What is customer intelligence (CI)?

Customer intelligence (CI) is the insights you gain from gathering customer data and feedback. It helps you make informed business decisions to improve customer experience.

What is customer intelligence analytics?

Customer intelligence analytics is a two-step process of collecting customer data and drawing insights from it. It’s the plan to ensure you collect detailed customer data during all customer interactions.

Benefits of customer intelligence analytics

Customer intelligence analytics enables businesses to:

• Make data-driven decisions with the collected customer data.

You can look at transactional and behavioral data to view purchase history, feature usage, and more. See what plans are most popular among different customer segments. Or check out which features customers use the most. All this data will help you gain insights into what you should double down on.

• Personalize the customer experience.

Customers expect personalized services and product experiences. With customer intelligence analytics, you can segment customers based on their shared characteristics, such as their jobs to be done. With these segments ready, you can deliver personalized content that helps customers achieve their goals and gain value from your product.

• Improve customer satisfaction and increase customer loyalty.

Gathering customer intelligence data improves customer loyalty and satisfaction because you’re in a position where you can better meet their needs. With a mix of customer activity data and customer feedback, you can improve the customer experience and remove areas of friction.

• Forecast customer behavior.

Customer intelligence data also helps you notice at-risk customers who are inactive or unhappy and are likely to churn. With this data, you can proactively retain them by offering 1-on-1 help or triggering in-app product guidance.

What are the four main customer intelligence data types?

There are four main types of customer analytics:

• Demographic data

Demographic data is information about a customer’s age, gender, income bracket, company size, location, etc. You can gather most of this information by triggering a welcome survey for new users.

• Psychographic data

This data tells you about a customer’s interests, challenges, values, and thoughts. These insights usually are gathered through surveys and interviews.

• Behavioral data

Behavioral data is inferred feedback about a customer’s interactions with your brand. This is information such as what features they use, how often they log in, and the paths they take from step to step. Collect behavioral data through product usage tracking, session recordings, heatmap analysis, and more.

• Transactional data

Transaction data tells you everything about a purchase – the purchase date and time, location, what plan was purchased, payment method, and more.

How to automate your customer intelligence analytics?

Automating your customer intelligence analytics makes gathering actionable insights faster, easier, and cheaper. Here are three ways to automate the process:

Collect different types of data

The customer intelligence process starts with data collection and customer data management. Collect data across multiple channels to build the most comprehensive customer profiles.

You can collect data with customer feedback – both direct feedback and indirect feedback. To collect immediate feedback, run in-app surveys. To collect indirect feedback, monitor social media platforms and third-party review sites to see what customers say about you unfiltered.

You should also collect product usage data to run feature audits and see how customers interact with your product.

Filter all of your data into a customer relationship management platform. This ensures customer data is always up-to-date so you can take action.

Analyze data using different tools.

Multiple business intelligence tools on the market help you gather and analyze data.

Different tools work for different use cases. For example, marketing teams must use a separate customer analytics tool from product teams.

Here are some of the features that these tools have to make data collection easier:

• Micro-segmentation
• Customer lifetime value forecasting
• Customer behavior modeling
• Predictive insights
• Machine learning
• Natural language processing

Act on data collected from various client intelligence sources

After collecting and analyzing data, it’s time to act on intelligent customer insights.

Use your customer data management tool to categorize your insights and act on them. Keep reading to learn seven use cases for the collected data.

Customer intelligence analytics use cases: How to act on collected data

Data is only valuable if you act on it. Make data-informed product improvements with these seven methods:

Perform behavioral segmentation

Behavioral segmentation helps you improve customer experience with personalized content.

Segmentation involves grouping customers based on shared behavioral characteristics. These could be features they engaged with, how often they logged in, their lifecycle stage, and more.

You can then trigger personalized content to different user segments based on their needs.

Make changes to your customer journey maps.

When you collect data across your customer journey maps, you can pinpoint areas that need improvement.

The data can show you where users need more guidance and what path they are choosing to complete specific tasks.

You can use that data to make changes to your customer journey map. For example, if there’s a friction point where a high percentage of users drop off or get stuck, prioritize fixing it.

Build personalized customer experiences.

Customer intelligence insights help you define your segments and create personalized customer experiences for each customer group.

Based on their answer, you can personalize the onboarding flow to meet their needs. They’ll achieve value faster and be more likely to stick around.

Build data-driven loyalty programs.

Loyalty programs are one of the best ways to improve satisfaction and grow customer lifetime value. Customer insights help you better engage with customers and get them onboarded into the loyalty program.

Using customer data, pinpoint who your power users are so you can ask them to join the loyalty program. You can also use this opportunity to reward loyal customers with a small gift.

Streamline your marketing efforts to acquire new customers.

Identifying your power users (or promoters) also helps your marketing team acquire new customers. Here’s how:

Check out the primary features power users use, and with this data understand the behavioral patterns of power users. This is your best custom fit. Adjust your messaging on your landing page to attract similar customers and improve lead quality.

Predict churn and build effective customer relationships.

Churn happens when an unsatisfied customer cancels their account. It’s the enemy of product growth. Make it a top priority to prevent churn.

Luckily, you can use customer intelligence analytics to predict churn before it happens. Product development teams use this data to spot product friction with the help of behavioral data.

You can spot churn patterns among your existing customers and reach out to them proactively to engage them and build effective customer relationships.

For example, segment customers based on low activity – if they haven’t logged into the app in over 7 days. Then, trigger an email campaign to win them back with additional product guidance and encouragement.

Prioritize further product improvements.

There are always a million and one things to do to improve your product. You can only do some. That’s why you need a system to prioritize product improvements – customer analytics can help you do that.

Insights give you an overall idea of customer needs and what features you need to work on to meet their needs. Then you can update your product roadmap accordingly.

For example, ProdCamp asks users to “upvote” which new features they want to see next. This helps ProdCamp prioritize its product roadmap and meet customer needs.

How Userpilot facilitates analyzing customer interaction data

Userpilot is a product growth platform that helps product teams collect and act on customer analytics insights. Here’s how:

Track product usage with product analytics

See how users and different segments interact with your product. Userpilot’s product analytics feature helps you make informed product decisions based on usage data.

You can see product usage data for multiple categories, including onboarding, marketing, engagement, revenue, and more.

Identify behavioral patterns

Custom event tracking allows you to set goals and measure how many users are reaching those product milestones.

Find positive behavioral patterns and then turn those customers into loyal customers.

Or find negative behavioral patterns to reengage these customers to prevent churn.

Understand drop-offs in the funnel.

You can also set goals according to your activation points. This is the point in the product journey where customers achieve value for the first time. It’s different for every product.

For example, email marketing software may set their activation point to when customers send their first email.

Identify drop-off points in the user funnel on the way to activation. Then fix them to boost conversions.

Conclusion

Customer intelligence analytics is a fantastic tool to improve customer experience. Collect data across all customer interactions and then act on those insights. It’s the best way to build a better product that meets customer needs.

Want to get started with collecting customer intelligence analytics? Get a Userpilot Demo and see how you can collect customer feedback, track product usage, and build in-app product experiences.