The Ultimate Guide to Product Data Analytics for PMs

Product data analytics and product management are inseparable. Instead of relying on their hunches and anecdotal evidence, product teams should leverage data to make informed decisions.

Our ultimate guide to product data analytics explores:

• The most relevant metrics teams should track.
• Various kinds of analyses that can benefit your processes.
• And, how to analyze product usage data to enhance the product experience.

We also look at various tools that can help you harness the power of analytics to drive your product growth.

What is product data analytics?

Product data analytics is the process of collecting, curating, and analyzing qualitative and quantitative data about user interactions with the product.

Its aim is to assess the performance of the product and gain a deep understanding of user behavior in order to optimize the product experience for their needs and preferences.

To achieve this, product analytics uses a set of metrics, tools, techniques, and data from various sources, like product usage data or customer feedback.

Marketing analytics vs product data analytics

Product data and marketing analytics focus on different aspects of the customer journey.

Product data analytics focuses on product performance and user behavior while inside the product. It analyzes product usage, engagement, and adoption in order to improve its functionality, usability, and overall user experience.

Marketing analytics focuses on customer acquisition, conversion, and retention. Its goal is to measure the effectiveness of marketing efforts, optimize campaigns, and improve the marketing ROI.

Why is tracking product analytics important?

There are a number of reasons why teams should invest in product analytics. Here are the most common benefits.

• Evaluating product performance – by tracking adoption rates, usage patterns, or customer satisfaction, teams can assess how well the product satisfies market needs.
• Data-driven processes – product data enables teams to make informed pricing, prioritization, and strategy decisions, which improves their chances of success and reduces risk.
• Identifying user needs and wants – by analyzing behavioral data, teams can get a more complete and objective picture of user preferences or pain points.
• Improving customer satisfaction and loyalty – addressing user needs and problems and accommodating their feedback drives customer satisfaction, which translates into greater customer loyalty and retention.

Which roles benefit from monitoring product analytics data?

Product data analytics benefits teams from across the organization.

• Product managers and product teams use analytics insights to make informed decisions about product development, prioritization of features, and overall product strategy. It helps them identify trends, opportunities, and areas for improvement.
• Product marketing managers use product analytics to create targeted marketing campaigns or refine product positioning and messaging to reflect customer preferences and behavior.
• UX designers leverage product data analytics to understand how customers engage with the product. Such insights allow them to create frictionless experiences and drive engagement.
• Customer success managers use product analytics data to better understand customer needs, preferences, and challenges. Thanks to that, they can proactively address any potential issues and provide personalized support.
• Software developers benefit from product data by gaining insights into how the product is performing. They can use the insights to identify any potential issues or bugs.

Important product analytics metrics to track

Which product metrics should you track?

There’s no simple answer to the question as it all depends on your role and objectives. Here are a few to consider.

• Activation rate – the percentage of users who have reached the activation stage. It allows teams to assess the effectiveness of the onboarding processes and improve digital product adoption and user retention.
• User engagement rate – the percentage of users who remain active over a period of time in a user cohort. It helps businesses understand how often and in what ways users are interacting with their products.
• Feature adoption rate – the percentage of users who have adopted a feature, that is who are using it regularly to solve a problem. It gives teams insights into how valuable the feature is and how effective secondary user onboarding and customer success are.
• Customer churn rate – the percentage of users who have stopped using a product over a period of time. It helps teams understand how well the product satisfies user needs and predict future revenue.
• Customer Satisfaction Score (CSAT) – an indication of customer satisfaction with the product. It is essential for understanding how well the product is meeting customer needs and identifying areas for improvement.
• Expansion revenue – the additional revenue generated from existing customers through upselling or cross-selling. It is critical for understanding the growth potential and identifying opportunities to maximize revenue from existing customers.
• Customer Lifetime Value (CLV) – the total revenue a customer brings over the duration of their relationship with your company. It helps teams with account expansion and optimizing their marketing and customer acquisition channels to attract high-value customers.

Types of analysis you can conduct with a product analytics software

Product data analytics come in various shapes and forms. Here are a few popular kinds of analysis that teams carry out.

Segment analysis for tracking user actions of similar users

Segment analysis allows you to identify patterns in the behavior of users sharing common characteristics.

For example, you may choose users with the same jobs to be done or the same role in the company. Within this group, you could look for a certain subset, like NPS detractors.

By looking at their product usage data, you may be able to see why their satisfaction levels are so low and find ways to support them better, for example with in-app guidance.

Cohort analysis for collecting actionable insights into user behavior

Cohort analysis is similar to segment analysis in that it looks at patterns in group behaviors.

However, cohort analysis mostly uses time to group users. In other words, you group together users who signed up in a particular period, say a month. And just like with segmentation, you can look at each group and analyze their behavior for patterns.

For example, you may be able to evaluate the impact of product changes, like updates, or seasonal fluctuations on user success.

Funnel analysis for monitoring progress through key funnel stages

Funnel analysis is a tool for tracking user conversions at different stages of the entire customer journey.

For example, you could look at how users progress through an onboarding flow. Each task from the onboarding checklist would correspond to one funnel stage.

By monitoring how users complete the tasks, you can identify bottlenecks where users slow down or drop off completely.

Customer journey analysis for creating a frictionless customer experience

Customer journey analysis aims to map all the interactions that users have with your product or company and identify ways to optimize them. This includes everything that happens before they even sign up for the product, like engagement with your marketing assets.

The focus of journey analytics is on all the events, problems, and emotions users experience at different touchpoints and its goal is to find ways to remove unnecessary friction from the CX.

Conversion analysis for identifying successful customer touchpoints

Conversion analysis is similar to funnel analysis in that it focuses on user progress from one stage to another. However, it analyzes specific customer touchpoints in more depth to assess how effective they are at driving conversions.

For example, you may be able to determine that users who engage with specific in-app guidance are more likely to upgrade to the premium plan.

Trends analysis for measuring behavioral patterns in-app

Trends analysis is the process of identifying, monitoring, and evaluating patterns in customer behavior that could impact long-term product performance and success.

For example, users may stop using a feature because new technology has emerged and made it redundant. Such insight will help the product manager make the decision to either update or sunset the feature.

Retention analysis for understanding what keeps users retained

Retention analysis helps you understand the reasons why your users keep using the product and stay with your company.

This kind of analysis could be particularly valuable because it can shed light on what successful users do. For example, you can see that users who convert to paid plans use the product in a similar way.

Such insights can help you identify the happy paths users take or behaviors you may want to promote among other user segments.

Churn analysis for identifying why customers leave

Churn analysis looks at user retention from another angle. Instead of trying to figure out why users stay, it looks at why they abandon you.

In short, churn analysis looks into the interactions of churned users with the product. It can help you to pinpoint tell-tale behaviors that lead to failure and prevent future churn.

For example, you could re-engage the at-risk users with in-app messages triggered whenever these behaviors occur.

How to analyze product usage data to improve customer experience?

Let’s now look at how we can leverage different kinds of analyses to improve customer experience.

Measure a new feature’s user engagement and adoption

Let’s start by measuring new feature engagement and adoption. Modern analytics tools allow you to tag the features you want to track and then filter the usage data by segments.

The data is then displayed on an analytical dashboard from where you can see trends in feature adoption. If you see that adoption is low, you can carry out funnel analysis or map out the paths the users take to identify friction points.

Identify and remove friction points from the user journey

There could be different reasons why users experience friction at a particular point. For example, the UI may be overwhelmingly cluttered or not intuitive enough.

Hence, finding out why exactly users fail to complete a task may require a bit more prying. For example, you may want to look at heatmaps and session recordings, send an in-app survey, or interview users.

Once you have the insights, look for ways to reduce the friction. For example, you may want to trigger in-app guidance to help users achieve their goals.

Test how users interact with changes to your UI or messaging

Before you trigger the guidance for all users, carry out an A/B test to see if it actually works. Keep testing and tweaking it until it moves the needle in the right direction.

Identify your most valuable customers

Your power users can be a goldmine of insights and ideas on how to improve the user experience for others.

First, they are successful users, so tracking their behavior can help you find the happy path along which you can push other users to help them succeed.

They also make ideal beta testers and often provide lots of valuable qualitative data in customer interviews and surveys.

Must-have features to look out for in product analytics software tools

What features should you look out for when choosing your product analytics platform?

Let’s check out a few.

• Event analytics (including custom events) to track user interactions with the product
• Funnel analysis and goal tracking for customer journey analysis
• Product/feature usage tracking for engagement analysis
• Behavior-based user segmentation
• Access to data in real-time
• Custom dashboards
• Integrations with other analytics tools, and webhooks

The best product analytics tools for your SaaS

There are plenty of product analytics and data management platforms that enable you to collect and analyze your product data effectively.

Let’s look at three tools that we believe stand out from the competition.

Userpilot – all-in-one engagement and product analytics tool

Userpilot is a product growth platform not just an analytics tool.

What does this mean?

Apart from its analytics, you can also use it to collect and analyze user feedback and act on the insights thanks to the engagement layer. Basically, it allows you to trigger contextual in-app messages to announce new features, provide guidance, and onboard users.

Here’s the breakdown of Userpilot’s analytics features:

• Events tracking, including custom events (code-free or through API)

• Goal tracking for milestone analysis
• Funnel analysis and paths (coming soon)
• Feature usage tracking (clicks, hovers, text infills)
• Heatmaps

• Survey, checklist, and resource center analytics
• User segmentation (based on user attributes, company data, tagged features, custom events, in-app experiences, and feedback)
• Real-time data relay for event-based message or survey triggering
• A/B testing (for in-app experiences)
• Integrations with specialist analytics tools like Amplitude, Heap, and Mixpanel, to name just a few
• Webhooks for custom integrations with 3rd party apps

Userpilot comes with 3 pricing plans.

The lowest one, Traction, starts from $249/month and gives you access to most of the analytics features above apart from A/B testing, webhooks, and event-based content triggering.

Amplitude – standalone product analytics solution

Amplitude is one of the best analytics and data management platforms out there.

What features does Amplitude offer?

• Cohort analysis
• Milestone analysis/goal tracking
• Funnel analysis and conversion drivers
• Impact analysis for testing how changes affect the product performance
• Root cause analysis
• Custom dashboards
• Pathfinder to map out the paths leading up to events
• Real-time data reporting
• Integrations with engagement tools so that you can act on the insights (Amplitude has no engagement layer)

Amplitude is available under three pricing tiers. The lowest one is actually free and it gives you access to the main product analytics features.

Google Analytics – popular tool for tracking website and product data

It’s a common misconception that Google Analytics is a web tracking tool only. That used to be the case with Universal Analytics, but this era is coming to an end (1 July 2023). GA4 supports tracking user events in-app as well.

Here are its main features:

• Real-time activity tracking
• No-code event tracking with Google Tag Manager
• Event filtering by category, action, and label
• Behavior reports – insights into visitor interactions with your product (e.g. pageviews or events)
• Audience reports – data on your website visitors, including demographics
• Acquisition reports – breakdown of acquisition channels, like organic or direct
• Goals and conversion tracking
• Funnel analysis
• Customized dashboards

And guess what? All of this is available for free. More advanced features, like attribution analysis, will cost you dearly though – up to $12.5k/month.

Conclusion

Product data analytics is an essential aspect of the product management process. It enables teams to analyze user behavior and product performance and make informed decisions to improve the product and customer experience.

If you want to see how Userpilot can help your team harness the power of product analytics, book the demo!