Analytics For Product Managers: What to Track and How to Act Upon Data

Is there such a thing as analytics for product managers? Data analytics is a must for every team in a SaaS company if you want to make decisions based on data.

This article explicitly explains what metrics are essential to product management teams and how to collect the data.

Keep reading for the good stuff!

TL;DR

• Product analytics is about collecting and analyzing user interactions across the product to understand what drives product adoption and long-term retention. With product analytics, product teams are able to understand and address true customers’ needs and pain points and spot product friction.
• Product analytics helps product managers determine how many customers are using their products and what obstacles the average user encounters throughout the road. It also gives an understanding of how sticky the product is.
• It helps understand product engagement trends across different cohorts to find patterns, make informed decisions based on the data; and test new features and product experiences.
• The product management team should collect and act on product engagement, activation, adoption, and retention metrics.
• The product manager or data scientists use analytics tools to perform behavioral analysis and segment customers based on shared traits to perform an in-depth analysis.
• Conduct a cohort analysis to see what a subsection of your users is doing with your software at a certain period.
• Funnel analysis will answer questions like how many users have completed the onboarding flow and where they drop off.
• Userpilot will help you gather data about customer engagement and satisfaction across the customer journey and build personalized in-app experiences to boost retention.
• Hotjar’s session recordings show what users are doing inside the product in real-time. This helps spot friction points.
• Pendo provides in-depth analytics of user paths, funnels, and cohorts.

What is product analytics?

Product analytics is about collecting and analyzing user interactions across the product in an effort to understand what drives product adoption and long-term retention. It enables product teams to understand and address true customers’ needs and pain points and spot product friction.

Product management analytics lead to improved retention and conversation rates, resulting in revenue growth.

The typical questions product analytics answer:

• What actions do users take? And why?
• How much time do they spend with the product?
• What is the churn rate, and how to decrease it?
• What features to develop to get a larger market share by satisfying customers?

How do product analytics metrics differ from marketing analytics?

Marketing analytics only explain the first step in the user journey. It draws insights into how visitors engage with the site and what marketing messages and channels attract relevant user segments.

Marketing teams aim to understand what converts customers and how to convert as many as possible.

Marketers rely on Google Analytics, Google Search Console, and other first-party data sources to analyze the first step in the user journey.

Likewise, product management analytics focuses on user engagement throughout the entire customer journey. It aims to understand what features users like/dislike and their customer experience.

The primary goal is to gather tons of information to build a product-led company, delivering the best solutions to the market’s needs.

How data analytics can help product managers

In short, product manager analytics enables companies to make data-driven business decisions on further product development to ensure product-market fit.

Let’s discover more benefits of product management analytics.

Product analytics help product managers determine how people are using their products

To do so, product managers (PMs) rely on specific user events tracked through different user engagement analytics. This translates into understanding how users use the product:

• Which features are used or get abandoned?
• What is the user workflow?
• Are newly introduced features quickly adopted or not?

To track these analytics, product managers rely on analytics platforms to collect and analyze data. For example, you can set up goal tracking and feature usage to collect data on completed milestones in feature adoption and the frequency of feature usage.

Product analytics’ retention metrics can tell product managers how sticky the product is

Customer retention is the metric that shows the percentage of recurrent customers (aka those who pay monthly and continue using your product).

For SaaS, it’s one of the vital product management analytics metrics that literally highlights whether businesses make it or break it.

Combine retention and usage metrics to understand how sticky your product is, and why.

Product analytics help product managers understand trends

Track how different user segments engage with the app and how this changes over time. And analyze product engagement across different cohorts over time to find patterns and make informed decisions based on the data.

For instance, you can learn that some features need to be sunset, or their friction points in the customer journey are causing churn and drop-offs.

What decisions can a product manager make based on product analytics?

Product analytics give product management teams insight into various aspects of the business and help them make data-driven decisions regarding questions like:

• What product tweak will lead to increased revenue?
• Which features are no longer of interest and practical use?
• What change in the onboarding flow will get users to complete product adoption faster?
• Why are some features getting abandoned?
• Do users follow the happy path?
• What customer segment drives the most revenue growth? What does their journey look like?

Tie customer insights with user analytics to drive product vision and impact key business results.

How can product managers track product analytics?

For analytics to make sense and guide your decisions, you need to combine insights, ask logical questions, and experiment with your data.

Create hypotheses, and test them to see how it works out in reality.

Here’s how product manager analytics helps with that.

Segmentation helps product managers perform an in-depth analysis of data

Customer segmentation is the process of grouping users by certain characteristics that they share (in-app behavior, sign-up date, goal, events).

For instance, needs-based customer segments allow you to compare how fast users of the cheapest plan adopt the product versus the enterprise ones.

You can see this by building segments based on the answers you collect through user feedback, then tracking how each segment engages with your product.

Cohort Analysis

Customer segments linked to a given period of time are called cohorts. Conduct a cohort analysis to see what a subsection of your users is doing with your software/tool at a certain time period.

There are two types of cohort analysis: absolute and relative. Absolute cohorts are created based on fixed groups of users. For example, those who completed product adoption within a week after you rolled out a new onboarding flow.

Relative cohorts analyze shifting groups of users (e.g., those who signed up within the past 15 days).

Funnel analysis helps reveal the health of processes

A funnel measures the steps that users take towards a specific goal. It also answers questions like how many users have completed the onboarding flow (activation funnel steps completed) and where they drop off.

For instance, you can use Userpilot’s events and goals tracking to track user interactions across the product’s interface:

• Clicks
• Form fills
• Hovers

And you can tag these interactions as “features” directly using the Chrome Extension.

Once you’ve set the features you want to track, it’s also possible to test them, so you can be sure you are tracking accurately.

Using the powerful trends overview, you can filter your events and feature tags by segments, time periods, and even companies.

Behavioral analysis allows product managers to understand customers on a deeper level

Behavioral analysis is a strategy of grouping customers based on how they interact with your product. Product teams use behavioral analysis to improve in-app experiences and promote customer expansion.

It also helps to predict future customer behavior, monitor growth patterns, and discover what drives growth.

You can also use session recordings to collect product data and determine which features are working and which can be improved.

Product analytics to track to make data-driven decisions

It’s essential to understand and track the main product manager analytics metrics that move the needle. This section will talk about essential product metrics that boost business growth.

Let’s learn all of them.

Product engagement analytics and metrics

Product engagement analytics tell you what product features are popular, how much time users spend using your product, and whether users are getting value from the product.

You can use this analysis to improve your retention with:

• Interactive walkthroughs — guide your users through the product once they sign up. Personalized experiences based on user persona.
• In-app guides — explain to your users how to get the most value out of new features, filters, etc.
• Short checklists — prompt users to engage with particular features by giving them a set of actions.

Here are several essential metrics of product engagement to keep an eye on:

• Churn rate — the percentage of users that terminate subscriptions to all paid users. High churn rates are a sign of unmet customer needs. Compare your churn rate against your engagement rate to find the pattern and act on it.
• NPS score (net promoter score) — measures customer satisfaction and loyalty.
• Feature usage — gathers data on the most/least used features.

Product adoption analytics and metrics

Product adoption occurs when your users start using your product purposefully. In order to measure product adoption effectively, you need to understand the breadth, depth, and duration of feature adoption.

Some metrics needed to measure product adoption are:

• Feature adoption rate — the percentage of new customers that use a feature.
• Time-to-value — tracks the time it takes for a user to reach the “AHA moment.”
• Customer engagement score — indicates how engaged your customers and free trial prospects are.
• Activation rate — measures the number of users reaching the activation point in the user journey.
• Product-qualified leads (PQLs) — how many active users have experienced the value of the product? These may likely become paying customers.

Product retention analytics and metrics

Product retention is critical for business growth as it increases your customer’s lifetime value.

Important product retention metrics include:

• Product stickiness — the DAU to MAU rate.
• Monthly recurring revenue (MRR) — is the guaranteed revenue a business will earn every month.
• Customer retention rate — the number of customers you retain over time.

Financial product analytics and metrics

Common examples of financial metrics are:

• The average revenue per daily active user (ARPDAU) — helps you understand how well your app generates revenue.
• Customer lifetime value (CLV) — is the total expected earnings per paying customer over the course of their relationship with you.
• Revenue growth rate — a quarterly growth rate is the most common for SaaS companies.

The best product analytics tools for product managers

Let’s get familiar with the two best tools for product analytics.

Userpilot

Regarding proper user onboarding that drives long-term product adoption, Userpilot has the right product analytics to help product managers succeed.

Let me explain. I’m not talking about product usage and analytics only.

Analytics is about collecting feedback, and tracking in-app user behavior but also tracking how users engage with your in-app experiences.

You then need to be able to act on those insights. That’s where proper user segmentation capabilities come in.

Userpilot has you covered there too. In short, when you start using Userpilot, you’ll get:

• Creating and tracking combinations of in-app events like clicks, hovers and form fills, and then analyzing all these interactions under your own custom events that can be built without code or API calls.
• The new custom events consist of feature tags as well – or combinations of tracked events you’ve passed through the Userpilot track script.
• The powerful trends overview allows you to filter your events and feature tags’ usage by segments, time period, and even company. This allows you to track event usage trends and even drill down to the individual users (or companies) who engaged with specific custom events and show them the right in-app experience
• Apart from product usage data, Userpilot also has built-in analytics for in-app engagement with in-app flows and experiences. Analyze how users engage with your checklists or Resource center modules, identify trends, and A/B test different approaches to improve engagement.

Last but not least, use all that data to build highly granular user segments and reach users with the right engagement flow at the right time.

Now, with so much power on your hands – what are you going to do with all this data?

Pendo

Pendo is a great product analytics tool that designed its own metric Product Engagement Score (PES). Pendo’s analytics features are somewhat more advanced, but it loses Userpilot in terms of user activation and retention features.

Plus, it’s safe to say that Pendo will cost you an arm and a leg. You will only get basic metrics and insights with the free product, while paid plans can get as high as $55,000/year.

See the complete comparison of Pendo and Userpilot.

Conclusion

Product insights unlock product growth opportunities and put you on the same page with your customers. Without this, it’s merely impossible to build a solid product the market craves.

Start collecting customer data and take your product to the next level! Get a Userpilot Demo to unlock its full potential for product analytics.