A Guide to Customer Analytics Tools - Mixpanel
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A Guide to Customer Analytics Tools

Customer analytics software help teams understand how customers interact with their digital products. Without analytics tools, marketing, product, analytics, and customer service teams can’t know how to improve their service, either by making it simpler or by offering features users want. Learn how to use analytics software to create satisfying experiences that keep customers returning.

Jenny Booth
Employees analyzing customer behavior with customer analytics software.

Why customer analytics tools are key to building great services

Customer analytics tools and software help teams build more useful and satisfying products by offering insights into what customers want. Teams use these tools to analyze their users individually or in aggregate to see how they behave and to plan actions the team can take to nudge users to use the product more often. Analytics also helps teams focus on their most desirable customers. With data to profile users by their demographics and tie it to their behaviors such as signups, purchases, and repeat visits, teams know which customer segments are the most profitable and can monitor them over time, a process known as creating customer cohorts. Teams can then focus more time and attention on improving the product for profitable users and less time trying to acquire and keep customers that are likely to quit the service or cost extra resources to onboard. Customer analytics software can increase:

Of all customer-focused KPIs and metrics, CLV is the most important. It’s a prediction of the value of a customer over their lifetime and is influenced by nearly every other customer metric, from usage and retention to purchases and satisfaction. Teams that measure and improve their CLV earn more revenue per customer, keep customers for longer, and build a more sustainable business.

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Types of customer analytics tools and software

No two customer data analytics tools are exactly alike. Many offer similar feature sets that partially overlap. For example, many companies use both a marketing automation tool and a user analytics tool, both of which track users’ behaviors as they navigate a site or app, but each of which has distinct capabilities. Teams that want to increase their CLV typically need a wide range of features that no one tool can provide—from tracking users to scoring leads and identifying bugs—and so use a customer analytics stack, or cluster of tools. Customer analytics tools can provide:


Segmentation tools provide a visual reporting interface where teams can divide their customers into groups, or segments. The more types of data the tool can segment users by, the better the team can understand and serve those users. For instance, a tool that only allows teams to select customers by their demographics—age, location, gender, or income—but not their behaviors—clicks, page views, and signups—make it difficult for teams to identify which string of actions indicate that a customer is ready to buy. The most useful tools allow for segmentation by many types of data. Common types of data:

  • Demographic
  • Behavioral
  • Psychographic
  • Technographic
  • Firmographic

Teams use segmentation tools to treat different groups of customers differently. For example, power users, daily visitors, or users acquired through a specific marketing campaign each have unique needs. Teams can identify which users are the most desirable—say, because they bring in the most revenue or cost the least to support—and focus their product, marketing, and retention efforts there. Examples of segmentation tools:

Customer satisfaction

Teams use CSAT tools to ask questions of customers, and to sample their thoughts and experiences. The CSAT tool category includes survey tools, in-app messaging and notification tools, and Net-Promoter Score™ (NPS) tools. Examples of customer satisfaction tools:

Marketing funnels and customer journeys

Marketing funnel tools allow teams to track strings of actions, also called user flows, or customer journeys. By looking at how individuals and groups move through the app and whether they reach their desired destinations, teams can approximate their satisfaction with the service. A SaaS team, for instance, can define a flow to measure the percentage of users who enter the app and send an email. If there are a significant number of drop-offs, or areas where users exit the app without accomplishing their goal, it indicates a hotspot where users run into trouble. Examples of marketing funnel and customer journey tools:

  • User analytics such as Mixpanel
  • Marketing automation such as Marketo

Acquisition, retention, and churn

Acquisition, retention, and churn monitoring tools help teams see how users discover the service, how they sign up, and whether they stick around or churn. By measuring customer acquisition, marking teams can calculate their return on investment (ROI), and the return for individual channels or campaigns to understand which emails, say, drove the most pipeline. By measuring retention and its inverse, churn, a news site can see what percentage of users continue to visit the site after three weeks. If they create a segment for users that exhibit unusually high retention, they can analyze those users’ demographics and behaviors to understand how the team can attract more of the same type of reader, or encourage existing readers to act more like them. Examples of customer acquisition, retention, and churn tools:

Customer support

Teams use customer support analytics tools to act upon their insights. The support tool Zendesk, for example, offers an integration with the user analytics platform Mixpanel so customer support agents can understand the behaviors customers took before opening a support ticket. Support teams with Zendesk and Mixpanel can identify common customer issues and their causes to close tickets faster, use notifications to help customers find quick answers, and collaborate with the product team to streamline user flows. Examples of customer support tools:

  • Customer support platforms such as Zendesk
  • Live chat messaging tools such as Intercom

To simplify their customer analytics software stack, most teams select one primary analytics tool with lots of integrated partners and add additional tools as needed in a hub-and-spoke model. Beyond the ease of having one interface to learn, these teams are able to view consistent datasets and find their measurements vary less from platform to platform. Customer analytics tools and software give teams the tools to understand how to acquire and retain more customers. Teams can measure user behaviors, sentiments, and journeys to understand the factors that influence CLV, and adjust their product to keep more of those customers for more of their lifetime.

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