What is customer analytics: Use cases, benefits, best practices

For companies built around a product-led growth (PLG) strategy, the importance of customer analytics is no secret.

For companies built around a product-led growth (PLG) strategy, the importance of customer analytics is no secret. These insights drive product development, align marketing efforts, and ultimately boost the bottom line.

What’s less obvious is which approach you should take to acquiring customer data insights. Consumers are already telling you what they want, need, and enjoy. But compiling information from across channels in a way that is actionable and aligned with your objectives isn’t always straightforward.

That’s why, in this article, we look at customer analytics examples, considering general best practices and specific use cases. We also look more closely at exactly what customer analytics is in 2024.

What is customer analytics?

Customer analytics is the process of collecting and analyzing data about customers to inform business decisions and strategies.

It incorporates insights about user behavior, preferences, demographics, and more. The best customer analytics will integrate data from across channels to provide a more complete picture of the customer journey.

This process typically follows these stages:

Data collection

This involves mining data from relevant sources such as your website, product, and transaction records. There are dozens of tools you can use for this, from Google Analytics to Hubspot and more.

Data organization

A customer data platform (CDP) will organize your data and direct it to the right place for analytics.

Data storage

This is the process of structuring and managing data to ensure that it’s secure and usable. When you reach a certain stage of customer analytics maturity, you’ll need to store your data in a warehouse to consolidate data from various sources. You can then sit the product analytics platform, Optimizely Warehouse-Native Analytics directly on top to bring data from outside your product right alongside product data.

Data segmentation or advanced analytics

Segmentation involves dividing the collected data into meaningful groups based on common characteristics or behaviors. Advanced analytics takes this a step further with techniques such as clustering, regression analysis, and text mining.

Visualization

This refers to the process of presenting the analyzed data in visual formats such as charts, graphs, and dashboards. Optimizely Warehouse-Native Analytics automates this step to present you with clear, actionable insights.

Data modeling

Customers at a higher customer analytics maturity level might use this technique. This is where machine learning is applied to data to develop predictive models or uncover patterns, relationships, and trends.

Iterative analytics

Customer analytics is an ongoing process and businesses will continually research and test hypotheses to understand changing customer behavior.

There are four main sources of customer analytics. These are:

Website customer analytics. These are gathered from interactions with your site.

Transaction-related customer analytics. These are financial insights such as payments, transactions, and purchases.

Product customer data analytics. This is data that is acquired in-app.

Customer-created analytics. This includes insights such as reviews and feedback to customer support.

Benefits of customer data insights

Customer data analytics allows you to understand customer behavior. This means you can get answers to questions about acquisition, revenue, engagement, and retention. These insights can then be used to inform every stage of operations, from product development to marketing to customer service.

For example, you might start a customer data analysis by asking, “Which channels drive the most new customers?” From here, you can use a product analytics platform like Optimizely Warehouse-Native Analytics to track customer acquisition across platforms like your website, LinkedIn and YouTube.

The result of these answers often leads to more revenue. With more data-driven marketing, you can segment audiences based on more detailed attributes, personalize campaigns, promote upsells more effectively, and create a consistently strong customer journey.

You can also boost engagement with existing users by supporting product and customer service teams to understand what creates a great experience. In a 2022 Gartner study, 84% of customer service leaders said that customer analytics are “very or extremely important” for achieving their organizational goals. And it makes sense — treating problems proactively and providing support where needed helps limit churn.

Examples of customer analytics for different use cases

In this section, we look at how the process and findings of customer data analytics can look for different parts of a PLG company.

Customer analytics for product teams

Product teams can use customer analytics to make data-driven decisions to improve an app's features and overall user experience. They can also identify trends to anticipate users’ future needs and stay ahead of the competition.

To take an example, imagine that the team behind a productivity-related app notices that a growing number of users access the app during the weekday evenings to use time management features. They could then introduce a new tool that allows users to schedule tasks for the following day and analyze how this segment responds.

Customer analytics for marketers

PLG marketers use customer analytics to gain insights into customer behavior, desires, and needs. This can help them make more personalized marketing campaigns and ensure a seamless brand experience across channels.

Using customer analytics to segment an audience and build a personalized campaign could look like this:

The team segments its audience to find users who often interact with a particular feature.
They send push notifications and in-app banners tailored to this audience promoting a similar, paid feature.
They track metrics such as open rates for push notifications and click-through rates on in-app banners.

With insights from across channels, such as those provided by a platform like Optimizely Warehouse-Native Analytics, marketers can take this a step further. For example, if an analysis reveals that users who engage with a particular feature in the app also interact with related content on social media, marketers can leverage this insight to develop a cross-channel campaign.

They can also provide a cohesive brand experience across all touchpoints, reinforcing the app's value proposition and strengthening brand loyalty.

Customer analytics for customer service

By leveraging customer analytics, customer service teams can uncover which areas of the customer experience typically cause issues. Armed with this knowledge, they can then proactively address pain points and streamline processes.

The most obvious example is a high rate of feature abandonment. As well as informing the product team’s process, this could highlight the need for targeted tutorials or help resources to guide users.

Customer analytics for business owners

Ultimately, customer analytics can help business owners guide their teams toward better user engagement and retention. By uniting teams inside a single product analytics platform, they can ensure seamless collaboration in line with the customer journey.

In the example of the productivity app from earlier, imagine that customer analytics showed that users often only use the app for short periods before abandoning it entirely. The product team could then collaborate with the marketing team to create a series of onboarding tutorials and feature guides within the app. The customer support team could then monitor user feedback and inquiries related to the onboarding process to ensure that it is answering all queries proactively.

How to analyze customer data: Customer analytics best practices

In this section, we’ll explore the steps that allow you to get the best results from your customer data analytics.

Define clear goals

Each time you run customer data analysis, you need to go in with a clear goal. This might be to identify which channels are giving you the best marketing results, for example, or to identify areas of your app where users are dropping off.

Inside Optimizely Warehouse-Native Analytics, unlike other product analytics platforms, you can conduct free, exploratory analysis. However, it’s still important to go in with a hypothesis to test as this ensures that you are focused on uncovering actionable insights and helps prevent bias.

Centralize data

It’s key to bring customer analytics from across channels together in one solution. This allows you to easily get a more accurate, complete picture of the customer journey across channels. What’s more, recent studies show that centralizing customer data can improve efficiency by up to 67%.

By choosing Optimizely Warehouse-Native Analytics, which sits directly on top of your data warehouse, you avoid the hassle of dealing with data silos and you don’t need to risk the inaccuracies that come with using reverse-ETL tools or transferring ETL data out of your analytics platform.

Dedicate a team of managers

While many teams will work together using the data inside your analytics platform, it’s useful to dedicate one team of people or a product manager to organize processes. If you don’t do this, you might end up with data that is stale, incomplete, or unstructured; this can lead to misleading answers.

Iterate and test

Taking an iterative approach to analyzing customer data insights allows you to respond to changing customer expectations and desires. In a PLG environment, this means staying ahead of the competition.

Choose Optimizely Warehouse-Native Analytics to iterate and test with ease. Your teams can access self-serve reporting templates for event segmentation, funnel, path, retention, impact, and more. And the intuitive UI doesn’t rely on SQL querying.