The CX Network guide to customer data, insights and analytics

There are active and passive ways of gathering the customer data that can inform  insights and analytics. 

Behavioral data

This involves analyzing customer actions, such as purchasing history, website interactions, and social media engagement. Understanding how customers behave can help businesses identify trends and opportunities.

Demographics and segmentation

Customer insights often involve segmenting customers into groups based on demographics like age, gender, location, and income. This segmentation allows businesses to target specific customer groups with tailored strategies.

Customer journey mapping

A customer journey map is a visualization of when and how customers come into contact with a company both on and offline. It can help create consumer insights by deepening a business’ understanding of customer needs, pain points and the best touchpoints to use certain technologies, such as self-service systems or automated interactions.

By mapping out the customer journey, brands can begin to understand not only who they are designing their services for, but what is holding back current user experiences, therefore creating useful insights that can be used in future CX projects.

Contact center enquiry mining

Contact centers are moving beyond their traditional role as a resource to troubleshoot user issues and are instead becoming a key component of CX strategies. They are also a data-rich resource that companies can utilize to gain customer insights.

By mining enquiries at contact centers for data from interactions, companies can glean useful information about customers.

Feedback and surveys

By inviting customers to complete surveys, companies can identify trends in the experiences of many customers, which provides a range of insights to build CX strategies.

By evaluating survey results with analytics technologies, companies are able to identify key trends at scale. These insights can then contribute to building a better idea of the customer and their digital behavior, thus contributing to future CX initiatives.

These customer data sources can be largely grouped into the following categories:

Zero-party data

This is the personal information customers willingly share with a business, for example through survey responses, product or communication preferences and account customizations. 

First-party data 

This is the information an organization collects direction from its customers, for example through web behaviour and first-party cookies, purchase history, email interactions, service and support interactions and loyalty programs. 

Third-party data 

This is the information on customers that a business purchases from third parties, for example audience segments for specific marketing campaigns. However, third-party data can be divisive. Some say it is too easy to acquire unvetted or untrustworthy data, while others argue that is can enhance the first-party data already held and help to fill in gaps and add multi-dimensional insights.

Cookies 

The rules around third-party cookies, specifically in Google Chrome, will change in 2025, as a new user choice model is introduced. This will allow users to opt-in or out of third-party cookies, while the Privacy Sandbox APIs will still be available to support privacy-preserving advertising solutions.

In their place, organizations are encouraged to use first-party cookies, which are set by the domain a user is visiting and are used for tasks like remembering login details or items in a shopping cart.

As outlined in this article by Annette Franz, there are many ways organizations can utilize the customer data they hold. These can include but are not limited to personalization, recommendations, product upgrades, journey orchestration, marketing campaigns, brand advocacy, relationship building, decision-making and social media engagement.

Customer data is also increasingly being used for modelling new solutions and CX optimizations. At CX Network Live: Predictive CX 2023, Ruchika Singh, director of data science and insights at Spotify, discussed how the streaming giant has leveraged predictive modelling practices as part of controlled experimentation and assessment aimed at informing value-based decisions and improving customer journeys. 

Australian supermarket Coles took a data-driven approach to journey and experience improvements that allowed the brand to create seamless, personalized interactions, while anticipating customer demands. The approach also helped management prove a hunch about where the customer’s experience may be going wrong, leading to a drastic change to the self-checkout experience. 

Watch Coles’ national CX manager Claire Cunningham explain the strategy – and results – in this video:

While this case study from Coles highlights how data can be used to prove a hypothesis, the insights gleaned from data analytics can also be used to proactively inform change.

Alex Hardy, consumer insight and analytics director for Nomad Foods, is a specialist in using market analytics, brand equity research and trend forecasting to understand what drives consumer choices, then translate these insights into actionable strategies

Here he is talking to senior event producer Chloe Chappell during All Access: AI + Data in CX 2024 and explaining how Nomad Foods navigates a complex data landscape to uncover valuable insights that inform everything from product development to media spend optimization.

When CX Network conducted its 2025 research into the Global State of CX, data emerged as a top trend and area for investment, but the results also confirmed challenges around data collection. 

When we asked about customer behaviors, 39 percent agreed that customers refuse to share the correct personal data with retailers, or they use guest checkout options online to protect their personal data. A further 10 percent strongly agreed while 19 percent disagreed. 

Data also appeared three times in the list of top 10 challenges facing practitioners in 2025. Survey respondents reported that challenges exist around creating actionable insights from data (selected by 16 percent of respondents) in addition to siloed (13 percent) and insufficient customer data (10 percent). 

There are many reasons practitioners experience challenges when extracting insights from data.

Cunningham says: “The challenge is getting people excited about data and, in that respect, it becomes really important to find common ground, which I think also helps with creating actionable insights – helping people to understand what your purpose is and what your aims are with that data.”

At Nike, storytelling is used to create meaningful customer relationships and deepen engagement. The video below features Donna Orman, senior director of customer loyalty for Nike in the EMEA region, explain how internal research is used to drive deep, authentic connections with the brand by delivering valuable content to consumers.

Use cases like this demonstrate that no amount of data will spur action without explaining the human impact behind the numbers. 

“People don't always understand what data can speak to customer behavior,” Cunningham says. “Being a little bit more creative in terms of what you interpret as customer data is essential. Oftentimes, practitioners think they don’t have an answer to a particular question because that question wasn’t directly put to the customer, but we can still answer the question from behavioral data,” she adds.

AI can also help organizations to overcome the challenges around customer data insights and analytics. Paired with machine learning (ML) AI is making data analysis faster and easier to perform than ever before. Natural language processing (NLP) enables AI systems to understand and analyze unstructured data, such as customer emails, chat transcripts, and social media posts.

Here are a few ways AI contributes to acquire customer insights:

• Predictive analytics: AI can analyze historical customer data to predict future behavior and preferences. It can identify trends and patterns that help businesses make informed decisions, such as predicting which products a customer is likely to purchase next or when they might churn.
• Customer segmentation: AI can segment customers into different groups based on their behavior, demographics, or other attributes. This segmentation enables businesses to tailor their marketing strategies.
• Personalization: AI-driven recommendation systems use customer data to provide personalized product or content recommendations. This enhances the customer experience and increases the likelihood of making a sale.

Despite the value of data to an organization, customers are not always willing to share it. There are many reasons for this, from data breaches or historic misuse of customer data, but practitioners are united in the belief that transparency and governance can help earn customer trust. 

This is achieved by showing customers how and why their data are being used for CX and service, particularly in industries such as financial services. As Ekaterina Mamonova, former global lead for marketing and CX at Allianz Commercial explains in this video from All Access: Customer Insights and Data Analytics 2024, transparent communications play a crucial role in building and sustaining trust with both brokers and end customers.

If you still have questions around data and analytics catch up with this session from All Access: Customer Insights and Data Analytics 2024, which features Jaakko Lempinen, chief customer and portfolio officer for Finland’s national broadcaster Yle and a CX Network Advisory Board member, answering FAQs from our network members.