A Comprehensive Guide to User Analytics: Tools & Methods
User analytics play an essential part in the product development and management process.
Why?
That’s one of the questions our guide answers. It also explores different kinds of user data analysis, shows you how to conduct it, and introduces a few product analytics tools with the necessary functionality.
What is user analytics?
User analytics is a research method that involves collecting, analyzing and interpreting data on user interactions with the SaaS product.
Its main objective is to understand how users behave inside the product to inform product development and design, marketing efforts, and customer support processes.
Why is user analytics important for SaaS companies?
User analytics are essential for product managers to make informed decisions on how to improve the product and deliver a positive customer experience.
Here’s how:
First, by understanding user needs and preferences, they can prioritize product initiatives that add value to the product and enable users to achieve their goals.
Better yet, analytics insights allow teams to personalize the user experience so that customers can experience value in less time.
All this increases customer satisfaction, leading to improved loyalty and retention, which are factors closely linked to the overall product and business success.
How do you know? From user analytics again, as you can use them to track product performance.
Different types of user analytics
User analytics is an umbrella term for different kinds of analysis. Here’s an overview of a few common ones.
Funnel analytics
A funnel is a sequence of steps in the customer journey.
For example, a funnel can consist of all the user actions the user needs to complete to sign up for the free trial, book the demo, or activate inside the product.
The name comes from the shape of the chart visualizing user progress. As fewer and fewer users progress to the subsequent stages, the bars in the chart get narrower towards the bottom.
Funnel analytics help you understand how many users advance from one stage to the next, how long it takes them, and how many of them drop off.
With such insights, you optimize different touchpoints in the journey and help users navigate through the product without friction.
User journey analytics
User journey analytics, aka path analytics, offers more granular insights into what users do at different funnel stages.
It involves mapping out user actions leading to a particular event – or following.
For example, you may want to use it to identify user behaviors linked to churn. To do so, you can choose the drop-off as the ending point and map out all user actions.
There are multiple applications for user journey analytics. It can help you:
- Find friction in the user journey.
- Discover the happy path to conversion.
- Identify behaviors signaling that the user is ready to convert, for example, to a higher plan or at risk of churning.
User behavior analytics
User behavior analytics encompasses user journey analytics and more: it focuses on all user behaviors inside the product.
For example, it may focus on product usage. By tracking user engagement with different features, you may be able to identify the functionality that users find particularly valuable – or features that are underperforming. Such insights can help you prioritize future product development and allocate resources.
Tracking active user behavior trends can also help you identify correlations between metrics and form hypotheses about causation.
User behavior analytics also involves usability testing, where you analyze user interactions with UI elements through heatmaps and session recordings.
Cohorts analytics
Cohort analytics is a process that focuses on the shared behaviors of users who signed up for the product or completed another important event in a given period.
For example, you may use it to analyze behavior patterns of users who started their onboarding process in a particular week or upgraded to a higher plan in a particular month.
The technique is often used for retention and churn analysis to determine how long users remain active inside the product and identify the moment when they start churning.
Once you know this, you can dig deeper to identify what causes churn and how to best reengage such users.
Cohort analytics are also used to test the impact of product updates on user behavior and detect seasonal trends.
Segment analytics
Segment analytics also focus on individual user groups sharing specific properties. For instance, you can use it to analyze the behavior of users from a specific geographical location, performing a specific role in their companies, or giving similar responses to in-app surveys.
Segment analytics isn’t linked to any specific analytics technique or process. These depend on your goals.
For example, you can use path analysis to track the actions of your power users to identify the most optimal path to activation for users with a specific use case.
How to perform user analysis?
To gain useful insights from user analysis, it’s best to follow a framework or playbook. To help you with that, here’s our 9-step user data analysis process.
Step 1: Define your user personas
Before you start analyzing your users’ behavior, make sure you know who they are and what goals they have.
Why does it matter?
Most products cater to different kinds of users who use their products differently. Consequently, any conclusions that you draw from analyzing the user behavior of the whole user population won’t apply to specific use cases.
When defining your user personas, focus on their:
- Role in the company (e.g. product marketing manager).
- Jobs to be done (e.g. increase new feature adoption).
- Company details (size, maturity, goals).
- Pains/Challenges (e.g. lack of technical expertise).
- Team collaboration (e.g. liaising with the PM).
- Benefits from using the product (e.g. building in-app experiences to drive adoption without coding).
Step 2: Define your objectives
Why are you conducting user data analysis?
That’s the next question to answer. For user analysis to be meaningful, it needs focus, so define your goals.
For example, your objective could be to ‘increase free to paid upgrades by 10% by the end of Q2’.
This goal is specific (increase upgrades), measurable (by 10%), achievable (10% isn’t unrealistic), relevant (the company is focusing on revenue expansion), and time-bound (by the end of Q2).
Specific, Measurable, Achievable, Relevant, Time-bound. Sounds familiar? Yes, it’s the SMART goal-setting framework.
I like this one because I used it a lot back in my teaching days, but there are other frameworks you can use, like OKR.
Step 3: Select a good user analytics tool
After you’ve defined your objectives, choose the product analytics tool. If you haven’t got a decent analytics platform in your tech stack yet, we offer a few recommendations in the final section, so stay tuned.
Here are a few things to bear in mind when shopping around:
- Functionality: Does it have the features to support your goals?
- Scalability: Can it support your growing needs in the future?
- Pricing: Can you afford it now? In the future?
- Complexity and learning curve: Is it easy to set up and learn? Does it require technical expertise to implement and use?
- Support: Do you get a dedicated customer success manager? How good are the onboarding resources? Is there self-service support available?
Step 4: Choose the type of user analysis process
With the right tool in place, it’s time for some analysis.
How you go about it and what techniques you use depends on the objectives.
For example, to increase upgrades, your focus will be on two main segments: those on the free plan and those on the paid one.
In this way, you can contrast the behavior of those who have converted and those who haven’t. For this purpose, path analysis is ideal.
Step 5: Collect user analytics data
Next, it’s time to collect the relevant data.
In most analytics tools, you need to tag features and events you want to track. No-code analytics platforms allow you to do it from the front end. For example, in Userpilot, you do it from the Chrome extension.
What events should you track?
Again, it depends on the goals. For example, to conduct funnel analysis, you need to track the conversion events at the end of each funnel stage, and if you’re analyzing how users interact with your UI, you need to tag UI features.
User data analytics isn’t only about quantitative data from analytics tools.
To understand why users act in a particular way (or don’t act), you need qualitative insights.
You can get them from in-app surveys or user interviews.
Step 6: Analyze data
Once you collect the relevant data, it’s time to dissect it for insights.
Data crunching can be pretty daunting, so start by visualizing it. Graphs, charts, and diagrams are much easier to digest than tables with raw data. Sometimes one look is enough to spot a trend, correlation, or friction point.
For example, in funnel analysis, if one bar gets dramatically narrower than the previous one, you know right away where to look for the cause of the drop-off.
Better yet, take advantage of the AI-powered analytics features that more and more platforms provide. They’re likely to get increasingly more powerful, making it easier to derive actionable insights from large data sets.
AI is also used to analyze qualitative user survey responses for patterns.
Step 7: Identify opportunities and challenges
Hopefully, the user data analysis reveals challenges to address and opportunities to exploit.
For example, your path analysis can help you identify a sequence of user actions indicating readiness to upgrade. You can use the information to trigger in-app messages that prompt users to do so as soon as they complete these events (you can track multiple user actions as one by creating custom events).
Sometimes, you may find multiple opportunities to improve the user experience – too many to implement immediately.
If that’s the case, use a scoring model to prioritize them according to your bespoke criteria. Doing so will help you focus on the optimizations that are most aligned with your product and organizational goals.
Step 8: Iterate and refine
The odds are that the initial changes to user experience don’t necessarily move the needle dramatically. It’s difficult to optimize things in isolation, without access to real-life data.
So instead of releasing changes to all users at once, consider rolling them out in increments to small segments, and test as you go on. In this way, you reduce risk and increase the chances that the total rollout is strong.
Better yet, try dogfooding and test the updates within the organization before releasing them to customers.
Step 9: Monitor and measure
Once the full rollout is complete, monitor its impact on product metrics.
Good tools for this include trend analysis and cohort analysis, as they allow you to spot changes in metrics after an update.
4 Best user analytics platforms to measure and improve user experience
To successfully implement your user analysis strategy, you need the right analytics tools. Fortunately, there are plenty of platforms with robust functionality available to product, marketing, design, and customer success teams.
Here are 4 that we think are worth considering.
Userpilot: Best tool for in-app user analytics
Userpilot is a SaaS analytics platform that drives user activation, feature adoption, and expansion revenue. It also helps product teams collect user feedback, streamline onboarding, and gather actionable insights from analytics.
With Userpilot, you’ll be able to track product usage and collect user behavior data to get a holistic view of how customers use your product — which will guide future development, improve the user experience, and inform your growth efforts.
Here are Userpilot’s top analytics features for product and marketing teams:
- User segmentation
- Code-free feature and event tagging
- Heatmaps
- Trend analysis
- Funnel analysis
- Path analysis
- Session recordings (coming soon)
- AI analytics (coming soon)
- Analytics dashboards
Userpilot is always looking to improve its analytics features, which is why it’s launching two advanced analytics features soon: AI analytics and session recordings.
Pendo: Best tool for tracking users on mobile
Pendo is well-known for its product analytics. Even though developing its analytics capabilities hasn’t been their priority recently, it’s still a robust platform with all the features you may need for in-depth user behavior analysis.
The platform isn’t as intuitive as Userpilot and comes with a hefty price tag, but has one advantage: it supports mobile apps.
FullStory: Best tool for in-app behavior analysis
FullStory is a digital experience intelligence platform that offers teams insights into their users’ experiences across web and mobile apps. As the name suggests, it allows teams to gather diverse user data to gain a complete understanding of user behavior.
The key features and capabilities of FullStory include:
- Heatmaps
- Session replays
- Funnel analysis
- Path mapping
- Frustration signal analysis
- Dashboards
Google Analytics: Best tool for tracking user activity on websites
Google Analytics is a popular free analytics platform, used predominantly for tracking user behavior on websites. Marketing teams use it to analyze web traffic and how users navigate sites to optimize conversion rates.
When it comes to user behavior analytics, the main GA reports include:
- Funnel exploration
- Cohort exploration
- Path exploration
Conclusion
Considering that all user interactions with SaaS products happen in the digital space, it’s difficult to imagine successful product management, marketing, or customer success without them. And even if it were, you would be missing out on a lot of opportunities that they can help you identify.
If you’d like to learn more about Userpilot analytics features, book the demo!
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