a single platform that can combine direct survey data from customers, but also indirect data (like social media reviews), and inferred data (like contact centre chat logs).
Challenge 2: Figuring Out Customer Trends
Because data is spread across the organisation, making sense of emerging customer trends is even harder. Businesses want to make the best decisions based on the available information. However, these decisions are often flawed because businesses do not have the ability to understand the data they’re looking at. Businesses cling to the easy insights floating at the top of their datasets, but often miss the deeper insights hidden behind unstructured data.
According to IDC, 85% of enterprise data is unstructured and is growing at a rate of 55% every year. With this rate of growth, businesses that fail to adapt miss out on the bigger picture and are making flawed decisions based on only a small percentage of the data available.
Solve the Challenge: Text Analytics to the Rescue
Luckily, text analytics capabilities are getting better and better each year! Businesses should leverage human-led, knowledge-based taxonomies by finding a partner that offers high accuracy and actionability, offering economies of scale from a wealth of knowledge gained in your industry, language and use case.
Challenge #3: Taking Action on Feedback
Some utilities brands find it tricky to know which actions to take after analysing their customer experience data. There are many reasons for this—most customer experience solutions require multi-language translation, human interpretation and maintenance, and continuous tuning of surveys. To make matters worse, because the process is so slow, the accuracy of the insights are impacted too. CX leaders are often stuck in the cycle of wading through data and less enabled to actually take action on it.
Solve the Challenge: Have a Roadmap From the Beginning
If you build your CX program around a roadmap (with clear checkpoints, of course), it will help you stay focused on your ultimate goals. You should be checking in with your roadmap monthly, and evaluating actions against the checkpoints every quarter. By constantly referring back to the original plan, it will help build your organisational culture around the customer, and this will definitely help with momentum of your program, taking you further than you could possibly go if you were shouldering the weight of the CX program alone.
To learn more, check out Graham’s full CX webinar designed just for utilities brands.
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It is a fact that CX survey response rates have been declining. Additionally, we are being surveyed more and more every day about every mundane thing in our lives. Even the federal government is in on it—an executive order in 1993 directed federal agencies to gather public feedback on how well they delivered services and to strive to offer a comparable level of customer experience with private companies. Orders similar to that one have continued into the present day.
But, with surveys being the lifeblood of nearly all customer experience (CX) programs, what is a CX practitioner to do to improve their CX survey response rates? Much has been written about the tactical things a survey owner can do: list hygiene, fatigue or quarantine rules, visual appeal of the invitation, subject line, formatting, time estimates in the invitation, etc. And while these elements can have some impact, they are temporary band-aids for the over-surveying problem.
The Secret to Improving CX Survey Response Rates Is…
I’ll let you in on a secret: if you truly want to improve and sustain your response rates, look to your CX program (specifically your closed loop processes). There are two critical things any company can do to improve its response rates, and they tie back to the inner and outer loop concepts described in the Net Promoter SystemSM.
You’ve probably heard that it’s vital for organizations to close these loops, as doing so can help you achieve everything from Experience Improvement (XI) to enhanced customer retention and sustained business growth. That’s true! But effectively closing these loops also provides an incentive and opens a door for continuous feedback from your customers or members.
The Inner Loop
The inner loop refers to the systems, processes, and teams that organizations use to respond to customers one-on-one to address negative feedback. Having an effective inner closed loop process is of obvious importance to any company that wants to keep its doors open, let alone create a differentiated and meaningful experience for customers. Fail to close the inner loop, and you open the “leaky bucket.”
However, if you can build a system that allows you to receive customer feedback, analyze it for actionable insights, and respond both meaningfully and expediently, you’ll have a much easier time retaining customers and extending their lifetime value. You will learn more about their individual preferences and may even potentially cross-sell or upsell them to additional products and services.
There is also plenty of research that demonstrates that customers whose complaints have been successfully resolved tend to leave higher review scores than customers who never had a complaint in the first place! Finally, by responding to customers when they have complaints, you demonstrate that you have listened and acted on their feedback, giving them a strong incentive to provide feedback again in the future.
The Outer Loop
The scope of the outer loop is considerably wider than that of the inner loop and requires more organizational resources, cross-silo cooperation, and team coordination. Rather than focus on individual customer interactions and complaint resolution, the outer loop is about the actions your organization takes on the collective feedback you’re receiving to drive Experience Improvement and communicate those improvements back to a much broader segment of customers (if not your entire customer base). The one-on-one interactions that comprise the inner loop are certainly important, but the outer loop is all about incorporating those into a cumulative group effort to drive sustained Experience Improvement.
This improves your CX survey response rates by demonstrating to all customers that your organization truly does care about feedback and attempts to take action to improve the overall customer experience. This provides a feedback incentive even for customers who may not have shared it in the past, as they see the direct benefit.
Widening Focus
Click hereto read my full-length Point of View on how focusing on your CX program will actually help you achieve better outcomes. In the meantime, take advantage of anything you might have learned here to meaningfully improve your inner and outer loop processes. I promise you you’ll see a difference.
Recently, a client asked me what we at InMoment thought defines a “customer interaction,” as there had been some debate on the subject within his team. I pondered the subject and brought it back to my colleagues. Quickly, we were asking ourselves not only about the characteristics of an interaction, but beyond that, what falls under the larger umbrella of customer experience? Is there a difference? Today, we’ll be diving deeper into these questions.
What Is a Customer Interaction?
Webster’s defines “interaction” as:
Mutual or reciprocal action or influence
To act upon one another
From this definition, we see clearly that two or more parties are required for an interaction; for example, a company or brand and a prospect or customer.
What Is a Customer Experience?
Harley Manning, VP, Research Director at Forrester, once defined customer experience as: How customers perceive their interactions with your company. He went on to define an interaction as when you and your customers have a two-way exchange.1
Neither Here, Nor There
So what does that mean when a prospect or customer browses your website but does not make a purchase? Or a customer clicks a link in your brand’s email, but does not go any further? According to the definitions above, those are not interactions. But there are a lot of people in companies working very hard to get these actions to happen (click through rate and time on website/app are very common marketing and ecommerce metrics).
If they are not interactions, what are they? I would classify them as engagements. A customer has engaged with your brand, but there was no interaction, because it was only unilateral. Thus, not all engagements are interactions.
And here is where it gets interesting. If the examples listed above are not interactions, but engagements, are they considered part of your customer experience? You better believe it.
The Intersection Between Customer Engagements and Customer Experiences
Customer experience is generally held to be the sum of all interactions someone has with your brand and the resulting feelings they have about your brand. But are experiences limited to interactions or engagements? Do customers have to interact with your people, products, services, or digital properties for their engagement to fall under customer experience?
Today, a company’s policies regarding diversity and inclusion, for example, or the politics, causes, and charities they choose to support have an impact on people’s feelings about the brand. I would argue that these are part of the customer experience as well. There are prospects out there that will choose to never do business with your company based on these issues and other customers who become more loyal for the same reasons.
Returning to the Question
To return to the original question, I would like to suggest that customer interactions and customer experience are concentric circles. An interaction is a subset of engagement, which in turn is a subset of experience.
And companies have to be attentive to all of the ways customers experience their brands, products, and services. Whether or not an engagement ever advances to the level of interaction is an integral piece of the CX puzzle.
Want to hear more from Eric about customer interactions, engagements, and experiences? Stay tuned for the next post in the series!
There’s a problem with how many businesses view customer experience (CX) data: human beings cannot (and should not) be distilled down to numbers. For many years, experience programs have hailed numbers as a sort of holy grail, but the reality is that numbers are no substitute for genuine human connection.
None of this is to say that metrics aren’t important, but companies should remember that they can only reveal so much about why customers may be experiencing an issue or even why they remain loyal to the brand. With that in mind, we’re going to dive into a few things to bear in mind while creating more human and more connective customer relationships!
Numbers Alone Can’t Tell a Story
Before we get into how to humanize and improve customer experiences, we first need to understand why structured data can’t give us all the answers. For instance, it’s common to send out Net Promoter Score (NPS), Customer Satisfaction (CSAT/OSAT), or Customer Effort Score (CES) surveys after a customer interacts with a brand, but what do these scores actually tell us? A higher ease-of-use score, for example, doesn’t necessarily mean you made the customer happier or that you improved that customer relationship. You can speculate about numbers, but they don’t reveal the exact, organic reason why customers feel one way or another.
So, how can companies compensate for this lack of context? The answer lies in unstructured data and the Experience Improvement (XI) solutions that can turn it into actionable intelligence. That actionable intelligence, in turn, gives brands the chance to create a more organic, more connective, and more human customer experience.
How to Humanize and Improve Customer Experiences
Only when a business listens to human feedback can it respond with a more human customer experience. This means tapping into the voice of the customer by allowing customers to express feedback in their own words.
Consider platforms like Instagram, Yelp, and YouTube. People can use these platforms to freely (and frankly) express themselves in a way that numbers cannot allow. The result is a form of unstructured feedback that your brand can not only use to trace the root causes of experience breakages, but also to empathize with your customers.
After accumulating enough unstructured data, the next step is to analyze and act on what you’ve learned. However, that’s easier said than done, especially if your CX resources are limited. That’s why it’s important to desilo data and share customer intelligence with your entire company. Then, you can get multiple departments to collaborate and act on their role in humanizing the customer experience (this approach also creates a single, holistic view of the customer for your organization).
If your brand can offer experiences that are far more human, that’s far more valuable than achieving any high metric score. And it goes hand in hand with customer loyalty. When a customer feels empathized with and known as a person, that customer will return to your brand—even if there’s a lot of competition—because their relationship with you has transcended mere transactions. This is the heart of Experience Improvement—answering customers’ search for meaning while strengthening both your bottom line and your marketplace leadership!
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Since the inception of customer experience (CX), the conversation about feedback and listening tools has largely revolved around data collection. Many brands have emphasized turning listening programs on immediately, gathering feedback from everyone, and using that feedback to inform both metrics and strictly reactive experience management.
Is there not a deeper layer to experience, though? Top-tier analyst firms like Forrester certainly seem to think so. That conversation about gathering feedback, about experience management, is being taken a step further to a new paradigm: Experience Improvement (XI).
Rather than being about reactive management and just watching metrics like NPS, experience improvement encourages brands to amp things up by creating meaningful, emotionally connective experiences for each and every customer. What follows are five steps to getting your program to that level.
Five Steps to Improve Experiences
Design
Listen
Understand
Transform
Realize
Step #1: Design
Until now, most experience program frameworks encourage brands to turn listening posts on immediately and use gathered feedback to shape eventual goals. However, with experience improvement, this model is inverted to great effect. Rather than getting feedback first, forming goals later, brands should carefully think about what objectives they want their program to accomplish and design their listening efforts around those goals.
For example, does your brand want to reduce customer churn by a given percentage? What about increasing retention or acquisition? Whatever your company’s goal, your experience program can help you get much further toward it if you spell out concrete, numbers-driven goals before turning any listening posts on. Frankly, some audiences are also more worth listening to than others, and completing this step can help your brand better decide where to tune in and why.
Step #2: Listen
Once you’ve established your experience program’s goals and audiences, you can then turn your aforementioned listening posts on. Having determined which audiences to listen to before doing so can help your brand consolidate experience program resources toward much more helpful groups. For example, if you’re looking to boost customer retention, it makes more sense to focus on your established customer base than anyone who interacts with your brand in any context. This approach saves your brand time and resources hunting down helpful intel.
Step #3: Understand
After gathering more focused, relevant feedback through your program, take time to carefully digest it and sort out what might need improvement. An experience platform armed with capabilities like sentiment analysis can be a huge help here. Additionally, it bears repeating that understanding your feedback means more than scoreboard-watching NPS—it means diving deep into customer feedback to understand common themes, praises, problems, and possible solutions.
Step #4: Transform
Understanding your customer feedback is one thing; using it to meaningfully transform the business is another. This is arguably the most work-intensive step of the experience improvement framework… and one of the most important. Meaningful transformation means sharing CX intelligence with leaders across the business (especially in the departments most relevant to the feedback) and working closely with them to outline and implement process improvements. Desiloing data is always a good idea because it gives employees a holistic view of the brand’s purpose.
Step #5: Realize
Realizing experience improvement means circling back to the goals you set forth in the design stage to ascertain how things shook out. Did you meet your program numbers? Perhaps more importantly, have the improvements implemented as a result of your program resulted in positive cultural changes? Having an initial goal to compare your outcome to is vital to realizing experience improvement… and simplifies proving ROI to request more resources for additional efforts.
By following these steps, organizations can transcend managing experiences and start meaningfully improving them. As we mentioned up top, Experience Improvement leads to the sorts of deeply connective experiences that keep customers coming back no matter what, leading to fundamental brand success.
To read more about these five steps—and brands who have found success with them—check out this article for free today!
Unlock Expert Guidance on Today’s CX Challenges & Opportunities
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Do you know what holiday shoppers need from you this season? What about what matters most to them in store? Or what is most important to them when it comes to their experience on your website?
Do you know what holiday shoppers need from you this season? What about what matters most to them in store? Or what is most important to them when it comes to their experience on your website?
If you’re unsure, don’t simply guess what your customers are looking for. Instead, check out this infographic we created based on a study we did that surveyed 5,000 North American customers about their expectations for the holiday shopping season!
ROI has been a notoriously fickle element of experience programs for years—but it doesn’t have to be. In fact, the difficulty of proving ROI stems less from experience programs being a financially elusive unicorn than many companies not tying their program to a quantifiable objective.
These days, it’s not uncommon for brands to take the term “listening program” to mean a series of listening posts set up across multiple channels.
Yes, those posts are an important part of listening, but experience programs can be so much more (and do so much more for your business). They can go far beyond listening in across channels and reacting to customer comments only as they come in.
Listening for, reacting to, and measuring customer sentiment in this manner is what’s commonly known as experience management. And honestly, it rarely moves the needle for brands or creates a better experience for customers. Experience improvement (XI), by contrast, allows companies to achieve both of those goals by connecting to customers in a very human way. Essentially, it pays for brands to have an experience improvement success framework.
Today, we’re going to touch on three ways a success framework can add unbridled power to any improvement effort:
Proving ROI
Listening Purposefully
Owning The Moments That Matter
Key #1: Proving ROI
ROI has been a notoriously fickle element of experience programs for years—but it doesn’t have to be. In fact, the difficulty of proving ROI stems less from experience programs being a financially elusive unicorn than many companies not tying their program to a quantifiable objective.
This is why it is crucial that brands establish hard, specific goals for their experience program. An objective like “be more customer-centric” isn’t going to cut it, especially when it comes to proving ROI. Rather, experience practitioners and stakeholders need to work together to hash out program objectives that can be tied to financial goals.
Whether it’s acquiring X amount of new customers or lowering cost to serve by Y percent, creating goals like these and gearing your program toward them will make establishing ROI much, much easier.
Key #2: Listening Purposefully
ROI isn’t the only area a success framework can help companies stencil in. This setup can also help brands better identify who to listen to and why.
Conventional wisdom holds that companies should listen for feedback from anyone, but that isn’t necessarily true. Callous as it may sound to some, the truth is that some audiences are just more worth listening to than others. A success framework can help companies identify which audiences they need to listen to to achieve program goals.
This approach is also handy for cutting through the mountains and mountains of data that experience programs inevitably rake in. They also help programs get to the heart of providing a great experience, which leads us to our final topic:
Key #3: Owning The Moments That Matter
The moments that matter are the instances in which the needs of customers, employees, and businesses all connect. They’re the moments in which a customer journey transcends a transaction and becomes a profound emotional connection. Owning the moments that matter is vital to creating connections and inspiring transformational success across your business.
This final key is a culmination of establishing financial goals, listening purposefully, and taking action—ultimately creating meaning for customers. That capacity to create meaning is what sets the best brands apart from the competition and carries them to the top of their verticals. And it all starts with building an experience improvement success framework.
Click here to learn more about how to create a success framework and why doing so at the very start of your experience improvement journey will guarantee success for you, your customers, and your employees.
Comment-based surveys can be effective for immediately gathering feedback from customers. However, there are several arenas in which brands use comment-based surveys when another survey type would yield better intelligence.
Comment-based surveys can be effective for immediately gathering feedback from customers. And when it comes to customer experience (CX), timeliness can make or break an organization’s ability to act on that feedback.
However, there are several arenas in which brands use comment-based surveys when another survey type would yield better intelligence. Today, I’d like to dive into several shortcomings that can make using comment-based surveys challenging for brands, as well as a few potential solutions for those challenges. Let’s get started.
Outlet-Level Analysis
As I discussed in my recent article on this subject, comment-based surveys are often less effective than other survey types for conducting outlet-level analysis. In other words, while brands can see how well stores, bank branches, and the like are performing generally, they usually can’t determine where individual outlets need to improve .
The reason for this has as much to do with the feedback customers leave as the survey design itself. From what I’ve seen across decades of research, customers rarely discuss more than 1-2 topics in their comments. Yes, customers may touch upon many topics as a group, but rarely are most or even a lot of those topics covered by singular comments.
What all of this ultimately means for brands using comment-based surveys to gauge outlet effectiveness is that the feedback they receive is almost always spread thin. The intelligence customers submit via this route can potentially cover many performance categories, but there’s usually not that much depth to it, making it difficult for brands to identify the deep-rooted problems or process breakages that they need to address at the unit level if they want to improve experiences.
(Un)helpful Feedback
Another reason that brands can only glean so much from comment-based surveys at the outlet level is that, much of the time, customers only provide superficial comments like:“good job”, “it was terrible”, and the immortally useless “no comment.” In other words, comment-based surveys can be where specificity goes to die.
Obviously, there’s not a whole lot that the team(s) running a brand’s experience improvement program can do with information that vague. Comments like these contain no helpful observations about what went right (or wrong) with the experience that the customer is referring to. The only solution to this problem is for brands to be more direct with their surveys and ask for feedback on one process or another directly.
How to Improve Comment-Based Surveys
These shortcomings are among the biggest reasons brands should be careful about trying to use comment-based surveys to diagnose processes, identify employee coaching opportunities, and seeing how well outlets are adhering to organization-wide policies and procedures. However, none of this means that comment-based surveys should be abandoned. In fact, there’s a solution to these surveys’ relative lack of specificity.
Brands can encourage their customers to provide better intelligence via multimedia feedback. Options like video and image feedback enable customers to express themselves in their own terms while also giving organizations much more to work with than comment-based surveys can typically yield. Multimedia feedback can thus better allow brands to see how their regional outlets are performing, diagnose processes, and provide a meaningfully improved experience for their customers.
Click here to read my Point of View article on comment-based surveys. I take a deeper dive into when they’re effective, when they’re not, and how to use them to achieve transformational success.
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