In an increasingly data-driven world, it is no longer enough to understand the past. Successful businesses utilize prescriptive analytics to aid in data-driven decisions that will improve their bottom line. Whether it’s reducing your cost to acquire a customer, decreasing your churn rate, or anything in between. Your business can use analytics to drive success and improve customer experiences. 

What is Prescriptive Analytics?

Prescriptive analytics is a type of advanced analytics that focuses on providing recommendations and insights to optimize decision-making. Prescriptive analytics uses a combination of mathematical models, algorithms, and business rules to analyze data and generate recommendations. It takes into account various factors, constraints, and objectives to suggest the best course of action in a given situation. This type of analytics is particularly valuable in complex and dynamic environments where decision-makers need guidance on how to respond to different scenarios.

What is the Primary Goal of Prescriptive Analytics?

The primary goal of prescriptive analytics is to provide actionable recommendations that help organizations or individuals make informed decisions to achieve desired outcomes. Unlike descriptive analytics, which focuses on summarizing historical data, and predictive analytics, which forecasts future events, prescriptive analytics takes it a step further by suggesting the best course of action.

With InMoment’s award-winning XI Platform, businesses have access to comprehensive analytics and prescriptive actions represented in customizable dashboards. These tools allow your business to digest analytical insights in the way that makes the most sense for your company. 

How Does Prescriptive Analytics Work?

Prescriptive analytics works by using advanced analytical techniques to recommend actions that optimize decision-making. It involves analyzing data, creating mathematical models, and considering various constraints and objectives to suggest the best course of action. 

Consider a retail company using prescriptive analytics to optimize its pricing strategy. The process would involve analyzing historical sales data, market trends, and external factors. Predictive analytics forecasts the demand for products under different price points. The optimization models factor in costs, competitor prices, and revenue objectives.

The prescriptive analytics system then recommends specific pricing adjustments for each product to maximize overall revenue while considering factors like customer demand elasticity and market conditions. The recommendations are not only based on historical data and predictions but also on the optimization of pricing strategies to achieve the desired financial outcomes for the company. Decision-makers can follow these recommendations to adjust prices and potentially improve the company’s profitability.

How Does Prescriptive Analytics Differ From Other Types of Analytics?

When it comes to data analytics, organizations leverage various approaches to extract meaningful insights and guide decision-making. Each type of analytics serves a distinct purpose, contributing to a comprehensive understanding of data. There are four key types of analytics: prescriptive, predictive, diagnostic, and descriptive. 

Predictive vs Prescriptive Analytics

While predictive analytics is instrumental in understanding likely future scenarios, prescriptive analytics takes it a step further by providing actionable insights that empower organizations to make optimal decisions and drive positive outcomes. Both methodologies, when used in tandem, contribute to a comprehensive and strategic approach to data-driven decision-making. Here are some key differences between the two types of analytics:

• Predictive analytics focuses on forecasting future events, while prescriptive analytics recommends actions to influence or achieve desired outcomes.
• Predictive analytics is more concerned with “what will happen,” while prescriptive analytics is concerned with “what to do about it.”
• Predictive analytics helps organizations prepare for the future, while prescriptive analytics guides decision-makers in shaping the future.

Diagnostic Analytics vs Prescriptive Analytics

Diagnostic analytics and prescriptive analytics are two distinct approaches within the realm of data analytics, each serving different purposes in the decision-making process. Diagnostic analytics is focused on understanding the reasons behind past events or performance.  Diagnostic analytics helps organizations understand the factors contributing to past outcomes, prescriptive analytics empowers decision-makers with actionable recommendations to shape future events. Here is a breakdown of some differences between these two types of analytics:

• Diagnostic analytics looks backward to understand past events, while prescriptive analytics looks forward to recommending actions for future outcomes.
• Diagnostic analytics is retrospective and focused on root cause analysis, while prescriptive analytics is proactive and provides actionable insights for decision-making.
• Diagnostic analytics helps uncover the “why” behind events, while prescriptive analytics guides decision-makers on “what to do” to achieve desired results.

As an example, consider an e-commerce platform employing diagnostic analytics to investigate a sudden drop in website traffic, examining factors such as marketing campaigns, website usability, and external events.

Descriptive and Prescriptive Analytics 

Descriptive analytics is centered around summarizing historical data to provide a comprehensive understanding of past events and trends. It seeks to answer the question, “What has happened?” by using data aggregation, visualization, and statistical analysis to present a clear and concise overview of historical performance. Prescriptive analytics takes a more proactive stance by answering the question, “What should be done?” Integrating both approaches offers a comprehensive strategy for effective decision-making.

Consider a retail company that uses descriptive analytics to review sales reports from the previous quarter, visualizing key metrics such as total sales, product categories, and customer demographics. By integrating prescriptive analytics into the decision-making process, the retail company can move beyond analyzing past performance to actively shaping and optimizing its strategies for the future. 

Advantages of Prescriptive Analytics

Prescriptive analytics offers several compelling advantages that make it a valuable tool for organizations seeking to optimize decision-making. One key benefit is its ability to provide actionable insights. By leveraging optimization models and algorithms, prescriptive analytics goes beyond predicting outcomes and offers specific recommendations on the best course of action. This empowers decision-makers with a clear and informed path forward, leading to more effective and strategic choices.

Another advantage is adaptability. Prescriptive analytics systems often incorporate feedback loops, allowing them to continuously learn and refine their recommendations based on new data and changing conditions. This adaptability enhances the relevance and accuracy of the insights provided, particularly in dynamic and evolving environments.

Additionally, prescriptive analytics enables organizations to consider various factors and constraints simultaneously. This holistic approach ensures that decisions align with multiple objectives, leading to outcomes that are not only optimized for a specific goal but also take into account broader organizational priorities.

Disadvantages of Prescriptive Analytics

Despite its benefits, prescriptive analytics comes with certain challenges and disadvantages. One notable concern is the complexity of implementation. Building and maintaining prescriptive analytics models requires expertise in advanced analytics, mathematics, and algorithmic development. Organizations may face obstacles in terms of the technical skills needed, as well as the resources required for ongoing maintenance.

Another challenge is the reliance on data quality. Prescriptive analytics heavily depends on accurate and relevant data. If the input data is incomplete, inaccurate, or biased, it can compromise the reliability of the recommendations. Ensuring data quality becomes a critical aspect of successful prescriptive analytics implementation.

Privacy and ethical considerations also come into play. Prescriptive analytics often involves processing sensitive information, raising concerns about data privacy and the ethical implications of decision-making based on algorithmic recommendations. Striking the right balance between data-driven insights and ethical considerations becomes essential in the deployment of prescriptive analytics.

While prescriptive analytics offers powerful advantages in guiding optimal decision-making, organizations must navigate the complexities of implementation, address data quality concerns, and carefully consider ethical implications to fully realize its potential benefits.

How Does Prescriptive Analytics Help in Business Decision-Making?

Prescriptive analytics plays a pivotal role in enhancing business decision-making by providing valuable insights and actionable recommendations. One key contribution lies in its ability to optimize choices. By leveraging sophisticated mathematical models and algorithms, prescriptive analytics evaluates multiple decision scenarios, considering various factors, constraints, and objectives. This process goes beyond predicting outcomes; it guides decision-makers on the most effective actions to take, ultimately leading to decisions that align with organizational goals.

Moreover, prescriptive analytics contributes to strategic planning and resource allocation. Organizations face numerous challenges in allocating resources efficiently, whether it’s managing production schedules, optimizing supply chain logistics, or determining marketing budgets. Prescriptive analytics analyzes complex datasets to offer recommendations that maximize efficiency, minimize costs, and improve overall resource utilization. This, in turn, enables businesses to allocate resources in a manner that yields the greatest return on investment.

Prescriptive analytics also facilitates risk management. By considering potential scenarios and their associated risks, decision-makers can make informed choices that mitigate uncertainties. Whether it’s in financial investments, supply chain disruptions, or market fluctuations, prescriptive analytics aids in identifying and addressing risks preemptively. This proactive approach empowers businesses to navigate volatile environments more effectively.

How to Get Started with Prescriptive Analytics

Implementing prescriptive analytics involves a strategic approach to harness its benefits effectively. The first step is to establish a clear understanding of organizational goals and challenges. Identify specific decision areas where prescriptive analytics can make a significant impact, such as resource allocation, supply chain optimization, or pricing strategies.

Once the focus areas are defined, the next step is to gather and prepare the necessary data. Data quality is paramount in prescriptive analytics, so organizations should ensure that they have access to accurate, relevant, and comprehensive data. Integration of data from various sources may be necessary, requiring robust data preprocessing to create a unified dataset suitable for analysis.

Collaboration between data scientists, domain experts, and decision-makers is essential throughout the process. Domain experts provide valuable insights into the contextual nuances of decision-making, while data scientists leverage their analytical expertise to build and refine prescriptive analytics models. A collaborative approach ensures that the models align with organizational objectives and are practical in real-world scenarios.

Pilot projects can be instrumental in validating the effectiveness of prescriptive analytics in a controlled environment. Start with a specific use case, implement the prescriptive analytics model, and assess the outcomes. The insights gained from the pilot project can inform adjustments and improvements before scaling up to broader applications.

Continuous monitoring and optimization are critical components of a successful prescriptive analytics initiative. Regularly review the performance of models, update them with new data, and refine algorithms based on feedback. This iterative approach ensures that the prescriptive analytics system remains adaptive and aligned with evolving business needs.

Utilize Prescriptive Analytics with InMoment

InMoment’s prescriptive analytics capabilities give your business the ability to transform raw data into strategic insights. These insights provide actionable recommendations that empower decision-makers to make informed choices and drive optimal outcomes. With the power of prescriptive analytics at your fingertips, you can not only understand historical performance but also proactively shape future strategies, enhance operational efficiency, and stay ahead of the competition in today’s dynamic business landscape. Schedule a demo to learn more today! 

The advent of conversational intelligence has become a catalyst for transformation in the way that businesses communicate with consumers. More than just a technological innovation, conversational intelligence represents a paradigm shift how organizations understand, interpret, and harness the power of human conversations. 

What is Conversation Intelligence

Conversation intelligence refers to the ability to understand, analyze, and optimize human conversations, particularly in the context of business interactions. It involves leveraging software such as natural language processing (NLP), machine learning, and artificial intelligence to gain insights from spoken or written conversations. The goal of conversation intelligence is to enhance communication, improve collaboration, and extract valuable information from interactions.

Why is Conversational Intelligence Important? 

One of the primary reasons conversation intelligence is crucial lies in its ability to enhance the customer experience. By deciphering the nuances of customer interactions, businesses can gain valuable insights into preferences, sentiments, and pain points. This information, in turn, empowers companies to tailor their products, services, and communication strategies to meet the evolving needs of their customer base.

Conversation intelligence also provides a data-driven foundation for decision-making. Analyzing conversations allows businesses to identify trends, evaluate employee performance, and make informed decisions that align with organizational goals. This data-driven approach fosters agility and adaptability, which is crucial in today’s fast-paced business environment.

How Does Conversation Intelligence Work?

Conversation intelligence works through the integration of advanced software, primarily natural language processing (NLP), machine learning (ML), and artificial intelligence (AI). Here is a breakdown of how conversation intelligence operates:

• Data Collection: Conversation intelligence platforms can work as part of an omnichannel contact center to gather data from various sources, including audio recordings of phone calls, video conferences, chat transcripts, emails, and other written communication channels.
• Preprocessing: Raw data often contains noise, irrelevant information, or inconsistencies. Preprocessing involves cleaning and organizing the data to ensure accuracy and reliability.
• Audio Transcription: Audio data is typically transcribed into text using speech-to-text technology. This step is crucial for analyzing spoken conversations.
• Understanding Language: NLP algorithms are employed to understand the structure and meaning of human language. This involves tasks such as tokenization (breaking text into words), syntactic analysis (parsing sentence structure), and semantic analysis (interpreting meaning).
• Analytics and Reporting: Conversation intelligence platforms can aid in contact center analytics and reporting features that summarize key metrics, trends, and insights derived from the analyzed conversations. This information is valuable for making data-driven decisions and optimizing business processes.

In essence, conversation intelligence involves a multi-step process of data collection, preprocessing, language understanding, sentiment analysis, machine learning, and real-time analytics. It leverages advanced technologies to extract meaningful insights from conversations, providing businesses with the tools to enhance customer experiences, improve communication strategies, and make informed decisions.

What’s the Difference Between Conversation Intelligence vs Call Tracking Software?

While conversation intelligence and call tracking software both deal with analyzing interactions, they serve different purposes. Call tracking software primarily focuses on monitoring and analyzing call metrics, such as call duration, source, and outcome. On the other hand, conversation intelligence encompasses a broader spectrum that includes the analysis of both spoken and written interactions. It incorporates advanced features such as sentiment analysis, data extraction, and real-time analytics to provide a comprehensive understanding of communication across various channels.

Benefits of Conversational Intelligence

The adoption of conversational intelligence has become a strategic imperative for businesses aiming to stay ahead in the digital era. This transformative technology not only revolutionizes the way organizations understand customer interactions but also brings forth a host of tangible benefits. From optimizing call center performance and nurturing agents to deriving valuable insights and enhancing productivity, conversational intelligence is reshaping the dynamics of customer service. Here is a deeper look into some of the benefits of conversational intelligence.

1. Optimizing Call Center Performance

Conversational intelligence proves to be a game-changer for call centers by providing real-time insights into customer interactions. Monitoring and analyzing calls help identify areas for improvement, ensuring that customer service representatives deliver a consistent and high-quality experience. This optimization leads to increased customer satisfaction and loyalty.

2. Agent Nurturing and Training

Conversational intelligence serves as a powerful tool for nurturing and training customer service agents. These platforms offer customer experience metrics such as response times, call resolution rates, customer satisfaction scores, and agent performance indicators. By analyzing successful interactions, managers can identify best practices and provide targeted coaching to enhance the skills of their teams. This fosters continuous improvement, boosts agent confidence, and contributes to a more positive and proficient workforce.

3. Extracting Valuable Insights

Extracting valuable insights from customer conversations is a key benefit of conversational intelligence. The platform’s ability to perform sentiment analysis, entity recognition, and data extraction enables businesses to understand customer preferences, pain points, and emerging trends. These insights are invaluable for making informed business decisions, refining strategies, and staying ahead in a competitive market.

InMoment’s XI Platform utilizes AI technology to generate auto-generated conversation summaries. These insights give users access to conversation themes, product or service mentions, and can also help track agent performance. This feature has been shown to reduce the average handle time (AHT) by up to 33%.

4. Personalized Customer Interactions

By understanding the nuances of conversations, conversational intelligence enables businesses to personalize customer interactions. This personalization goes beyond addressing customers by name; it involves tailoring responses based on historical interactions, preferences, and previous purchase behavior. The result is a more engaging and customer-centric experience that fosters brand loyalty.

5. Efficient Resource Allocation

Understanding the dynamics of customer conversations helps businesses allocate resources more efficiently. By identifying peak hours, common customer queries, and areas of high demand, organizations can optimize staffing levels, ensuring that they have the right resources in place to handle customer interactions effectively.

Conversation Intelligence Examples

When examining conversation intelligence, it becomes evident that the impact of this transformative technology extends across various industries by reshaping communication dynamics and optimizing operational processes. Let’s look at a few industry-specific examples. 

1. Healthcare: Optimizing Patient Interactions

In the healthcare industry, conversational intelligence proves to be invaluable for optimizing patient interactions. By analyzing conversations between healthcare professionals and patients, organizations can enhance communication effectiveness. This includes understanding patient concerns, improving appointment scheduling processes, and ensuring compliance with healthcare regulations. The insights derived contribute to a more patient-centric approach, fostering improved healthcare outcomes and patient satisfaction.

2. E-Commerce: Enhancing Customer Support and Sales

In the e-commerce sector, conversation intelligence plays a crucial role in enhancing customer support and driving sales. By analyzing customer interactions across various channels, such as live chat, emails, and phone calls, businesses gain insights into customer preferences, product feedback, and common pain points. This information is instrumental in tailoring marketing strategies, improving the online shopping experience, and providing personalized recommendations, ultimately boosting customer satisfaction and loyalty.

3. Financial Services: Ensuring Regulatory Compliance

In the highly regulated financial services industry, conversation intelligence is essential for ensuring regulatory compliance. By analyzing conversations in customer interactions, financial institutions can monitor and enforce adherence to compliance standards and legal requirements. This proactive approach helps mitigate risks, avoid regulatory penalties, and maintain the integrity of financial operations.

These industry-specific examples showcase the targeted application of conversation intelligence, demonstrating its ability to address unique challenges and opportunities within different sectors. Whether it’s optimizing patient care in healthcare, enhancing customer support in e-commerce, or ensuring regulatory compliance in financial services, conversation intelligence proves to be a versatile tool with applications tailored to industry-specific needs.

Conversation Intelligence with InMoment

The adoption of conversational intelligence, powered by advanced technologies like natural language processing and machine learning, is not merely a strategic choice but a necessity in the contemporary business landscape. InMoment’s conversation intelligence technology empowers organizations to navigate the intricacies of conversations and turn insights into actions, and ultimately drive business success. Schedule a demo to learn more today! 

Contact centers are not new by any means. As a matter of fact, the first employed call center agents date back to the 1950s. But, these agents only made and received phone calls. It wasn’t until the turn of the century that these call centers evolved into multichannel contact centers. However, even these are quickly becoming obsolete. The future of the contact center lies in the omnichannel contact center. 

What is an Omnichannel Contact Center? 

An omnichannel contact center is a customer service model that integrates and manages various communication channels in a unified and seamless manner. Unlike traditional multichannel setups, where each communication channel operates independently, an omnichannel contact center provides a cohesive and consistent customer experience across all channels.

What’s the Difference Between Omnichannel and Multichannel Contact Center?

In an omnichannel contact center, customers can interact with a business through channels such as phone calls, emails, chat, social media, and more. What distinguishes it from multichannel systems is the integration of these channels, allowing customers to switch between them without losing the context of their interactions. For example, a customer might start a conversation via chat and then continue it later over the phone, with the contact center retaining the history and context of the entire interaction.

Is an Omnichannel Contact Center Better Than A Multichannel Contact Center?

The debate between an omnichannel contact center and a multichannel contact center often revolves around the depth and quality of customer experience. While both models involve managing customer interactions across various channels, there are distinct differences in their approaches.

While a multichannel contact center provides various communication options, an omnichannel contact center is better positioned to meet the heightened expectations of today’s customers. It not only offers diverse channels but integrates them seamlessly, creating a unified and customer-centric approach that fosters satisfaction and loyalty. Ultimately, the choice between the two models depends on the organization’s commitment to delivering a superior customer experience.

What are the Benefits of an Omnichannel Contact Center?

Embracing strategies that bolster omnichannel contact centers facilitates the delivery of seamless, convenient, and efficient customer interactions. Through the integration of various communication channels into a cohesive system, businesses can enjoy a host of benefits that greatly elevate the customer experience.

Enhanced Customer Engagement

Omnichannel contact center solutions empower businesses to engage with customers on their terms. Whether a customer prefers email, phone, chat, or social media, an omnichannel system seamlessly integrates all these channels, allowing for consistent, real-time interactions. When customers can switch between channels without losing context, it reduces frustration and leads to a more positive experience.

Imagine a customer who initiates a support request through chat but needs to step away. With an omnichannel system, they can easily switch to a phone call later, and the agent will already have the chat history, avoiding the need to repeat the issue. This level of flexibility and continuity in customer feedback fosters better relationships and builds trust.

Efficient Closed-Loop Systems

In a contact center, the value of closing the loop is of the utmost importance. Omnichannel solutions can greatly improve the efficiency of closed-loop systems within a business. When agents have access to a unified view of customer data and interaction history across all channels, they can quickly understand the context of the customer’s issue.  This is a crucial element of customer experience management as it ensures that no customer concern or query falls through the cracks and that each issue is tracked from initiation to resolution.

Omnichannel contact center solutions can also incorporate automation and AI-powered tools. These technologies can intelligently route customer inquiries to the most appropriate agent or department. For instance, a chatbot can handle routine queries, while complex issues are directed to specialized human agents. This streamlines the resolution process, ensuring that customers get the help they need as quickly as possible.

Consistent Brand Experience

Consistency is a key factor in creating a strong and recognizable brand. Omnichannel contact center solutions enable businesses to deliver a consistent brand experience across all customer touchpoints. From the tone of communication to the information provided, customers should feel that they are dealing with one unified entity, not a collection of disconnected departments.

This consistency helps to reinforce brand loyalty and trust. Whether a customer is engaging on social media, talking on the phone, or visiting a physical store, the brand experience should remain uniform. Omnichannel solutions can help achieve this by ensuring all customer-facing teams have access to the same information and guidelines for interacting with customers.

Data-Driven Insights

Omnichannel contact center solutions provide valuable data and analytics. By collecting and analyzing customer interactions across various channels, businesses gain insights into customer preferences, pain points, and behavioral patterns. These insights can inform strategic decisions, improve product and service offerings, and enhance the overall customer experience.

Data-driven insights also allow for more personalized interactions. When agents have access to a customer’s history and preferences, they can tailor their responses and recommendations, creating a more personalized and meaningful interaction. This not only improves the customer experience but can also lead to increased sales and customer retention.

What Does an Omnichannel Contact Center Solve?

An omnichannel contact center addresses several challenges and issues in customer service and provides solutions to enhance the overall customer experience. Here are key problems that an omnichannel contact center aims to solve:

Customer Friction

• Issue: In a multichannel environment, customers may experience friction when transitioning between different channels, leading to a disjointed and frustrating experience.
• Solution: Omnichannel contact centers eliminate friction by offering a seamless and consistent experience across all communication channels. Customers can switch between channels without losing context or having to repeat information. 

Operational Efficiency

• Issue: Multichannel centers may struggle with the inefficiency of managing different channels separately, leading to delays, errors, and increased workload.
• Solution: Omnichannel contact centers centralize communication management, improving operational efficiency. Agents can access all channels from a unified dashboard, streamlining processes and reducing the risk of information silos.

Enhanced Insights

• Issue: Siloed data from separate channels can make it challenging to gain a comprehensive understanding of customer behavior and preferences.
• Solution: Omnichannel systems provide a holistic view of customer interactions, allowing businesses to gather valuable insights. This data-driven approach enables informed decision-making and the ability to tailor services to customer needs.

Features to Look for in an Omnichannel Contact Center

When deciding on omnichannel contact center solutions, there are many things to consider. The functionalities you need will be dependent on the uses and goals of your contact center. Regardless, there are a few main features that any successful contact center should have. 

Agent Scorecards

Agent Scorecards are tools that provide a comprehensive evaluation of an individual agent’s performance within the contact center. These scorecards typically encompass a range of key employee and customer experience KPIs and metrics specific to the agent’s responsibilities. Metrics may include factors like average handling time, first contact resolution, customer satisfaction scores, and adherence to scripts or guidelines. By analyzing these metrics, managers gain insights into an agent’s efficiency, effectiveness, and overall contribution to the customer experience.

AI-Driven Text Analytics

AI-Driven Text Analytics refers to the application of artificial intelligence and machine learning techniques to analyze and derive meaningful insights from unstructured text data. In the context of an omnichannel contact center, unstructured data often includes customer interactions from various sources, such as emails, chat transcripts, social media comments, and survey responses. Traditional analysis of such data can be time-consuming and challenging due to its unstructured nature, but AI-driven solutions bring efficiency and accuracy to the process.

Transcription Services

Contact centers often deal with a significant volume of audio data, including customer service calls, feedback voicemails, and internal meetings. Manually processing and analyzing these recordings can be time-consuming and impractical. Transcription services streamline this by converting hours of audio content into text, making it more manageable for further analysis and exploration. This process is essential for making audio content accessible, searchable, and analyzable.

Quality Assurance

Quality Assurance in a contact center involves systematic monitoring and evaluation of customer interactions to ensure that they meet predefined standards of excellence. This process encompasses various elements, including adherence to scripts, accuracy of information provided, adherence to company policies, and the overall effectiveness of customer service.

Strategic Services

Strategic Services in an omnichannel contact center context involve a suite of specialized offerings designed to provide businesses with expert guidance and actionable insights. Engaging in strategic services allows businesses to tap into the expertise of professionals who specialize in optimizing contact center operations. These experts often have a deep understanding of industry trends, best practices, and emerging technologies. These services also include the provision of detailed and customized reports that dive into contact center analytics and answer key business questions. These questions could revolve around performance metrics, customer satisfaction, operational efficiency, and other critical aspects of contact center management.

Omnichannel Contact Center Examples

Numerous organizations across various industries have successfully implemented omnichannel contact centers to enhance customer experiences and streamline communication across multiple channels. To represent the practical uses of an omnichannel contact center solution, let’s examine two customers in two different industries. 

Financial Services

A customer, Sarah, encounters an issue with a recent credit card transaction. She notices an unauthorized charge on her credit card statement and is concerned about potential fraud.

The Omnichannel Solution:

• Mobile App Investigation: Sarah logs into the mobile banking app to review her recent transactions. The app provides detailed information about each transaction, allowing her to identify the unauthorized charge.
• In-App Chat Support: Using the in-app chat support feature, Sarah initiates a conversation with a customer support representative. She explains the issue and provides details about the unauthorized transaction.
• Live Video Call Option: The customer support representative, recognizing the sensitivity of the issue, suggests initiating a live video call through the mobile app. This allows Sarah to show any supporting documents or ID verification directly.
• Context Preservation: Throughout the interaction, Sarah’s information and conversation history are seamlessly transferred from the mobile app to the video call, ensuring that she doesn’t need to repeat information and that the context is preserved.
• Resolution and Follow-Up: The representative investigates the issue in real-time, verifies Sarah’s identity, and resolves the unauthorized charge. During the video call, the representative guides Sarah through the steps to secure her account and provides assurance. Follow-up communications are sent through secure channels, ensuring privacy.

In this financial services scenario, the omnichannel contact center enables a smooth and secure resolution for the customer, utilizing mobile app features, chat support, and live video calls to address the issue comprehensively.

Retail

Omnichannel contact centers can also succeed in other industries. Let’s say John, a retail customer, purchases a laptop online from a retail store. Unfortunately, the laptop arrives with a defect, and John needs assistance with returning and replacing the product.

The Omnichannel Solution:

• Live Chat Support: Using the website’s live chat support, John initiates a conversation with a customer service agent. He explains the issue with the defective laptop and requests assistance with the return and replacement process.
• Email Correspondence: To provide supporting documentation, John uploads pictures of the defective product through the live chat. The customer service agent suggests continuing the conversation via email to maintain a documented trail.
• In-Store Return Option: The retailer’s omnichannel strategy includes an option for in-store returns. The customer service agent provides John with the option to return the defective laptop at a nearby physical store for quicker resolution.
• Context Transfer: When John visits the physical store, the in-store staff has access to the email correspondence and live chat history, ensuring a seamless continuation of the resolution process. John doesn’t need to re-explain the issue.
• Replacement Process: The in-store staff processes the return, facilitates the replacement of the laptop, and ensures that John leaves the store with a fully functional product. Any additional communications or confirmations are sent via email for John’s records.

In this retail scenario, the omnichannel contact center facilitates a convenient and efficient resolution for the customer, utilizing online order tracking, live chat support, email correspondence, and in-store returns to address the issue effectively.

Develop Your Omnichannel Contact Center 

With InMoment’s omnichannel contact center software, you can house all your data in one place. This capability allows you to deliver consistent, quality, and customer-centric experiences. Schedule a demo to see what InMoment can do for you.