Customer data can tell businesses a lot about how people interact with their brand. When that data is brought together and analyzed properly, it can help teams understand what customers need, where they run into problems, and what can be done to improve their experience.

With customer data analytics, AI, and connected customer information, businesses can turn scattered data into useful customer insights that teams can act on. This can also help create a clearer, 360-degree view of each customer and make customer journeys more relevant and personalized.

In this article, we’ll look at how businesses can turn customer data into actionable CX insights and use them to improve customer experiences.

Key takeaways

• Learn why setting clear goals first decides which customer data analytics is worth doing at all.
• Understand how data silos and poor data quality quietly undermine customer experience analytics.
• Discover how AI turns messy customer data into streamlined customer insights a team can act on.
• Learn what it takes to build a 360-degree customer view, and where a customer data platform helps.
• Explore how customer journey analytics and customer insights drive personalization and loyalty.
• Understand how to turn insights into campaigns you can measure and improve.

Customer data is the lifeblood

Ranging from contact center interactions, demographic information, and sales metrics, CX leaders regularly harness their customer data to deliver personalized connections and drive their digital transformations. In Forrester’s CX budget planning guide, 40% of CX leaders planned to increase overall CX investments above inflation over the coming months.

However, simply having a wealth of customer feedback doesn’t guarantee success, as the abundance of information can make it more difficult to find the best insights.

From ensuring the right data is easily accessible to maximizing its use with technology, follow the step-by-step process shared below to draw the full CX potential out of your organization’s data.

Define your goals and objectives

Before CX teams can successfully analyze and employ any customer data to distill valuable insights, they must first clearly define what information they need and how they then want to use that information to make meaningful changes to their organization’s CX offerings. For example:

• Increasing personalization through identifying customer behavior patterns
• Optimizing operational efficiency by detecting potential bottlenecks in services
• Developing or modifying services based on customer demands and market trends

Having well-defined goals in advance not only provides needed direction for an in-house CX team or an external strategic partner but also means that they know the correct customer data to utilize within their models. Additionally, it helps ensure that any AI tools used in the analytical process are trained with relevant data, allowing them to give more accurate insights for current and future goals.

How AI turns customer data into actionable CX insights

AI can help CX teams make sense of large volumes of structured and unstructured customer data without having to manually work through every interaction. It can identify patterns across customer conversations, transactions, website activity, feedback, and other touchpoints, helping teams spot issues and opportunities faster.

For example, customer experience analytics can show that customers are abandoning a particular step in the buying journey. AI can then look across different data sources to help identify what may be driving that behavior, such as a complicated process, repeated errors, long wait times, or inconsistent support.

This turns customer data into AI-powered customer insights that teams can act on. Instead of only knowing what happened, CX teams can get a clearer picture of why it happened and what they could do next.

AI can also help with:

• Identifying recurring customer complaints and friction points
• Detecting changes in customer behavior
• Analyzing customer sentiment and intent
• Predicting potential churn or other customer outcomes
• Finding patterns across different customer segments
• Recommending relevant next steps based on customer behavior

This workflow shows how AI analyzes customer data to help CX teams understand what happened, why it happened, and what they can do next.

Ensure the quality and availability of your data

Once a target is set, the next challenge is ensuring your customer data is up to par. A recent IBM survey of 1700 chief data officers found that 83% say data silos hinder innovation and impede real-time analytics and decision-making.

Usually occurring in back-office operations, data silos are the result of teams storing customer data in separate locations, preventing the entire organization from receiving a holistic view of its operations. These silos often lead to poor data quality, with duplicate or outdated entries skewing analytics.

Adopting an omnichannel approach will help centralize this information for employees, while also offering a more interconnected experience for customers. to centralize data and provide a seamless experience for customers and employees. Integrating automation solutions like Roboworx can further improve the accuracy and quality of data while also freeing up staff for higher-value tasks.

Utilize the right models and tools

With the right customer data aligned to your goals, AI and Machine Learning (ML) tools can input this data into analytical models for actionable insights.

For example, if a CX team at a telecommunications company is tasked with investigating a surge in customer dissatisfaction, they can use a diagnostic model to find the root cause of this issue through past data. Then to predict any potential dips in approval in the coming months, they can rely on a predictive model that leverages current or real-time data.

AI and ML tools enhance efficiency by processing this data quickly, and generative AI is set to revolutionize this even further. Greatly improving the accessibility of data analytics, the self-learning nature of this technology provides CX teams with more relevant insights through auto-generated summaries and visuals, as well as recommended next steps to take.

Building a 360-degree customer view

Customer data often sits across multiple systems. CRM platforms may contain customer profiles and purchase histories, while contact center systems hold interaction data. Websites and apps generate behavioral data, and marketing platforms capture campaign engagement.

Looking at these sources separately makes it difficult to understand the complete customer journey.A customer data platform can help bring relevant customer information together, creating a more connected view of each customer. When combined with customer data analytics, this gives teams a clearer understanding of how customers interact with a brand across channels.

A 360-degree customer view can bring together information such as:

• Customer profiles and preferences
• Purchase and transaction history
• Website and app behavior
• Customer service interactions
• Campaign and communication history
• Feedback, reviews, and survey responses
• Customer sentiment and engagement patterns

The value comes from connecting these signals. For example, a customer who contacts support after visiting a product page shouldn’t be viewed as two unrelated interactions. Connecting those events can give the business a better understanding of what the customer is trying to accomplish and where they may be facing friction.

This connected view also makes it easier for different teams to work from the same customer information rather than creating separate versions of the customer journey.

Building a 360-degree customer view

Using customer insights to personalize the customer journey

Personalization works best when it is based on what customers actually do, not just what you assume they want. Customer insights can help brands understand preferences, behaviors, purchase patterns, service issues, and engagement across different touchpoints. These insights can then be used to make interactions more relevant.

For example, a customer who regularly purchases a particular product may respond well to recommendations related to that product. Similarly, a customer who has contacted support several times about the same issue may need a different experience from someone making their first inquiry.

Customer journey analytics helps connect these interactions across the customer lifecycle. Instead of looking at individual touchpoints in isolation, businesses can see where customers are progressing smoothly, where they are dropping off, and where additional support may be needed.

This can help teams personalize:

• Product and content recommendations
• Marketing messages and offers
• Customer support interactions
• Onboarding and retention campaigns
• Self-service experiences
• Follow-up communications

Turn your insights into action

Once AI-powered models uncover insights, it is time to translate them into action. Additionally, tracking key performance indicators (KPIs) is essential for helping refine these data-driven campaigns. Passing along any insights and results to other departments is also important to ensure processes across the rest of the organization can see similar improvements.

For instance, eClerx helped one of the world’s leading banks solve an increase in customer churn. The team used AI-powered analytics models to explore why users stopped engaging on the client’s website, and devised several hyper-personalized campaigns to slow the churn. They tracked campaign performance, making changes when necessary to boost engagement. As a result, the client saw an increase of nearly $130,000 in revenue in just the first few months.

Build your customer data strategy with eClerx

Using customer data to plan out your company’s future is something many organizations are striving to accomplish, but it can be easier said than done.

Click below to get in touch with us and learn more about how eClerx’s Analytics teams and award-winning automation products can help you achieve your business goals.