What is User Analytics and How to Get Started with It [+ Tools]

With so many categories of product analytics, it becomes hard to identify the lines separating each type — much less figure out which ones are worth tracking. User analytics simplifies the matter by focusing on in-app behavioral data that offers insights into how your users interact with the product.

In this guide, we’ll walk you through what user analytics are, why user analysis is important, how to collect user analytics data, and which tools product teams should use to get the job done!

TL;DR

• User analytics is a combination of quantitative and qualitative data that measures the behavior of customers when they interact with your product.
• User analytics provide actionable insights, help you improve satisfaction, and drive product growth.
• Segmentation, surveys, heatmaps, and funnel/trend reports can all play a role in your user analysis strategy.
• Tracking and calculating the right metrics is crucial for effective user analysis. Depending on your tracking goals, you can monitor metrics such as onboarding completion rate, product usage, customer satisfaction, etc.
• User analytics tools like Userpilot, Amplitude, and Google Analytics can help you collect detailed insights on customer behavior.
• If you need a user behavior analytics option that also allows you to act on collected data, book a Userpilot demo to see how it empowers your business and drives growth!

What is user analytics?

User analytics consists of gathering qualitative/quantitative data, analyzing it, and interpreting these behavioral metrics to extract insights on how users interact with the product. The user insights drawn from these analytics can help you improve the user experience and boost product retention.

Why is user analytics important?

User analytics are incredibly helpful in multiple areas, but a few key benefits include:

• Using actionable insights to guide product development. User analytics help you make product changes that are based on actual usage patterns rather than assumptions or lore.
• Improving customer satisfaction scores. Streamlining the user experience and increasing satisfaction rates are a lot easier when you have a better understanding of user behavior.
• Skyrocketing product growth. User analytics improves retention and growth by accurately tailoring the product experience to your customers or prospects.

How to collect user analytics data?

Of course, to conduct data analysis on user analytics, you first need to collect data. Let’s take a look at a few ways to collect analytics data and how to tie in goals to the data collected.

Use welcome surveys to gather customer data

Welcome surveys are one of the first opportunities you have to gain insights into a specific user. Asking users about their role in the company, team size, jobs-to-be-done (JTBD), and problems/goals will offer a more accurate picture that you can use to fine-tune user personas moving forward.

Marketing teams generally include welcome surveys in the signup flow or welcome screen. Surveying early in the onboarding process will help you personalize in-app flows and product tours based on the needs of that specific user.

Tag features to monitor product usage

Having a robust product analytics infrastructure will make it easier to monitor behavior across different features and user segments. Feature tagging helps you measure product usage by tracking interactions (such as clicks or hovers) for your core features.

Collect user behavior data using event tracking

Event tracking is the most straightforward way to collect user behavior analytics and granular event data. Product teams have the option of measuring analytics using pre-built tracked events or creating their own custom events to track server-side data.

In either case, event tracking can provide historical data (and real-time reporting, with certain platforms) on user behavior to help you spot trends or patterns. Tracked events only collect data points on a single event, while custom events let you group multiple events together.

For instance, you could group all the events related to a specific feature and then only count the interaction if all those events occurred. This is useful for tracking engagement with features that require multiple clicks to use (so you don’t get false positives whenever users only click on the first step).

Create goals to track user progress

Understanding how users navigate your product and which features they engage with is helpful in itself. However, adding goals into the mix makes your user analytics a lot more actionable and streamlines the process of tracking which goals users are achieving throughout the full customer experience.

Goals could include:

• Specific actions that users need to take, such as completing their account, creating custom dashboards, trying a feature for the first time, or other Aha! moments.
• Growth metrics like product adoption, repetitive feature usage, or upgrades to higher subscription tiers.
• Retention rates across different user personas to see how churned users behave differently from retained customers or vice versa.

By setting up goal tracking, you’ll be able to see how many users (what percentage of the total customer base) are accomplishing key goals. Not all user analytics platforms let you create custom goals or choose different metrics to tie them to, so aim for analytics tools with this functionality.

The more you set goals and identify opportunities, the easier it will become to create a goal-oriented user onboarding process. Userpilot lets you set goals for every in-app flow that you make so you can measure its overall impact on your customer base.

Send surveys across touch points of the user journey

Surveying your users can help you make data-informed decisions and gain insight into how customers feel whenever people interact with your product. In-app surveys are the most reliable approach to collecting feedback from your customer base.

Your surveys can collect qualitative feedback or quantitative data to benchmark satisfaction. If you take the quantitative route, make sure you’re surveying for the right metrics that actually provide actionable data (rather than vanity metrics that’d make you waste time analyzing).

For instance, you could use a scalar survey to ask users how satisfied they were with their onboarding:

How to perform user analysis?

User analysis is a multi-step process that spans many teams with a joint goal: getting a comprehensive view of how users engage/interact with the product. User analysis (when done right) will provide invaluable user data and help you identify areas for improvement.

The sections below will help you craft the optimal user analysis strategy for your needs.

Segment users to understand preferences

It’s important to remember that your users don’t share a hive mind. All unique visitors have their own needs, preferences, and usage patterns that you need to be aware of. The easiest way to cater to all these needs (without alienating other users) is through user segmentation.

There various types of user segmentation you could use:

• Demographic data (age, gender, education, geography, etc.)
• Behavioral data (usage history, session duration, and other patterns)
• Psychographic traits (values, interests, and opinions)
• Technographic traits (platform, desktop/mobile device, and operating system)
• Firmographic attributes (industry, company size, and growth/revenue stage)
• Customer data (whether collected natively or synced from a third-party CRM)

Once you’ve divided your users into distinct segments, you can conduct a correlative analysis to compare users from one group to another. For instance, you could compare power users to inactive users to see how their user behavior differs.

If you notice that a certain feature is more commonly utilized amongst power users, then you could consider using in-app flows to highlight it early in the user journey. Conversely, you could also use segmentation to deliver a personalized customer experience to your most engaged users.

Userpilot helps personalize your user engagement efforts through its advanced segmentation capabilities:

Use heatmaps to analyze user engagement

User behaviors are a lot easier to comprehend when you’re able to visualize the data instead of digging through the numbers manually. Heatmaps are a great way to compare feature engagement, spot friction points, and provide targeted support to any struggling users.

Userpilot’s heatmaps use a color-coded interface to help you grasp user behavior analytics at a glance:

Use trend analysis to examine usage pattern

Trend analysis helps you monitor feature usage and spot any peaks or troughs early. A spike in one metric or sudden drop in another can help inform product teams on which changes users react to (and whether that reaction is positive or negative in nature).

Trend analysis can also be used to conduct a side-by-side comparison of different features or product areas to see how they’re performing. These insights can then inform product teams on what to prioritize their development time towards or which features require urgent improvements.

Userpilot’s trends reports help you track performance over time and view detailed breakdowns based on segment or date range:

Set up funnels to see where users drop

Funnels can be incredibly helpful for identifying drop-off points, high-friction areas, and larger user retention patterns through cohort analysis.

Userpilot uses tracked events to create funnel charts that product teams can use to visualize different stages of the user experience. When looking at Userpilot’s funnel reports, you’ll be able to see the exact number (and percentage) of users that made it from one stage/step to the next.

By reviewing these types of reports and data, funnel optimization becomes a guided effort (as opposed to a haphazard experimentation process). If you combine these insights with individual user interviews for those who dropped out of the funnel, you’ll get a very comprehensive view of user experiences.

Analyze feedback to gain user experience insights

When it comes to analyzing feedback, there are a few pitfalls to avoid. The most dangerous are the various types of survey bias product teams might run into when trying to interpret qualitative customer feedback.

Avoiding survey bias is possible if you take the proper precautions, like asking fair questions, minimizing jargon, and qualifying qualitative responses with quantitative data to ensure everything lines up. Of course, quantitative data can be difficult to analyze as well.

NPS surveys may seem like a simple way to measure customer satisfaction, loyalty, and advocacy. However, product teams need to go about NPS analysis with the right approach to get the most actionable (and accurate) insights from these surveys.

For instance, product managers could review the responses to NPS surveys as a form of user research to identify popular issues amongst passives or detractors. These actionable insights can then be relayed to the product team responsible for fixing such problems.

Userpilot supports NPS response tagging to help you track recurring bugs or issues:

Key metrics to track for the user analysis process

User analytics will usually draw from product analytics, web analytics, and other data sources to give product managers as holistic a view as possible. This combined tracking approach will usually lead to a fairly long list of metrics that you’ll need to keep your eye on.

Here are some key metrics for user analysis and how to track them:

• Onboarding completion rate. You’ll need a user analytics tool with built-in event tracking to measure onboarding completion rates.
• Product usage. You’ll need a user analytics tool with native product analytics capabilities to track usage.
• Product stickiness. Measuring product stickiness is as simple as dividing the number of daily users by the total number of monthly active users.
• Feature adoption. You’ll need a tool with feature tagging capabilities to monitor feature adoption rates.
• Active users. Tracking the total number of monthly/weekly/daily active users — MAUs, WAUs, and DAUs respectively — will help you measure changes in user activity over time.
• Retention rate. Retention rates measure the percentage of customers that are retained during a specific period of time.
• Churn rate. Churn rates measure the percentage of users who don’t renew their subscriptions (whether through involuntary churn or intentional cancellation reasons).
• Customer Satisfaction Score (CSAT). The CSAT score is a metric that measures how satisfied your users are with the product that you provide (most often through in-app surveys).
• Net Promoter Score (NPS). NPS data is gathered from users through in-app surveys, automatically calculated, and then displayed on a unified NPS dashboard (if your software of choice has one).
• Traffic sources. Identifying the traffic source that produces the most engaged users will tell you which channel(s) to double down on, so be sure to set up UTM tracking in Google Analytics.

Regardless of which user analytics you decide to track, ensure that your sample size is sufficient to provide accurate insights. The larger the sample size, the less likely it will be for a single user to skew your results, making the data set more resilient against edge cases.

Best user analytics tools for collecting insights into user behaviors

While third-party tools aren’t strictly necessary, they help you save time (especially if they integrate with other tools to automatically sync user data). We’ll cover both comprehensive product adoption platforms like Userpilot as well as dedicated user analytics platforms like Amplitude.

Here are three user analytics tools you should consider using:

Userpilot

Userpilot is a digital adoption platform with built-in user analytics capabilities. It has native features for both collecting and analyzing data.

It lets you tag features to see how users interact with them, compare goals by cohort, and create trend reports that track behavioral patterns over time.

Here are some Userpilot features you can use for user behavior tracking:

• No-Code Feature Tagging: Userpilot’s click-to-track feature tagger lets you mark features, buttons, and elements with the Chrome extension. You’ll be able to track user interactions such as clicks, hovers, or inputs to get an accurate behavioral view for specific features.

• Funnel Reports: Funnel reports show you the total number of users that enter a funnel and the percentage of users that complete each step. This can help you track behavioral paths and see which stages most users get stuck on.
• Trends Reports: Generating trends reports will help you visualize the occurrence of key events over time and break down these analytics by device, browser, operating system, country, signup date, or even individual user IDs and email addresses to see granular behavioral analytics.

Amplitude

Amplitude is a product analytics and event-tracking platform with some interesting user analytics features. Most notably, it has a pathfinder feature that lets you zoom in on specific user paths to the percentage of users that share the same navigation patterns.

Google Analytics

Google Analytics is a free and widely used tool across countless marketing campaigns. Despite its limited feature set (for SaaS companies), it’s still helpful for analyzing traffic or purchase-specific behaviors.

These insights might not be as actionable for in-app optimization, but they’re highly applicable to external marketing campaigns. For instance, Google Analytics has a funnel explorer that shows you the completion/abandonment rate for each step in a user funnel.

Conclusion

As you can see, user analytics is a boon for product teams who want to drive real change. Choose the right user analytics platforms, track the most important metrics, and act on the insights gathered from user research then, you’ll be well on your way to improving the customer experience.

If you’re ready to start reaping the benefits of user behavior analysis and want to improve the user journey for all your customers, then it’s time to get your free Userpilot demo today!