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"Lead or No Lead, That is the Question: How to Properly Use Customer Experience Analytics"

Dan Wojcik6/27/2023

We explain to you what Customer Experience Analytics is and how you can use it successfully.

Table of contents

What is CX Analytics?
Data, data, data – the foundation
How you can climb your CX data mountain
CX Analytics in practice
The CX Analytics Dream Team
Your tools for holistic CX Analytics
. These in turn show whether you are on the right track with your strategy and whether your already created personas — mostly created or heavily influenced personas by management, based on personal assumptions and wishes, match the real users.
and enable more efficient and effective customer service. It is important to consider ethical considerations and ensure the privacy of customers is maintained.
Only through a clean division of tasks will data lead to the team being able to continually improve the customer experience. CX forms a bracket between analysis and design (UX design). The iterative design process is thus, as far as input & outcome is concerned, round.

It is a hot topic and most people know: no or the wrong Customer Experience Analytics prevents the success of a brand. Yet this term often raises questions: What exactly is CX Analytics and how do you use it in a targeted manner? What makes a good CX Analytics team, and what are the tools and buzzwords you should know? This article provides the answers.

What is CX Analytics?

The term Customer Experience is indispensable in modern digital business. All too often, however, it flies under the radar or is treated half-heartedly. We live in a world where first-class customer experiences are crucial. If your brand can't provide that, another one can - and your customers are gone. The key to success lies in analyzing the customer experience. If you know what your users want to experience or maybe where they are not yet getting along, you can target and effectively optimize your offer and thus improve your KPIs more focused. Whether your goal is a purchase, a contact or an interaction. If you analyze how your users tick, you can really take off with your product.

It has never been easier to get customer data than it is today. Customer Experience Analytics refers to the use of data analysis techniques to measure, understand, and improve customer interactions with a company. Various data sources such as direct feedback, online reviews, social media interactions, and transaction data are used to draw a comprehensive picture of the customer experience.

By using Customer Experience Analytics, you can also identify patterns and trends in the customer experience. You can then use these insights to identify problems early on and take corrective action. All this, before they affect customer satisfaction.

In addition, through Customer Experience Analytics, you can create personalized customer experiences. The analysis of customer data and interactions enables you to create individual customer profiles and offer personalized products and services. The goal is to align your offering as closely as possible with the needs and preferences of your customers. However, the crux is in the evaluation of your findings and in the targeted implementation of improvements.

Data, data, data – the foundation

Customers can use an increasing number of channels to contact companies. Therefore, it is crucial that companies can provide a seamless omnichannel customer experience. A successful omnichannel strategy requires, however, an accurate analysis of customer data across all channels. And this is where omnichannel CX data comes into play.

Omnichannel CX data refers to the data collected when analyzing the customer experience across all channels. This includes all customer interactions with a company, regardless of whether these interactions took place online or offline. Here is an example of an omnichannel customer journey:

Touchpoint across social media, e.g. Instagram
Guided to the website
Downloads your app
Orders a product online
Contacts support by phone
Visits a store nearby

It is important to collect and analyze Omnichannel CX data, as it gives you a complete picture of the customer experience. This can be in the Customer Journey described above, e.g. Instagram Analytics, the analysis of support requests and returns or even the survey of employees internally about certain business processes. By analyzing the data, you can understand how customers interact on different channels and which channels are used most frequently. You can then use these insights to improve the customer experience across all channels.

With regard to the data protection discussion and the query of user consents (e.g. for Apple operating systems), you must attach particular importance to recording the consents as cleanly and comprehensively as possible, reliably and sensibly interpreting them and then feeding them back into the product development cycle. Therefore, the rather simple data from the Campaign Analytics of Instagram play just as big a role as the session screen recordings of for example Hotjar, from which you can trace every click of the users in minutes.

In CX Analytics, the focus is particularly on Micro Conversions and attempts to bind users to the service or product in a sustainable way. Micro Conversions can be individual process milestones, such as initiating the checkout or secondary interactions, such as a review by users, a blog post that is being read, or a product video that is being watched. These Micro Conversions are not a conversion in the classical sense at first glance, but they indirectly influence the decisions of your users. Therefore, they are an important building block of the customer experience and also contribute to business objectives.

Companies that use primary and secondary Omnichannel CX data and analyze it, therefore, have a better chance of increasing customer satisfaction, promoting customer loyalty, and ultimately increasing revenue.

How you can climb your CX data mountain

Despite the growing importance of CX, many companies struggle to turn CX insights into actions. Here are some reasons why this could be the case:

Lack of connection between CX insights and business goals: Often CX is seen as a separate unit and not as an integral part of the business goal. CX managers must ensure that CX insights are integrated into the business strategy and support business goals. CX Analytics, therefore, needs to gain higher importance.
Difficulties in measuring CX: CX is a subjective concept and can be hard to quantify. It can only ever be a quantitative approximation, but never an absolute number. Companies struggle to turn CX insights into tangible metrics to measure the ROI of CX initiatives and track success.
Silo thinking: CX affects all areas of the business and requires cooperation between different departments to create a seamless customer experience. Silo thinking within the business can lead to departments communicating or cooperating poorly, making it impossible to turn CX insights into action.
Lack of resources: A successful CX initiative requires time, money, and resources. If businesses lack sufficient resources to turn CX insights into action, they may struggle to create a positive customer experience. Artificial intelligence can be the game-changer here.
Lack of responsibility: CX initiatives require clear responsibility within the business. If clear responsibility for CX is lacking, CX insights can get lost between different departments or not be turned into action.

In summary, there are many reasons why businesses struggle to turn CX insights into action. Businesses need to ensure, above all, that CX is seen as an integral part of the business goal, that CX insights are turned into tangible metrics, that cooperation between different departments is promoted, and that sufficient resources are provided for CX initiatives. Only then can businesses create a positive customer experience and secure the success of their business.

CX Analytics in practice

You're probably thinking: What data is relevant to my goals?

That always depends, of course, on what you offer and how customers can interact with you. We've sketched this out for you using the example of a purchase in an online store and divided the purchase into three phases:

1. Awareness Phase

The awareness phase is the first phase of the purchase process. During this phase, your customers become aware of the product or service. In practice, this can be achieved through targeted advertising campaigns, search engine optimization, or social media activities. CX Analytics can be used in this phase to analyze which channels are most effective in attracting potential customers.

2. Consideration Phase

In the consideration phase, your customers evaluate different products or services and compare them. In an online store, this can be achieved by looking at product descriptions, customer reviews, and price comparisons. CX Analytics can be used in this phase to analyze the behavior of customers and understand it. This way, it can be found out which products or services are viewed most often and which information is most important for decision-making.

3. Purchase Phase

The purchase phase is the last phase of the purchase process. During this phase, your customer makes the purchase. CX Analytics can be used in this phase to analyze the buying behavior of customers. For example, it can analyze which payment methods are used most often, how long the purchase process takes, or what drop-off rates there are. This information can then be used to optimize the buying process.

After the purchase, the order (fulfillment) follows. There may be queries about the offer (Customer Support). Providing users with assistance and offering good service is also an important part of the customer experience. Here, the crucial question arises: Do your customers come back and buy from you again, or will they even recommend you? All this has to do with the experience that the customers have had from beginning to end. Only if the process runs smoothly does a company or brand have a chance of an authentic recommendation and returning clientele.

As briefly described in the practical example, you can collect and evaluate data, around the customer experience, at all touchpoints along your funnel.

CX covers the entire value chain from Attention (Marketing) to Dialog (Support) to Reconsideration (customer loyalty and re-activation). This includes traditional sales channels, the shopping experience on the website or in the app. The customer support and the physical experience of the product, the unboxing, the product itself and all associated interactions in physical and digital form. Not only the e-commerce sector, but especially digital products and services require a careful consideration and continuous further development and improvement of the Customer Experience.

Picture: Sample Customer Journey Map (Source: muse case GmbH / iStockphoto)

So, CX is positioned across the board and therefore also affects multiple departments and teams within a company. To set up a good CX Analytics team, you need to think holistically.

The CX Analytics Dream Team

There are various skills and attributes your CX Analytics dream team needs to be successful.

Firstly, you need someone with expertise who is able to collect, analyze, and interpret large amounts of data. This requires a broad understanding of data analysis tools and technologies and skills in data management and modeling. In addition, all team members need in-depth knowledge of the industry and a deep understanding of the needs and expectations of the clientele. Artificial Intelligence (AI) is particularly well suited for the evaluation of CX Analytics data. Especially in combination with capable real-life analysts.

The AI can then take over the following:

Processing large amounts of data: AI systems are able to process large amounts of data quickly and efficiently. CX Analytics often involves analyzing massive data sets that come from different sources like websites, mobile apps, social media, and customer feedback. AI can handle this at a speed and scale that are not possible for humans.
Detection of patterns and trends: AI, particularly machine learning, is effective in detecting patterns and trends in data. This means that AI systems are capable of detecting subtle changes in customer behavior or preferences that may not be obvious to the human eye.
Predictive Analysis: AI can be used to make predictions about future customer behavior. By learning from historical data, AI can simulate possible scenarios and help companies act proactively and adjust their strategies accordingly.
Sentiment Analysis: AI is capable of analyzing the sentiment behind texts, which is particularly valuable when it comes to customer feedback and opinions in social media. By identifying whether customers are happy, dissatisfied, or neutral, companies can take targeted measures to improve customer satisfaction.
Personalisation: AI allows creating personalized customer experiences by capturing the preferences and behavior of each individual. This can range from personalized recommendations to tailored marketing campaigns.
Automation of routine tasks: AI can automate routine tasks, like collecting and sorting data. This gives analysts more time to focus on more complex and valuable aspects of data analysis.
Cost Efficiency: While implementing AI initially requires investment, it can lead to cost savings in the long term by making processes more efficient and reducing the need for manual interventions.
Real-time Analysis: AI systems can analyze data in real time, enabling businesses to respond instantly to customer needs and requests, thereby enhancing the customer experience.

Another important factor for a good CX Analytics team is the collaboration of expertise and technology. The team should consist of experts from various fields such as data analysis, UX design and marketing and be able to work together to develop a comprehensive understanding of the customer experience. An open and collaborative approach enables the team to gain valuable insights and develop strategies to improve the CX.

In addition to technical know-how and collaboration the ability to convert data into business decisions is crucial. Team members should be able to translate their analysis results into clear and concise reports and presentations (CX reports) that help decision-makers make informed decisions.

CX needs to be a central part of business analytics and for your management and your stakeholders a central metric: for the vitality of the products. CX is not a buzzword but a method, a framework. If one speaks of positions next to clear skills that should come together in a company, then a CY Analytics team could be put together like this:

Your products must first and foremost meet the needs of the users as easily and low-threshold as possible. Low-hanging fruit one would suspect — easier said than done in practice.
The UX team (User Experience) has to always have its analytical ear with the users and derive hypotheses from this continuously on how to improve the operating concept and the user interface (UI) of the online shop, the physical product itself, or a website.
This includes not only the optimal positioning of “Buy” buttons and offering the most convenient payment methods. Also the written text (UX Writing) and its tonality, the visual language and the speed of the digital product are construction sites for the CX Analytics team.

Whoever believes that e.g. Online shops should all be built the same and there's no room for innovation or CX improvement, I recommend to them a look into the Research Repository. Here, large-scale studies on the subject of CX on E-Commerce platforms are collected and reviewed. CX Analytics speaks the language of the users and customers and looks at their behavior.

Your tools for holistic CX Analytics

CX Analytics tools are specialized software solutions that help businesses analyze and assess their customers' interactions and experiences with their products or services. These tools collect and process customer data from various sources like websites, mobile applications, social media, and customer feedback. The goal is to gain insights that can help improve the customer experience and increase customer satisfaction and loyalty.

Selection of CX Analytics Tools:

Sprinklr: A customer experience management platform that allows you to control over 21 social media channels, create and analyze brand experiences through direct and personalized customer communication.
HubSpot Marketing Hub: A marketing automation software that allows comprehensive, scalable, experience-oriented inbound marketing campaigns to be developed. The integrated analytics tools enable data collection, which helps to make informed and strategic decisions.
Contentsquare: An experience analytics platform that tracks micro-interactions of site visitors, from the first entering to leaving the website, and processes them into metrics and graphics. This provides the site operator with meaningful insights into the behaviors of the user and the opportunity to improve the customer experience.
Optimizely Campaign: Omnizely Campaign is a professional omnichannel marketing software that allows users to create, send, and evaluate campaign mailings. Omnizely Campaign seamlessly integrates with popular web analytics, e-commerce, and CRM systems.
Google Analytics: A widely used tool for web analysis that collects data on website visits and user behavior.
Adobe Experience Manager: A comprehensive suite of solutions, including analytics, targeting, and campaign management, aimed at creating personalized customer experiences.
Qualtrics CustomerXM: An online service monitoring tool to detect and screen IT infrastructure and applications.
Hotjar: A tool that captures and analyzes customer feedback in real time to gain deeper insights into customer satisfaction.
Medallia: A visual analysis tool that offers heatmaps, visitor recordings, and conversion funnels to understand the behavior of website visitors.

: A platform for the experience management. Here, customer feedback is collected and analyzed in order to improve business decisions.
A comprehensive tool for improving the CX does not exist. Instead, you need a carefully set up and tailored set of Analytics and Customer Feedback tools. How do you find out then which tools you exactly need? For this, it can be helpful to look at the entire funnel of your users. Because the funnel shows ideally every relevant touchpoint of the users with your product or platform. Every one of these touchpoints should generate valid data, which results in learnings, which then flow back into your product development cycle. Macro user tracking, like Google Analytics, is the start.

Here you can record the general acquisitions i.e., the flows from different media/sources to your product. So, you can observe the customer experience at its inception. Hotjar and similar monitoring tools give you the opportunity to be close up when your digital product is used by the users.

This is especially helpful to recognize inconsistencies and weaknesses in the user interface (UI) or problems in the technical implementation and to beat them promptly. Qualitative data, like session recordings are relatively time-consuming to evaluate.
How can you determine the value of an article or a video on your website, in relation to the conversion of your product? The French tool Contentsquare helps out here. It calculates based on user data how well individual elements on your website perform

and assigns a monetary value for it. Thus, after some time, a value in euros can be calculated for various banners or content. Super powerful, especially for decisions on image content, which unfortunately is often heavily influenced by personal tastes.A collection of powerful Customer Journey Analytics Tools

can also be found on OMR Reviews. Salesforce Sales Cloud
For the analysis, Customer Experience Analytics Platforms like can provide valid data. Customer Analytics Platforms often come with built-in user profile generators, like for example the Adobe Experience Manager. This kind of tools help you to create anonymized profiles of your users and generate learnings from this. Even if you can't generate databases with names and emails due to the GDPR regulation, you can, for example, gain insights from which you emerged as to why users are leaving your shopping cart alien (Abandoned Shopping Cart). Digital Customer Experience Analytics tools like Delve.ai automatically generate live personas from your user data

. These in turn show whether you are on the right track with your strategy and whether your already created personas — mostly created or heavily influenced personas by management, based on personal assumptions and wishes, match the real users.

CX Analytics and Chat GPT — the perfect symbiosisThe influence of Artificial Intelligence on Customer Experience Analytics, we have already discussed above. But how can the Open AI Chatbot Chat GPT

be used to support CX Analytics?ChatGPT, like other large-scale language models, can play an essential role

in improving CX Analytics tools. Here are some specific ways you can use ChatGPT:Customer service optimization:
ChatGPT can be integrated into chatbots to allow for a more natural and human-like interaction with customers. Through natural language processing, ChatGPT can understand and respond to complex queries, thereby increasing customer satisfaction and providing valuable insights for analysis.Feedback Analysis: ChatGPT can be used for the processing and analysis of open customer feedback. It can search through large amounts of text and extract relevant information to identify trends, sentiments, and key themes that occur in customer communication. Keyword Predictive Customer Experience Analytics
.Automated Summaries:
CX analysts often have to fight through huge amounts of data. ChatGPT can create automated summaries of customer conversations, surveys, and feedback so that analysts can quickly access the most important insights.Error detection and solution suggestions:
ChatGPT can be used as an intelligent support tool to identify frequently occurring problems and generate solution suggestions. This can help address recurring issues in the customer experience and increase satisfaction.Training and Support for Customer Service Employees:
ChatGPT can serve as a virtual trainer and assistant for customer service staff, helping them to better understand customer data and behavior and develop effective communication strategies.Predictive Analysis:

Although ChatGPT is primarily a language model, it can be used in combination with other AI technologies to make predictions about customer behavior. For example, it can help to create scenarios that show how customers are likely to react to certain products or services.Overall, the integration of ChatGPT into CX Analytics tools can help gain deeper insights that increase customer satisfaction

and enable more efficient and effective customer service. It is important to consider ethical considerations and ensure the privacy of customers is maintained.

The Guide to Converting Data into Concrete Improvements

Now you have the foundation and can better understand CX Analytics. Once you understand the subject, you quickly realize that the analytics team is the foundation for an optimal result.

As mentioned above, companies usually form their teams traditionally by department and thus in silos. Here, information loss can occur or important insights from the feedback of the customers can be lost. Insights, which for example are gained through the dialogue with the users in the phone support, through detailed user surveys directly on the website or in dedicated user tests, must find their long way to the desk of the UX designers.Even more:

The stakeholders and especially the financiers need to understand that only in this way, and not through assumptions and tastes of the decision-makers, the customer experience can be improved sustainably.

An example from e-commerce

An online shop is often reported to customer support for incorrect deliveries or delivery delays due to incorrect address entries. This can be triggered by a poorly designed/implemented address form (or also by technical errors in it). The customer support forms the starting point of the CX optimization, followed by the data analytics specialists, which gain through the user data from Google Analytics, Hotjar or Fullstory data. These outline the problem more precisely and together with the UX designers the core problems can be worked out. Then the UX unit sets to work to establish hypotheses for improvement of the Customer Journey

and develop concrete concepts.

On the way from recognizing a problem in the CX to optimization, communicative obstacles stand in the way of the product teams. Because not every stakeholder or executive can interpret the often quite complicated data floods. Interpretation scopes on the way to valid data additionally complicate the recognition gains.
The goal of a CX Analytics team, however, should not be to create acollection of user data and showcase it in a gallery, e.g. Tableau, so that leaders can check off “Analytics”. The goal must be to generate real value from the data obtained.

Only through a clean division of tasks will data lead to the team being able to continually improve the customer experience. CX forms a bracket between analysis and design (UX design). The iterative design process is thus, as far as input & outcome is concerned, round.

Predictive Customer Experience Analytics: Looking in the rear-view mirror to predict the future","When your CX Analytics team is well set up and you are making noticeable improvements to your CX, you can now also apply Predictive Customer Experience Analytics (PCEA). PCEA is an analysis process of your data to make predictions about how your customers will behave in the future. PCEA combines Customer Experience (CX) and Predictive Analytics (PA)

to enable informed decision making with regard to future interaction with customers.PCEA uses, like CX Analytics, customer feedback, interaction data, and behavior patterns. Only with PCEA, you can predict how your customers will react in the future. So you can identify and fix problems in the customer experience before they occur. Here you can, for example, use the Gainsight platform

Customer Journey Lead Management

Author

Dan Wojcik

Dan Wojcik ist Gründer und CEO von muse case (Stuttgart) und muse case labs (Berlin). Als erfahrener UX-Designer und Spezialist für E-Commerce und Service Design setzt sich Dan täglich mit nutzerzentrierter Produktentwicklung und agilen Arbeitsweisen auseinander. Crossfunktionale Teams aus Entwicklern und UX Designern arbeiten in seinem Team bei der muse case GmbH eng miteinander zusammen. muse case ist in einer Vielzahl von Projekten, aus den Branchen Automotive, Real Estate, Logistik etc., als Spezialist für UX/UI-Design und Software Engineering, vertreten. Die muse case labs GmbH in Berlin ist ein Tech-Education-Accelerator, das Nachwuchs-Spezialisten in den Bereichen UX/UI-Design und Software Engineering ausbildet. Es ist zertifizierter Bildungsträger.

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