User Data Analytics: How to Gather Valuable Insights?
What are user data analytics? Why do they matter? And how to use them to improve your product?
From this article, you’re going to find out about different types of user data analysis and how to develop a user data analytics strategy that will help your team make data-driven decisions to enhance customer and product experience.
Let’s get right into it!
TL;DR
- User data analytics involves collecting and analyzing data from various sources to understand user behavior and create user-centric products.
- Monitoring user analytics is essential for optimizing marketing efforts, making data-driven product decisions, and enhancing user experiences.
- Various teams, including marketing, product, UX/UI designers, customer success, sales, and senior leadership, benefit from user data analysis.
- Segment analysis helps identify common trends and patterns within user groups, allowing for the personalization of user experiences and addressing unique pain points and needs.
- Cohort analysis focuses on the behavior of user groups based on the time when they joined or completed a specific action. It is especially valuable for monitoring user retention and churn rates over time.
- Funnel analysis provides insights into user progression through different stages of the customer journey. It allows you to identify areas where users encounter friction and improve conversion rates at various touchpoints.
- Trends analysis focuses on tracking specific metrics over time.
- Start building your user data analytics strategy by defining the key user segments that will be the focus of the analysis, based on relevant criteria such as user attributes, behavior, or demographics.
- Next, collect relevant user data from multiple sources. This could be data on feature usage, completed events, or recordings of user sessions.
- The next phase involves in-depth analysis to extract meaningful insights.
- The final step is to use the insights gained from the analysis to implement improvements in the product or user experience.
- Key user data metrics to track include activation rate, product adoption rate, engagement rate, retention rate, product stickiness, and Net Promoter Score (NPS).
- To see how Userpilot can help you with user data analytics, book the demo!
What are user data analytics?
User data analytics is the process of collecting, organizing, and analyzing data from multiple sources to gain insights into user behavior at different stages of the customer journey.
Its main goal is to help teams better understand how users respond to marketing efforts and engage with the product. This enables them to create products that solve genuine user problems — and do it well.
Why is monitoring user analytics important for SaaS companies?
Monitoring user data helps you optimize your marketing campaigns to widen the top of the funnel. This may involve choosing the best acquisition channels or refining your pricing strategy to make the product more competitive.
Next, it helps make data-driven product decisions so that you can deliver more value to the customer and improve their experience with the product.
This includes prioritizing the right features, improving product usability to create frictionless experiences, or creating personalized onboarding flows to drive customer retention and product adoption.
Overall, user data analytics can help you build successful products and improve customer satisfaction.
Which teams benefit from the user analysis process?
User data analytics covers user interactions at different touchpoints across the whole customer journey.
Consequently, every single team involved in the product development process can benefit from the insights it offers:
- Marketing teams — to streamline customer acquisition and improve their retention.
- Product teams — to improve product functionality and deliver relevant user onboarding that drives product adoption.
- UX/UI designers — to create intuitive and inclusive user experiences.
- Customer success teams — to develop in-app guidance and support resources that help users deal with their issues independently.
- Sales teams — to identify product-qualified leads (PQLs) and convert them into paying customers or drive account expansion through upsells/cross-sells.
- Senior leadership — to assess the overall performance of the product.
Common types of user analysis to conduct
There are different ways to analyze user data analysis, depending on your goals.
Segment analysis for monitoring user behavior of similar groups
Segment analysis involves grouping users based on specific criteria to identify common trends and patterns in their behavior.
This kind of analysis is essential if you’re serious about personalizing the user experience for your customers to address their unique pain points and needs.
Let’s take your NPS promoters and detractors as an example.
By analyzing the behavior of your detractors, you can diagnose their issues and help them overcome them. One way to find ways to support them could be through the analysis of the promoters’ behavior data.
Cohort analysis for monitoring retention and churn
Cohort analysis also looks at distinct user groups.
However, it uses only one criterion: the period when they completed an event. For example, you could analyze the behavior of users who signed up for the product in the same week or month.
This type of analysis is used to monitor user retention and churn rates over time. You could use it to track the impact of product changes or seasonal fluctuations.
For example, if you have a fitness app, you’re likely to experience a spike in sign-ups in January, with the retention remaining fairly high in Q2 but dropping dramatically afterward. This may be the time to double down on your in-app messaging to keep users engaged.
Funnel analysis for deep diving into customer journey stages
Funnel analysis provides teams with insights into user progress across different stages of the customer journey.
Thanks to that, you can identify areas where they experience friction and improve conversion rates at different touchpoints.
For example, as a marketer, you could use it to optimize the website to boost sign-ups or demo bookings, and a product manager can use funnel analysis to improve onboarding flows and increase activation rates.
Trends analysis for tracking important metrics over time
Trends analysis focuses on a specific metric and tracks its performance over time.
For example, you could use it to track user engagement with a particular feature or monthly active users.
Trend analysis is useful for tracking the overall performance of the product or its aspect.
Moreover, by visualizing two or more trends together in one chart, you can also spot correlations between different events and use the insights to formulate hypotheses for further experiments.
How to create a user analysis strategy for your SaaS?
When creating your user data analysis strategy, have clear goals in mind. This will help you focus on the right user segments, collect the right data, and analyze it effectively to extract actionable insights.
Define the key segments to track their user behaviors
The first step is segmenting your users based on specific criteria.
Modern user analytics platforms, like Userpilot, allow you to segment users based on various criteria, like:
- User attributes (e.g., name, ID, plan, web sessions, device type, or signup date)
- Company data
- Tagged features they have engaged with
- Custom events they’ve completed
- In-app experiences they’ve engaged with, like flows or checklists
- User feedback — both quantitative, like NPS scores, and qualitative, like NPS responses
For example, if you’re trying to improve the effectiveness of your user onboarding to improve product adoption, you may want to segment your users based on their role in the company.
If you want to improve the activation rate, you could segment users into those who have completed the activation event and those who haven’t, or based on how long it took them to reach this stage.
Collect customer behavior data through multiple sources
Once you’ve got your segments ready, it’s time to collect the data. Again, what data you collect and how depends on your objectives.
For example, to improve user onboarding, you may want to track user engagement with different onboarding patterns.
To reduce time-to-value, you can collect data about all the events they’ve completed and features they’ve engaged with leading up to the Aha! moment, and to optimize the UI — record user sessions or trigger in-app surveys.
With the analytics tools currently available on the market, collecting user data is super easy.
For example, in Userpilot, you tag the features and events to track from the Chrome extension, and in Heap, you don’t even have to do that (although the risk is being flooded with tons of data you will never need).
Analyze customer data to gain insights
Next, extract the insights you’re after by conducting one of the analyses mentioned above.
For example, to improve the performance of your onboarding, you’d use funnel analysis. This would give you the completion rates at each of the steps.
After that, you could segment your users based on whether they completed a step or not, and look at their behavior in more detail, for example, through session recordings.
Implement your findings to optimize the customer experience
Finally, it’s time to act on the insights and implement improvements to the user experience.
This could be in the form of adding an extra tooltip here and there to help users discover the features that will help them achieve their goals more efficiently or removing irrelevant questions from the welcome survey that cause unnecessary friction.
User data analysis metrics to monitor for extracting meaningful insights
While the exact user data metrics you track depend on your goals, there are a few that most SaaS product teams track. These include:
- Activation rate — the percentage of new users that have completed the activation event over a period of time
- Product adoption rate — the percentage of users that have embedded the feature into their workflows and use it regularly
- Engagement rate — a measure of how often users interact with a feature or product
- Retention rate — the percentage of new sign-ups who remain customers in a period of time
- Product stickiness — a measure of how well the product can retain users, calculated by dividing the number of daily active users (DAUs) by monthly active users (MAUs)
- Net Promoter Score (NPS) — how likely users are to recommend the product to others
The best user analytics tools for SaaS companies
To gain actionable insights into user behavior data, you need the right tools.
Let’s have a look at a couple of options that can make the daunting task that little bit easier, even for non-tech team members with limited experience in data analysis.
Userpilot — advanced product growth and user analytics tool
Userpilot is a no-code product growth platform with advanced customer analytics features, which enables you not only to track and analyze user data but also act on the insights.
Its analytics functionality includes:
- Advanced user segmentation
- Feature tracking (clicks, hovers, text infills)
- Event tracking (including custom events)
- Heatmaps analytics
- NPS analytics
- Trend analysis
- Funnel analysis
- Onboarding flow analytics
- Resource center analytics
- Retention analysis
- Advanced A/B testing
In addition, later this year, you can expect:
- User profile analytics
- AI-powered analytics for qualitative feedback
- Custom dashboards
Google Analytics — free website analytics platform
Google Analytics is a well-known web analytics platform and one of the most popular marketing tools.
Two main reasons for its popularity are advanced analytics features — and the fact that it’s free to use.
Its main features include:
- User segmentation
- Page view and event tracking
- Audience reports
- Acquisition reports (e.g., traffic sources)
- Funnel analysis and conversion tracking
- Custom data visualization and dashboards
Conclusion
User data analytics help teams gain a better understanding of user behavior. This helps make informed product development decisions and optimize most aspects of the customer experience.
If you’d like to learn more about Userpilot and how it can help you collect and analyze your user data, book the demo!
