Product Analytics: An Actionable Guide with Examples [+ Metrics & Tools]
SaaS product analytics helps you track the ‘why,’ ‘how,’ and ‘when’ of customer-product interactions. And when meticulously examined, it can help you shape your product strategies from guesswork into data-driven decision-making.
In this guide, we’ve gathered everything in one place—from key metrics to insightful examples to state-of-the-art tools—to show you how to enhance customer experience based on product analytics.
- Product analytics focuses on interpreting user data that’s specifically related to the performance and usage of a product.
- It helps understand user behavior, enables data-driven decision-making, improves user experience, and drives customer retention and product growth as you start making data-driven strategies.
- Analytics is a must-have tool for product managers, product marketers, UX designers, customer success managers, and developers.
- There are four practical ways to collect product analytics data:
- Using feature tagging to track the customer journey.
- Setting up event tracking to see how users interact with the product.
- Leverage A/B testing data to understand what leads to better conversions.
- Sending relational and transactional surveys to capture customer sentiment and pain points.
- There are eight types of product analysis you can leverage, and they include:
- Segment analysis. To spot behavioral patterns among a group of users (e.g. features that drive engagement) and find ways to enhance the product experience.
- Retention analysis. This allows you to correlate feature usage with user satisfaction, predict future user behavior, and come up with customer retention strategies.
- Churn analysis. Which provides insights into why customers are leaving and how to prevent them.
- Milestone analysis. Which indicates the average user’s progress and where they stand within the journey. This allows you to, for example, identify friction.
- Funnel analysis. Which lets you watch how users move through the funnel, step by step, and find opportunities for improvement.
- Path analysis. It’s used to map out every step that your users made through their journey and identify the path your users followed to achieve success with minimal obstruction.
- Conversion analysis. It tracks the conversion rates throughout the funnel with A/B tests and identifies the factors that prompt your users to take desired actions and convert.
- Survey analysis. Which involves observing survey responses to find patterns and trends, categorize feedback, and understand the user’s perspective.
- We brought five examples of how you can use product analytics to drive growth:
- Using segmentation to group customers, target audiences who share specific data, and personalize automated messages.
- Identify users who are inactive or dropping off during onboarding and target them for sending contextual help (e.g. an interactive walkthrough) to drive engagement.
- Track feature usage and design your product to have a cap on valuable premium features so you can incentivize users to upgrade.
- Check feature requests, then offer personalized deals and early access invitations to users who make suggestions.
- Checking survey data to see what’s missing in your product or causing friction, address it, and prevent users from having to search online or reach out to your customer service team.
- Although the best KPIs for your business will always depend on your goals, there are some product analytics metrics that are often helpful for SaaS businesses. They can include product usage, retention rate, Net Promoter Score, and customer lifetime value.
- A product analytics tool should offer great data diversity, integrations, customization options, data privacy policies, and the ability to collaborate with product teams.
- The best data management platform tools in the market are:
- Userpilot to collect and analyze product data such as events, feature usage, and survey responses.
- Amplitude for top-tier funnel analysis and cross-platform analytics.
- Mixpanel for tracking a diverse set of product metrics and showing real-time data.
- Since you need the right tool to unlock valuable insights. Why not book a Userpilot demo to see how you can unlock new growth opportunities?
What is product analytics?
Product analytics focuses on interpreting user data that’s specifically related to the performance and usage of a product. The goal is to use quantitative data to find actionable insights into how users interact with your product.
It’s mostly valuable for product development, marketing strategies, and overall business planning—helping you optimize your product’s customer experience, user retention, and product growth.
Why does product analytics matter?
As a product marketer, product analytics isn’t just a task on your to-do list. It’s a crucial element of a robust product management and marketing strategy, as it arms you with empirical evidence to make smart, informed decisions.
- Product analytics helps you understand user behavior to apply tactics that increase customer satisfaction.
- Actionable insights from product analytics enable data-driven decision-making to eliminate the guesswork from the equation.
- It guides your product development efforts to make improvements that lead to a more polished user experience.
- As a result, it helps you drive customer retention and product growth as you start making data-driven strategies.
Who benefits from product analytics?
Anyone in your company should care about product analytics. However, any team member who has any responsibility for the product experience will find this app data more useful (especially if they need to execute product strategies and tactics).
So if you’re into any of the following positions, here’s why product analytics are a must for you:
- Product manager. You can monitor user behavior and see if a feature is driving engagement and fits the user’s needs. This allows you to make data-driven decisions to prioritize development, sunset features, create a successful roadmap, and overall, build a better product.
- Product marketing manager. You can track usage patterns and purchase behavior to come up with data-driven pricing strategies, upsell messages, and in-app promotion tactics. This data also allows you to measure your success through KPIs like customer lifetime value, user retention, and expansion MRR.
- UX designer. You can monitor how users interact with your product to measure its usability. This way, you can find opportunities to improve the product experience and make it as seamless as possible.
- Software developer. Knowing which features work and which don’t can give you an idea of what type of work you should prioritize—helping you design a product that better fits the target market.
- Customer success manager. You can watch over product usage and in-app behavior data to understand what path is more likely to lead to success, measure the effectiveness of your onboarding process, and reach out to disengaged users who are likely to churn.
How to collect product analytics data
Now that you know how important product analytics is for identifying what’s performing well and what needs improvement in your product. How do you start collecting such data?
Let’s go over four essential methods for collecting product analytics data.
Tag feature to collect product usage data
Product usage data can help you understand exactly how and why your product is being used. It can pinpoint features that are regularly used, and those that are ignored and provide insights on how to improve user experience.
To track it, you can use feature tagging to collect data on users’ clicks, hovers, or even text input as they use your product.
For example, suppose you have identified core features such as “uploading leads” and “email sequence builder.” You can create a tag for each of them so that every time a user interacts with it, the tag captures and logs the interaction.
Use events to gather behavioral data
While feature tags track client-side events (what happens on the user side), there are situations where some user activities can only be reached by your server, such as high-volume critical events like marketing campaigns. That’s where you need event tracking.
All you have to do is specify the event data you wish to track along with its API key. The APIs will track events about the customers that meet any criteria used to describe the event.
You can also group both feature tag events and these tracked events to track them in a group. The custom event will match when any of the specified events occur.
Set up A/B tests to identify user engagement drivers
A/B testing is a very helpful tool to get product insights.
Let’s say you’re wondering what elements of your onboarding process have the most impact on user engagement. Performing a controlled or head-to-head A/B test can help you collect experimental data you can compare to get insights.
Maybe showing in-app tooltips during onboarding leads to more feature engagement, or maybe a short checklist would do the job. The only way to know is through experimentation and collecting statistically significant data to prove it.
Trigger product surveys for user experience feedback
The best source of product experience data lies within your users. Only they can know what’s going well and what’s going wrong with your product.
That’s why product surveys are essential, as they’re the only tool besides customer interviews that can help you capture user sentiments, preferences, and pain points.
Now, there are two types of surveys:
- Relational surveys. Which are meant to understand the relationship your customers share with your product. They’re typically conducted on a quarterly or yearly basis. They’re best used for long-term feedback collection, tracking changes over time, and understanding trends (examples: NPS and PMF surveys).
- Transactional surveys. These are conducted following specific interactions between the customer and the product, such as purchasing an upgrade. They’re ideal for gathering immediate feedback on specific actions or product changes (examples: CSAT and CES surveys).
Remember: Both types of surveys are necessary to get a complete perspective on your customer’s needs and craft the best product strategy. Use both.
What are the different types of product analysis to conduct?
Product analytics is not a general process. You must embrace different types of analysis so you can get to the ‘why,’ the ‘what,’ and the ‘how’ of your product’s performance.
That said, let’s explore different types of product analyses and see how they can realign your customer experience strategies.
Segment analysis to understand user preferences
Customers are not made equal. That’s why you need to use segmentation to separate your customer base into groups.
You can segment users based on demographics, psychographics, behavioral patterns, and needs. This way, you can offer a relevant experience or message to a specific group of customers rather than following a one-size-fits-all approach.
For instance, you might create a segment of disengaged users to create customized re-engagement flows that are more likely to regain their interest, thus enhancing both customer retention and overall profitability.
Retention analysis to develop retention strategies
Retention analysis is crucial for understanding and measuring customer retention. It dives deep into how successful customers engage with your product and the factors that encourage them to stick around (e.g. high-value features, the happy path, etc.).
The beauty of retention analysis is that it’s not just about understanding, it’s about leveraging the knowledge to enhance the user experience. For instance, if you find out there are certain features that power users engage with regularly, you might trigger tooltips so other users can easily discover those features.
This way, you can easily come up with product strategies to better meet user expectations and improve overall retention rates.
Churn analysis to identify churn reasons
Churn analysis provides deep insights into why customers are leaving. It involves analyzing churn surveys (like in the screenshot below) to identify trends that you can address.
Maybe they found the user interface too complex, or perhaps they didn’t quite get the value for money they were expecting.
Now, churn analysis is not just about recognizing patterns—it’s about using this feedback to address these concerns head-on and prevent churn.
So if users find your app’s UI to be too convoluted, for example, now you know that revamping your product’s interface will help you reduce churn in the long term.
Milestone analysis to monitor user progress
Product managers can often set specific user goals and lay out milestones throughout the customer journey. With this info, you can:
- Use analytics to track the average user’s progress and where they stand within the journey (like in the screenshot below).
- Use milestone analysis to spot stages where users may be struggling to find progress or where engagement may be waning.
- Find ways to aid your customers in achieving further goals.
For example, if many users are having trouble adopting core features, you can try adding an onboarding checklist and see how it influences new users to reach the activation stage faster.
Funnel analysis to study customer journey
Funnel analysis allows you to map out the steps your customers take throughout their journey.
That said, tracking your funnel performance gives you the opportunity to examine the user journey and answer some key questions:
- What’s causing a bottleneck in the funnel?
- How much time it takes users to perform an action?
- Which steps in the journey do not lead to an eventual purchase?
For instance, if you find that users are hesitating to adopt secondary features, come up with a webinar to teach customers how to adopt an advanced feature to do their jobs more efficiently (and help them reach the next stage of the funnel).
Path analysis to plan user interactions
Every good product or service has a “happy path” leading to customer success.
And identifying this path is what path analysis is for. It records every turn your users take, every decision point they encounter, and every action they engage in.
To use it profitably, watch your most successful user. How did they achieve it? Is there a common, repeatable pattern that you could encourage more users to follow? If so, you’re facing an opportunity to lead more customers to success.
For instance, if the analytics reveal that engaged users tend to watch your product tutorial video before upgrading to a paid plan. Here, the tutorial becomes a crucial part of the happy path, and you can insert those videos as part of the free trial onboarding to lead more users toward success.
Conversion analysis to identify growth opportunities
Conversion analysis is about identifying factors that prompt your users to take desired actions—be it making an upgrade, adopting an advanced feature, or renewing their subscription.
For example, it can take raw A/B testing data to reveal what drives more conversions. Does adding an upselling message in the top corner of the UI lead to more upgrades? Or does a specific tooltip in your onboarding process lead to more free-to-trial conversions?
With this knowledge, you can iterate in-app experiences that aren’t working as well and enhance those that are—consistently nudging users toward the ultimate end of your funnel.
Survey analysis for product improvements
As long as you asked the right questions in your surveys. Survey analysis is what helps you decipher the voice of your customers.
You can find patterns and trends, categorize feedback, and eventually come across golden nuggets that can lead you to brilliant product strategies.
For example, you can not only segment your customers as promoters, passives, and detractors based on their NPS survey responses. You can also tag their qualitative responses to find common themes and keywords, so if “bad usability” is frequently used by detractors, you can take this feedback to revamp your app’s interface and interactivity to avoid generating more detractors in the future.
This way, you can keep refining your product to make it fit the market based on a deep understanding of the user’s perspective.
Examples of using product data analytics insights to drive growth
Want to see how to apply product analytics in the real world? Let’s look at five examples of SaaS brands leveraging product data to their benefit:
Use customer data for in-app marketing automation
An excellent way to leverage product analytics is through segmentation. As it allows you to group customers, target audiences who share specific data, and personalize automated messages.
Let’s say you have just launched a new feature and want to announce it to your user base. Instead of making a blanket announcement to everyone, you could simply show it to users who’d benefit from it and even invite them to a webinar (like in the screenshot below).
Trigger in-app guidance to provide contextual help
It’s inevitable there are always going to be some users who will struggle with your app, especially during the onboarding process.
However, if you can identify users who are inactive or dropped off during onboarding, you can target them with contextual help to guide them when they lose their way.
For example, instead of showing a generic product tour that users are likely to skip, you can trigger interactive walkthroughs to hand-hold users and avoid overwhelming them with the information they won’t retain (just like Kommunicate in the example below).
Contextual help works because they’re triggered when the user is more likely to need it—enhancing the learning experience.
Set product usage limit to drive account expansion
One effective strategy to encourage account expansion is setting a product usage limit.
By designing your product to have a cap on certain premium features, you can incentivize users to upgrade if they need more capacity—like in the screenshot below.
For example, if you offer a freemium plan, adding a usage limit can give you an opportunity to upsell an upgrade. This way, once the user has tasted what your product can offer—and found value in it—it will likely lead to a successful account expansion.
Send personalized offerings based on user behavior
Keeping track of feature usage and feature requests can present valuable opportunities to offer personalized deals.
For example, let’s say there’s a segment of users who submit feature request surveys. You could provide these users with early access to upcoming features that match their interests.
Or, if there are users who are particularly active with a specific feature, you could invite them to try a new upgraded version and give you their feedback.
Act on survey data to drive customer loyalty
Survey feedback is not only a great way to understand your audience’s pain points, needs, and desires. It’s also a useful way to provide proactive customer service.
You can see what’s missing in your product or causing friction, address it, and prevent users from searching online or reaching out to your customer service team.
For instance, if your CES surveys show a low score, it is a good indication that you need to improve the user experience either by adding more in-app help or reshaping your UI.
This way, users who give you feedback will feel heard, and you’ll get a step closer to building customer loyalty.
Key metrics to measure for product analytics
Now, what metrics can you measure for product analytics?
It’s hard to mention them all, as there are more product metrics than you can count with your fingers. Plus, the best metrics for your business will mostly depend on your model, role, and goals.
However, some KPIs are particularly essential for most SaaS businesses, and they include:
- Product usage. Which only shows data that represents how and when your customers are using your product. Including the top features used, the number of unique users, and their total interactions.
- Product adoption. The rate at which new signups become active users over a period of time. It indicates how good your product is at retaining new users.
- Retention rate. The percentage of customers that keep using your product over a period of time. The better the retention rate, the better your product is at keeping customers happy.
- Churn rate. The percentage of customers that stop using your product over a period of time. It’s the opposite of retention, and it indicates how many customers you’re losing over time.
- Product-market fit. Which uses a survey that asks users how disappointed they’d be if they could no longer use your product. It determines whether or not your product achieved PMF depending on the results.
- Customer satisfaction. The percentage of users who feel satisfied with your product or service. The more satisfaction, the more likely you are to retain customers.
- Net Promoter Score. Calculated based on a quantitative survey, NPS is the number of brand promoters your brand has over the number of detractors. It’s used for measuring customer loyalty.
- Customer lifetime value. The total revenue you generate from one customer throughout their entire lifetime. It indicates how lucrative it is, on average, to acquire a new customer based on their spending and their time spent doing business with you.
How to choose the right product analytics tool?
When you’re on the hunt for the right product analytics solution, it’s essential to consider your business needs first. So you can jot down your “must-haves,” “good-to-haves,” and “no-nos.”
But what are some essential aspects you should look for? Here’s what a good-fit product analytics tool should offer:
- Data diversity. Meaning it can handle different types of data such as user behavior, feedback, and in-app activity.
- Integrations. It should seamlessly integrate with the other tools and platforms you’re already using.
- Cross-team sharing. Options to build analytics dashboard no-code so you and non-technical teammates can share insights.
- Customization. The tool offers customization options for reports, dashboards, and graphics. So it should allow you to view and analyze the metrics that matter most to your business.
- Data compliance. Any tool you consider should have strong data security measures to protect your sensitive business information. It must include backups, data privacy conditions, GDPR compliance, and SOC2 type II certification.
Best product analytics tools to gain actionable insights
With a plethora of options in the market, which product analytics software tools that aren’t called Google Analytics is a better fit for you?
Let’s explore three of the best tools for SaaS business that we know:
Userpilot is a product management tool with robust product analytics features.
This means it not only helps you watch over your data, it also provides multiple ways to collect both behavioral data and user feedback.
For collecting data, Userpilot offers
- Feature tags and event-tracking to monitor user behaviors.
- Built-in tracking features to watch over the performance of your in-app flows, such as tooltips and checklists.
- Goal-based tracking to monitor how many users are achieving specific milestones (influenced by your in-app flows).
As for analyzing data, Userpilot has easy-to-set-up dashboards to:
- Watch over segmented audiences to find common patterns and responses on specific groups of customers.
- Feature analytics for tracking the performance of your products and how they’re used by customers.
- Funnel and path analysis to have a bird-eye view of how users move through the funnel, how they deviate from it, or how they achieve success with your product.
- Survey data to measure metrics such as NPS and CES. It also allows you to tag qualitative responses to find common keywords among detractors or promoters.
Amplitude is one of the most advanced analytics tools available to product managers. It offers all the analytics functionality that you would expect from a top-of-the-class tool, like advanced user segmentation, funnel analysis, and retention analysis.
It excels particularly at user journey analytics, as it has cross-platform analytics which allows you to track how users move between your native apps, mobile apps, and web pages. But as with many advanced tools, its learning curve can be quite steep for non-technical users.
It’s particularly good at tracking product analytics metrics (like in the screenshot below) to clearly show how users interact with your product. As well as showing data in real-time, which means that you’ll always be looking at the most current information.
But despite having a free plan, Mixpanel enterprise offers can get expensive and require an engineering team to set it up—just like many advanced tools in the market.
SaaS product analytics is an indispensable tool for understanding how your users interact with your product. It provides actionable data that can guide you to improve your product’s relevance, competitiveness, and profitability.
But remember, you need the right tool to unlock these valuable insights. So why not book a Userpilot demo to see how you can unlock new growth opportunities?