Updated December 28, 2023

Customer experience analytics describes a means to find and collect information about prospects and customers, and how that data is analyzed. Using customer experience (CX) analytics properly can yield a range of benefits, including:

• Highlighting problem areas within the sales and marketing procedures
• Finding ways to better serve customers (product updates, new features, etc.)
• Improve sales copy and onboarding language to improve retention and repeat business
• Insight into brand perception

Discover how practitioners around the globe are using data, analysis & AI to improve customer experience based on this landmark survey from CallMiner.

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CallMiner CX Landscape Report: 2024

Discover how practitioners around the globe using data, analysis & AI to improve customer experience based on this landmark survey from CallMiner.

In this article:

• What is customer experience analytics?
• Data types and sources used for customer experience analytics
• Use cases for customer experience analytics
• Getting started with customer experience analytics
• Customer experience analytics tips & best practices
• Frequently asked questions

What is customer experience analytics?

Today, there’s more data available to companies than ever before, and leveraging that data to create exceptional customer experiences is critical for maintaining a competitive edge.

Customer experience analytics is the collection, analysis, and interpretation of customer data to gain insights into customers’ needs, behaviors, perceptions, and experiences with a brand or its products or services. CX analytics can help your business understand the complete customer journey, from their first interaction with your brand to their post-purchase experience.

Data types and sources used for customer experience analytics

Rather than analyzing individual data sources in silos, customer experience analytics combines data from multiple data sources to provide a more complete picture of customer engagement and insights from all types of customer interactions in context. That’s what makes CX analytics so valuable.

Generally, there are four primary types of customer data that can inform customer experience analytics.

Identity and demographic data

This includes basic information that identifies or describes a customer. Examples include:

• Name
• Date of birth
• Contact information
• Social media profiles
• Ethnicity
• Income level
• Marital status
• Industry or career

Interaction and engagement data

This data provides insights into how customers engage with your brand on specific touchpoints, such as your company’s website, social media, and email. Examples include:

• Social media likes, comments, and shares
• Number and duration of video views
• Website visits, bounce rate, time spent on page, conversions, etc.
• Click-through rate (CTR)
• Conversion rate
• Heat map data
• Email opens, forwards, and bounces

Behavioral data

Behavioral data offers insights into the actions customers take when interacting with your products or services. Examples include:

• Purchase history
• Customer spend
• Shopping cart abandonment
• Subscription cancellations or renewals
• Trial offer participation
• Types of devices used to interact with your brand
• Customer retention rate
• Customer churn rate
• Customer lifetime value (CLV)

Attitudinal data

Attitudinal data provides insights into what your customers think and how they feel about your brand, product, or service. This can include information on customer satisfaction, customer effort, sentiment, and emotion, both overall and at specific touchpoints throughout the customer journey. This data can be gleaned from sources such as:

• Call center interactions
• Third-party review sites
• Social media comments
• Forums and message boards
• Email and chat interactions
• Surveys such as Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES)

New capabilities in the CallMiner platform deliver improved outcomes in the contact center and enterprise-wide, including:

🔍 Enhanced advanced search capabilities
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Learn more:

— CallMiner, Inc. (@CallMiner) November 7, 2023

In addition to data related to what your customers do and how they perceive your brand, products, or services, it’s also crucial to track metrics related to call center efficiency and how your business responds to customer needs, such as:

• Average call handling time (AHT)
• First-call resolution rate (FCR)
• Repeat contact rate
• Escalation rate
• Number of new support tickets
• Open support tickets
• Time to resolve customer support tickets
• Time spent on hold
• Average response time
• Total resolution rate
• Team performance metrics
• Agent performance metrics
• Agent churn
• Employee experience metrics

These metrics provide valuable context to customer experience data, making it possible to correlate or attribute customer behavior, attitudes, and engagement to your business’s services, such as:

• An increase in customer churn following a drop in first-call resolution rate
• A decrease in CSAT score following a significant increase in the number of unresolved customer support tickets
• An increase in customer effort score following an increase in repeat contact rate
• An increase in purchase values attributed to a specific sales representative or team

Use cases for customer experience analytics

Customer experience analytics can be used in many ways to drive business performance and boost customer satisfaction by creating impactful, memorable, and positive customer experiences. Here are just a few of the ways business intelligence can leverage customer experience analytics to drive business results:

• Personalization: Customer experience analytics are often used to personalize marketing messaging and tailor offers perfectly to the customer’s needs and preferences.
• Customer journey optimization: Businesses can leverage CX analytics at every touchpoint to map and analyze the customer journey, identifying the most crucial touchpoints and key moments of decision where you can make the biggest impact.
• Anticipating customer behavior: By analyzing past behavior, current trends, and other factors, predictive CX analytics can make data-driven predictions about what customers will do next, such as make a purchase, churn, or how they’ll respond to a marketing campaign.
• Reducing customer churn: Not only can sophisticated CX analytics help to predict customer churn, but it can also analyze the factors that lead to it. This allows you to develop targeted strategies to retain those at-risk customers, such as enhanced customer support or personalized incentives.
• Product and service innovation: Whether you’re looking to improve an existing product or service or develop something new and innovative that meets a customer need, customer experience analytics provides valuable insights into the features, capabilities, and services customers need and want.

Getting started with customer experience analytics

There is no shortage of articles and tools surrounding the topic of customer experience analytics. If your business has not yet implemented a strategy to track key CX metrics, it can be tough to know where to begin. Doing customer experience analytics right requires organizations to:

• Choose the right tools for collection and analysis, such as conversation analytics solutions, which can score 100% of your customer interactions to evaluate the overall customer experience
• Track the right metrics, such as Voice of the Customer, Net Promoter Score, CX Scorecards, and other methodologies, and tie them all together
• Translate data into actionable insights that inform decision-making to create a better customer experience through the use of AI-driven conversation intelligence and other tools

Customer experience analytics tips & best practices

To help you get started with customer experience analytics and leverage it effectively to meet your company’s unique needs, we’ve rounded up valuable tips and best practices from leading customer experience experts. The tips and quotes below are listed in no particular order and include links to the original source material and (where available) social media links so you can follow the experts you find most valuable.

1. Tap into the power of AI-driven conversational intelligence. “Conversational intelligence is a combination of machine learning and natural language processing technology. Instead of relying on surface-level assessments of written or spoken information, conversational intelligence leverages the adaptive powers of artificial intelligence (AI) to spontaneously deduce intent, sentiment and meaning from such data. This makes it possible for teams to assess large numbers of interactions much more deeply and in a relatively short amount of time.” - What is conversational intelligence?, CallMiner; X/Twitter: @CallMiner

2. Make sure your data is telling the truth. “Activity statistics are derived through the use of pre-defined or targeted user groups surveys or reviews, cookies, banners, html email, adware and spyware.

Predefined groups are used by Nielson NetRatings to produce commercial guidelines for internet design. They include recommendations of functionality, content design and creation and accessibility.

Commonly, internet experts are used to assess websites, they perform heuristic evaluations based upon defined criteria. This method is highly favored in commercial arenas as it provides absolutes.

An unfortunate aspect of this sort of evaluation is a level of condescension is required to dumb down the findings to allow for non-technical usage of the internet. While this kind of study may produce insights into professional aspirations for internet usage it says very little about mass consumption. The motivation for this kind of study is consultancy or publication fees. The use of this sort of data to describe national or global interactions is highly suspect.

Targeted user groups are used to perform ethnographic reviews commonly in users’ offices or homes that study interactions based upon goals, walkthroughs or scenarios. This type of study is highly effectual in producing notional interaction behaviour. Until recently the use ethnographic reviews has been associated with a response to an existing website rather than the underlying philosophy or ethos.” – Karl Smith, Do #eCommerce #analytics prove #customer #affiliation?, Karl Smith; X/Twitter: @UserExperienceU

3. Trust is now the ultimate differentiator. “Trust is an advantage; plain and simple. For brands to compete today, they must build trust through a complex ecosystem of customer touchpoints. Consumers are becoming more aware of the information they provide to brands, and they have expectations on delivery, costs, and quality that continue to rise with each interaction.

A breach of trust could cost a brand millions of dollars from the fall out. But trust is not just about protecting the information customers provide to you, but rather your ability to deliver on a promise of quality and service. All businesses are in the business of trust and those that earn the most trust will be the winners.” - TJ Claridge and Frank Lee, Customer Experience: A Key Brand Differentiator, Spiceworks; X/Twitter: @SpiceworksNews

4. There’s a difference between customer experience and customer service. “Even the most experienced marketers confuse the customer experience with customer service. They’re also an important cog of the matrix – defining them initially is key:

Customer service is an action performed in reaction to a customer comment, request, complaint or question. Customer service is performed once you have a customer, and it is part of your customer retention strategy.

Customer experience is everything you can do proactively to attract a prospective customer, as well as to promote general good will about your business once your customer enters the funnel. The experience is engagement in every form, not just public relations.

The days of guerilla marketing are over. You can no longer get away with sneaky or deceptive marketing tactics; today customers are savvy. You no longer ‘own’ your message; commerce transacts in a democratized world.

We live in a world where the customer has just as much weight as you do when it comes to influencing purchase paths. Storytelling is good for your brand, but having the customer join you in the engagement matrix is far more powerful.” – Marsha Collier, Human Connections with Your Customers through the Marketing Experience Matrix, Marsha Collier’s Musings; X/Twitter: @MarshaCollier

5. Pre-planning the tech is a crucial piece of the puzzle. “QoS-Quality of Service (analytics) and QoE-Quality of Experience (user satisfaction) are tightly intertwined in an ever challenging and complex environment of networks, devices, mobile and applications. Bandwidth is often considered the cause for jitter, round trip delays, echo, crosstalk and noisy calls.

As a technical professional and integrator you are faced with planning in advance what the problems will be and providing instant solutions when the C-level calls to complain. The key issue is having tools that when the CXO calls complaining about having a terrible call with a customer you need a better answer than – ‘I don’t have any idea.’” – Evan Kirstel, QoS vs QoE, LinkedIn; X/Twitter: @evankirstel

6. Make sure to make room for personalization. “We need to move away from process standardization and towards experience personalization. The only way we can do that is by starting to move away from segmenting customers by circumstance and towards categorizing customers by need.

Here’s a simple 5 step framework to help you achieve this:

• Understand: Work to understand your customer’s true needs, not wants. Describe your customers the way you would describe your best friends.
• Categorize: Make different categories based on who your customers are as people and what their needs are.
• Strategize: Build your customer experience strategy around the delivery of these needs. Design products and services that specifically deliver the needs of your new customer categories.
• Attract: Market to and attract the types of people who will be the best fit for your needs based products and services. Market to different categories differently. The kind of advert that might attract the Prince of Wales probably won’t attract the Prince of Darkness even though they share many of the same circumstances.
• Guide: When customers come to you, make sure to ask the right question to help you accurately categorize them to make sure you are delivering the products and services they really need in a way that is personalized for them.”

– James Dodkins, Don’t Segment Your Customers, LinkedIn; X/Twitter: @JDODKINS

7. All the analytics in the world won’t trump quality. “CX is not about websites. It’s not about buying experience. It’s not about a shiny new store in a high rent location. It’s not even about the products. It’s about how the products over-deliver on the experience people expect and add value.

Quality is remembered long after price is forgotten’ were the famous words of Sir Henry Royce (founder of Rolls Royce).

As Apple has done with me (and many others) they have converted me to become an advocate. Yes, the products are beautiful. Yes, the products are reliable. Yes, they make sense because they retain a high portion of their value, but it’s far more than that the experience is outstanding.

So, my question to you is…is YOUR company experience outstanding?

Do you deliver far more than your customers expect? Do your customers become advocates? Do you thrill your customers? Do you do whatever is required to make sure they are glad they invested in your product and service?” – Adam Gray, What customer experience means in the real world, DLA ignite; X/Twitter: @DLAIgnite

8. Data analysis is a part of business now. “Data has often been called the new oil. While companies have a social media strategy, a privacy policy they also need a talent data strategy too. One of the issues with data, is data privacy and the GDPR regulations which means that holding people data that are not your employees require you to get opt-in.

To quote a recent LinkedIn report, ‘One of Nielsen’s businesses tapped its People Analytics team to understand why it was losing talent. Starting with five years of people data in a (big) spreadsheet and some hypotheses, they identified the factors most highly correlated with attrition. The biggest finding was that employees with a change in job responsibilities due to promotion or lateral movement within the past two years were much less likely to leave. This insight prompted Nielsen’s leadership to focus on making it easier for employees to learn about and pursue jobs internally and identifying ‘at-risk’ high performers and proactively putting opportunities in front of them.’” – Tim Hughes, The 4 Technologies That Killed Transactional Recruitment Process, DLA ignite; X/Twitter: @Timothy_Hughes

9. Use customer analytics to identify potential high-value customers. “Predictive analytics can help you identify customers who are likely to become high-value customers in the future by analyzing their past behavior. This information can be used to target these customers with special offers or promotions that are designed to convert them into high-value customers.

For example, if a customer has bought several linen suits from the summer collection over the past few months, you might predict that they are likely to buy several more from the fall collection that will be rolled out in the next few weeks. You could then target them with a personalized text saying you think they would be interested in the soon-to-be-released fall collection, and offer them a sneak preview in order to encourage them to make a purchase.

You could also use predictive analytics to identify customers who are likely to churn, or stop doing business with you. This information can be used to target these customers with special offers or promotions that are designed to keep them from churning.

By understanding their customers’ past behavior, businesses can identify those who are most likely to be valuable in the future and target them with offers that are tailored to their needs.” - 3 ways to use data analytics to identify and target high-value customers, Tulip; X/Twitter: @TulipRetail

10. Leverage customer experience analytics to create personalized experiences. “When it comes to capitalizing on customer experience as a key differentiator, the importance of personalization cannot be understated. In fact, 80% of customers are more likely to make a purchase when offered personalized experiences.

These days, however, a personalized customer experience doesn’t mean sending the odd email with the customer’s name in the header. Many online retailers and SaaS companies are now going the extra mile by using algorithms to offer content, landing pages, and recommendations that are tailored to the individual customer.

Intuitive and helpful personalization tactics like this make customers feel valued and appreciated. It means they are more likely to enjoy and further advocate the product or service they are purchasing. Furthermore, personalized recommendations are a great way to proactively solve your customer’s unique problems.” - Customer Experience: The Key Brand Differentiator In Business, Talkative; X/Twitter: @talkativeuk

11. Explore the emotion behind customer sentiment. “Sentiment and emotion are not considered one and the same. In fact, the latter is far more complex than the former in practice.

Emotions center on the individual who experiences them, arising from a subjective experience while engendering both a physical and behavioral response. Sentiment, on the other hand, can be described as resulting from relationships with other parts of society at large. Sentiment points outward (opinion) and emotion points inward (mood).

The nature of sentiment limits the results its analysis can produce. Sentiment analysis tends to yield binary results at its most basic level, with results often classed as either positive sentiment or negative sentiment – sometimes a neutral classification is used as well. This simple polarity provides enough insight when used in a narrow context to make it worthwhile. However, exploring the deeper emotional context behind sentiment can produce more powerful results.” - The ultimate guide to sentiment and emotion analysis, CallMiner; X/Twitter: @CallMiner

12. Set the vision before you get into the analytics. “Getting the most value from your customer experience analytics will require a well-defined vision of the insights you want and the technology sophistication you will need to produce them. Combine this future vision with a thorough self-assessment of your current state. This will help to identify gaps that must be bridged to realize your vision.

Once you identify key metrics and data sources, you will need to determine the infrastructure requirements to ingest, store, process and serve up the data. Unstructured data will likely require big data technology, operations and management. The integration of structured and unstructured data will require information architecture and management (taxonomy, metadata, data quality and governance).

In addition to these technical challenges, there may also be organizational challenges to overcome. Do you have the right people, skill sets and processes to achieve an alignment of purpose and an alliance for taking action? If not, look for experts in the field to help you jumpstart the learning curve and to manage this organizational change.” – Dave Zwicker, Enhancing Your Digital Customer Experience with Analytics and Insights, Earley Information Science; X/Twitter: @EarleyInfoSci

13. How you visualize and structure analytics is important. “Structured customer feedback is the most common, and the easiest to deal with. For example, a typical question in your survey might be, ‘How satisfied were you with your most recent experience at Hotel ABC on a scale of 1 to 5, with 5 being very satisfied and 1 being very dissatisfied?’

When customers provide their answer to this question, it comes in the form of a number. In this case, a 5 would indicate very satisfied with the most recent visit to the hotel, a 1 would indicate very dissatisfied, and so on.

Almost any VoC software platform can easily analyze these data and create graphs to aggregate and compare the responses: Maybe 30% of respondents were very satisfied, 35% very dissatisfied, and so forth.” – Sean McDade, Lesson #4: Text Analytics Is More Than A VoC Feature — It’s An Absolute Must-Have, PeopleMetrics; X/Twitter: @PeopleMetrics

14. You can’t fully automate CX. “When it comes to more complex problems, customers continue to seek out human assistance. In a recent report, 82% of respondents claimed to want the reassurance only a live agent could offer when asked why they escalated their issue.

That’s why a fully automated approach to CX won’t be successful. When it comes to empathy and resolution of complex issues, live agents are irreplaceable. Machine-learned results must be considered within the context of the customer’s perspective, as well as agent resources, technology, training, and empowerment to truly bridge the insight-to-action gap. When that perfect balance is found, AI and ML are powerful tools that make customer interactions more humane.” - 6 Steps to Improve Customer Interactions with AI, CallMiner; X/Twitter: @CallMiner

15. Focus on creating analytical “harmony” in your business. “In the world of customer analytics, soaring solos only get you so far. Delivering harmonious customer experiences across the customer life cycle requires careful collaboration across different business functions — from marketing to customer care to operations to product — yet, since each line of business has its own genre of incentives and KPIs, it’s virtually impossible to get them to play off the same sheet music.

Clearly, a new paradigm is needed. The next best experience (NBX for short) is the holy grail that CI pros should strive toward in order to deliver the right experience to the right customer at the right time. Like its ‘next best trilogy’ forebears (next best product, next best offer, and next best action), NBX delivers a recommendation based on signals from customer data.

Unlike them, however, it analyzes signals across the customer journey, independent of business domain. The recommendation can therefore manifest in many types of customer experiences: from customer service to customer engagement to operations or sales and marketing.” – Brandon Purcell, Orchestrate Your Customer Analytics Practice With The Next Best Experience, Forrester; X/Twitter: @forrester

16. Tap into predictive analytics. “Today, companies can regularly, lawfully, and seamlessly collect smartphone and interaction data from across their customer, financial, and operations systems, yielding deep insights about their customers. Those with an eye toward the future are boosting their data and analytics capabilities and harnessing predictive insights to connect more closely with their customers, anticipate behaviors, and identify CX issues and opportunities in real time.

These companies can better understand their interactions with customers and even preempt problems in customer journeys. Their customers are reaping benefits: think quick compensation for a flight delay, or outreach from an insurance company when a patient is having trouble resolving a problem.

These benefits extend far beyond the people typically thought of as ‘customers’—to members, clients, patients, guests, and intermediaries. Early movers in the world of customer-experience analytics herald a fundamental shift in how companies evaluate and shape customer experiences.” - Rachel Diebner, David Malfara, Kevin Neher, Mike Thompson, and Maxence Vancauwenberghe, Prediction: The Future of CX, McKinsey & Company; X/Twitter: @McKinsey

17. Test your decisions and try to view yourself objectively. “If you have lots of sales personas, create a fake customer that is each of those personas, and then that customer should get all the emails, invoices, everything else that a regular customer that fits that persona group should get.

Then take a look at those accounts. Are you awesome, or are you super annoying? Do you hear nothing for a year, except for invoices, and then, ‘Hey, do you want to renew?’ How is that conversation going between you and that customer? So really try to pay attention to that. It depends on your organization if you want to tell people that this is what’s happening, but you really want to make sure that that customer isn’t receiving preferential treatment.

So, you want to make sure that it’s kind of not obvious to people that this is the fake customer so they’re like, ‘Oh, well, we’re going to be extra nice to the fake customer.’ They should be getting exactly the same stuff that any of your other customers get. This is extremely useful for you.” – Dana DiTomaso, Building Better Customer Experiences – Best of Whiteboard Friday, Moz; X/Twitter: @Moz

18. Analytics turns raw data into actionable insights. “Unless you’re a skilled data scientist, you can’t do much with raw data. To get real value, you need to understand how that data translates into insights and then turn them into measurable actions.

Marketers need a way to analyze and understand what the endless data points and metrics are telling them. Analytics helps them understand the story that’s hidden in that data. Like oil needs refining, data needs analytics to refine it into something valuable to the business.

Analytics sits on top of your data and helps you draw out the intelligence and insights hiding in it. For example, by surfacing trends in your data and overlying different points, you can analyze, compare, and discern actionable strategies for your marketing campaigns.

Bringing data and reliable analytics together helps you become a data-driven marketer who acts purposefully and makes sound strategic decisions backed by intelligence.” - Paul Scondac, Got Analytics? Becoming a Data-Driven Marketer, SugarCRM; X/Twitter: @SugarCRM

19. Tap into and connect your existing data sources. “You don't need to overhaul everything to do analytics in order to build customer engagement. Start with what you have. Mine through and connect all available data sources to gain insights into customers, including where new data is needed to make the customer engagement picture complete. To make the most of the data, you must measure the right things.

Develop a clear understanding of the available data to ensure it is appropriate for use related to customer engagement. There is no one-size-fits-all option when it comes to data and analytics for customer engagement. To get to the right data, ask these questions:

• Who is the data about?
• Where is the data measured in the organization?
• What data is measured? What is the purpose of its collection?
• How is the information used? The action could be decision-making, stakeholder status updates or customer performance dashboards.”

- Sandra Mathis, 6 ways to use analytics to improve customer engagement, TechTarget; X/Twitter: @TechTargetNews

20. Internet of Things (IoT) data can be valuable. “In the current business environment, IoT and automation open the door for a wide range of customer experience improvements. Enterprise process automation allows businesses to set up whole workflows, accelerating the repetitive operations that would otherwise require the focus of key employees. These automation techniques allow professionals to focus more on creating outstanding customer experiences and less on menial tasks like data input.

The way consumers connect with brands has evolved over time, thanks to technology. Websites paved the path for the success of mobile devices and social platforms, giving firms additional chances to communicate with their customers directly. There has been a seismic shift in customer experience and engagement as a result of the development of the Internet of Things (IoT).

Today, a number of services that can establish touchpoints between businesses and their users are enabled by IoT data, including environment, usage, and inquiries. To ensure the greatest possible consumer and product experience, IoT data may be strategically utilized to personalize products and trigger alerts, education, and corrective events.” - Ashesh Anand, How can you Improve Customer’s Experience Using IoT?, Analytics Steps; X/Twitter: @AnalyticsSteps

21. Use analytics to complete your buyer journey map. “To identify the gaps in your experience, start with an experience or journey map you already have. If you don’t already have one, keep it simple and make a rough sketch of the major user flows of your product experience. Review these key flows with your customer-facing teams and start circling areas where a mode shift occurs. Maybe it’s when a user switches from an app to an email or call, or goes from buying to using a product. Focus on the gaps you all agree on, but prioritize and optimize the ones that front-line customer-facing teams think are the biggest issues. Name a product and operating team steward for each circle to ensure you have someone accountable for closing that gap.

In your next sprint: Gather up existing flows or take a first pass at sketching your flows. Identify the key stakeholders from across the organization, but especially from every customer-facing team. Begin circling your gaps.” – Shiren Vijiasingam, Don’t Frustrate Users with Gaps in Your Product Experience, General Assembly; X/Twitter: @GA

22. Leverage AI for hyper-personalization. “Hyper-personalization is the most advanced way brands can tailor their marketing to individual customers. It’s done by creating custom and targeted experiences through the use of data, analytics, AI, and automation.

Through hyper-personalization, companies can send highly contextualized communications to specific customers at the right place and time, and through the right channel.

As digital marketing becomes more competitive, hyperpersonalized marketing provides the opportunity for organizations to meaningfully engage customers, deepen existing relationships and build new ones, and improve the customer experience.

Implementing this type of strategy not only increases customer satisfaction but also drives brand loyalty, willingness to spend, and overall marketing effectiveness.” - Connecting with meaning: Hyper-personalizing the customer experience using data, analytics, and AI, Deloitte; X/Twitter: @Deloitte

23. Don’t neglect the impact of emotion. “Working with one of the leading sellers of mobile devices we wanted to determine the extent to which consumers repurchased the same brand. As expected, those who rated their devices highly were more likely to repurchase the same brand than those who were dissatisfied with their devices.

But when we layered in the emotional attachment the numbers popped: consumers who liked their devices and expressed positive emotional attachments to their mobiles were 50% more likely to repurchase the same brand than those who also rated their devices favorably but showed no emotional feelings regarding the brand.” - Howard Lax, For “Rational” CX, Focus on How Your Customers FEEL, CustomerThink; X/Twitter: @customerthink

24. If something besides the customer is the “heart” of your data collection, analytics will be difficult. “What’s at the center of your data collection systems? Maybe financial and operational data. That inside-out architecture will make it harder for you to foster customer-focus and differentiate customer experience across the customer experience journey and life cycle. Put on your customer hat and shoes and think like they do.

After all, your company exists to (and thanks to!) help your customer get ahead in his/her business. Invest in creating a common definition of the customer and a common taxonomy and data structure.” – Lynn Hunsaker, Customer Experience Data Silos Demystified, ClearAction Continuum; X/Twitter: @clearaction

25. Analyze both O-data and X-data. “Organizations regularly collect and review the information generated by their operational systems, including enterprise applications like Customer Relationship Management (CRM), Human Resources (HR), financials, and supply chain. These systems produce vast quantities of operational data (O-data), which is what organizations have traditionally used to make most of their decisions. However, while O-data can reveal what has happened in the past, it often lacks insight into why something took place or what is likely to occur in the future.

That’s because O-data lacks a critical element…people. Information that reflects how people – including customers, employees, partners, suppliers, or prospects – think and feel about their interactions with an organization is called ‘experience data,’ or X-data.

X-data provides insight into people’s perceptions of and attitudes towards a company, and it is essential for understanding business operations and making timely adjustments to improve people’s experiences. Additionally, because people’s perceptions and attitudes are often a leading indicator of their future behaviors – like purchasing more, recommending the company, or looking for a new job – X-data allows companies to identify and resolve potential issues before they escalate into significant problems.” - Driving Insights with X- and O-Data, Qualtrics XM Institute; X/Twitter: @XM_Institute

Frequently asked questions

Data analytics enables businesses to gain valuable insights into customer behavior, sentiment, and emotion. Armed with this information, businesses can:

• Address customers’ wants and needs: Data analytics makes it possible to tailor customer experiences to address customers’ wants and need
• Create personalized experiences: Businesses leverage data analytics to create personalized experiences for individual customers.
• Identify customers likely to churn: Data analytics enables businesses to identify customers at risk of churn and take proactive steps to regain their trust.
• Cultivate customer loyalty: Companies can leverage data analytics to build brand experiences that win customers’ trust and loyalty.

There are four main steps in the customer experience analytics process, including:

• Data collection: Customer experience analytics solutions collect data from a variety of customer interactions, including social media, live chat, phone, email, the internet, and more. The data collection process may include voice-to-text transcription of unstructured data obtained from phone calls and other voice-based interactions.
• Data organization: After collecting data, data organization is the next step in the customer experience analytics process. This can include labeling and tagging data, categorizing or classifying data, and converting unstructured data into structured data for further analysis.
• Data storage: Customer experience analytics data must be stored — often in a secure manner if it contains personal information about individual consumers — in a central location for real-time and historical analysis to drive future decision-making.
• Data analysis: The data analysis stage applies artificial intelligence (AI) and machine learning (ML) to the data collected to derive actionable insights, uncover hidden trends and patterns, and identify new opportunities.

The four main categories of customer experience analytics include:

• Descriptive analytics: The most basic category of customer experience analytics, descriptive analytics describes essential information such as what happened, when, and who was involved.
• Diagnostic analytics: Diagnostic analytics takes descriptive analytics one step further, uncovering the reasons behind the who, what, and when of descriptive analytics. For example, diagnostic analytics may provide insights into why a particular customer decided to switch to a competitor at a particular time.
• Predictive analytics: Predictive analytics looks to the future. By analyzing historical data and uncovering patterns and trends, predictive analytics reveals what is most likely to happen next based on past behaviors and other details.
• Prescriptive analytics: Prescriptive analytics provides insights into what businesses should do in order to achieve the desired outcome. For example, if predictive analytics reveals that a customer is likely to churn, prescriptive analytics informs what the business should do in order to prevent churn and retain the customer.

Customer experience metrics, or CX metrics, focus on the customer and the customer journey throughout the customer’s interactions with a brand, from first exposure to marketing, sales, and customer service across channels such as the web, email, phone, and social media.

On the other hand, user experience metrics, or UX metrics, focus on the design of the product and how the end user interacts with a product. The goal of UX design is to create a product that’s user-friendly and appealing to the target user.

UX metrics are a part of CX metrics, but CX metrics have a much broader scope. UX metrics are limited to how a customer interacts with a product, while CX metrics encompass how a customer interacts with the brand across all touchpoints throughout the customer journey.