What is product usage data? At the risk of stating the obvious, product usage data is any measurement of how your SaaS product is being used.
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Product metrics are a gold mine for product insights: monthly active users, feature adoption rate, conversion rate. Traditionally, companies used surveys and study groups to understand what active users care about, to explore user behavior, and to experiment with new product features. While still a valuable input, the traditional methods are expensive, limited in scope, and sometimes produce different user behavior than what occurs naturally. With more modern data analytics, it is now possible for a SaaS company to collect data on what users are actually doing in their products without any intervention.
The concept of "data-driven decision-making" has been very trendy for the past decade or so, and product metrics are the key to making this a reality. The right metrics inform where to invest engineering/product/design (EPD) efforts, how to resolve customer satisfaction issues, and even how to upsell more customers. Ultimately, product metrics can help any product better shape its product strategy.
Some guides mistakenly refer to various figures as "product usage data." Some of the most common examples are product performance metrics or product adoption metrics, like net promoter score (NPS), customer satisfaction score, etc. Business metrics are another common perpetrator, like customer acquisition cost (CAC), monthly recurring revenue (MRR), and customer lifetime value (CLV). But neither tell you how your product is being used. Both customer satisfaction and financial figures are undoubtedly important and are related to product usage data but should be excluded from the definition of "product usage data."
While unexciting, there's a reason these product usage metrics are so frequently used by product teams -- they are simple and valuable. If you have a product, you should know these numbers.
Active users are most commonly measured on the time horizon of a day and a month. Daily active users (DAUs) and monthly active users (MAUs) are the number of users that use your product during a specific day/month. Some consider DAU and MAU to be a "vanity metric" for a CEO to flex their growth on Twitter or LinkedIn, but they are essential for any product. At its core, active user count gives you a bird's-eye view of your product. When your number of active users increases, your product is doing well and can be used to identify and measure power users, for example. When your number of active users decrease, something is likely going wrong (or it's just a holiday lull). Useful for a higher-level pulse check, active user counts do not produce targeted conclusions, since they are often influenced by various other factors (e.g., customer retention, marketing spend, product/feature launches).
Churn and retention are two sides of the same coin. Churn rate is the percentage of users that stop using your product during a given time period (e.g., during the user's first week/month). Retention rate is the percentage of users that continue to use your product after a given time period. High churn and low retention are signs that you need to help new users understand your product's value proposition faster and get them to an aha moment.
The percent of customers that used the target feature during the time period. Feature adoption rate can help your team pinpoint which features need more love and which are succeeding. Sometimes in-app messaging is all it takes to increase feature usage -- your users simply do not know the feature exists or how they should use it. In other cases, core functionality might be missing. User session playbacks and user event data can inform the appropriate antidote.
Usage frequency can be measured on two extremes, as well as everything in between.
Generally speaking, you want users to be engaged in your product, so you want to see higher usage frequency. In some rare cases, making certain processes more efficient can actually decrease usage frequency while increasing customer satisfaction.
Time-to-value measures how long it takes a user to reach one of your product's "aha" moments. For Slack, this is measured as the time it takes a workspace to reach 2000 messages. Decreasing TTV is a surefire way to improve the new user experience and convert new customers. If your TTV is high, consider:
Of all the metrics, error/bug rate is the most technical. It might take more effort to set up the right data capture here, but bug rates are critical. There are various ways of measuring bug rate, but the most common are:
Understanding how often bugs occur relative to usage can be vital for improving the customer journey and increasing customer loyalty. While bugs are inevitable if your team is shipping quickly, there needs to be a balance in quality. Otherwise, you risk user churn.
Across each of the metrics in the previous section, you might have noticed each metric did not have an exact definition. It is important to choose product metrics and a product metrics framework that makes sense for your product. Here are a few examples:
These examples of contextual layers underscore how important it is to make sure your product metrics fit your product. The "wrong" product usage metrics, like choosing the wrong key performance indicators (KPIs) for a team, can result in wasted effort. The "right" metrics lead to better prioritization and business success.
Product usage data is useful for many teams, including, but certainly not limited to, the ones highlighted below. Integrating product metrics across your organization leads to better decision-making. All teams benefit from objective data, especially as they scale and the number of subjective opinions increases.
Obviously, product metrics are fundamental to product teams. Equipped with the right product metrics framework, a product manager can understand the entire customer journey -- from the new user experience to power user experience.
Product engagement metrics, like feature adoption rate and feature usage rate, can help product managers more effectively prioritize EPD efforts. For example, the latest product usage analysis reveals that a feature has high adoption for new users, but low feature usage for existing users. This might indicate that users interact with and discover the feature, but do not understand its value. Product managers might then look for wins with informational tooltips, templated creation flows, or functionality improvements to better help customers meet their business goals. Or sadly, it might be a feature you need to unship.
A Customer Success (CS) team cares about:
Understanding customer behavior allows a CS team to tailor more useful content to clients. For example, say a customer is heavily using feature A but not using feature B at all. In future interactions with that customer, it makes sense to highlight enhancements to feature A and how feature B could help them get more out of the product. Data-driven decisions like this can propel a company to reduce churn rate and improve customer loyalty.
Like the Customer Success example, engagement metrics can be critical for an Account Executive. Knowing which members of a prospect organization are highly engaged in your product can make it easier to find a product champion. Similarly, real-time insight into features usage empowers your Sales organization to drive more useful and relevant conversations with prospects. In the long run, deploying product metrics in Sales and CS raises your average customer lifetime value.
Growth teams can also use product metrics to make better decisions. There are a number of primary use cases on the Growth side:
Embedding product metrics in your team's growth strategy produces more efficient growth efforts: increasing new customer growth rate, decreasing customer acquisition costs, and improving market adoption.
When product metrics are collected accurately and made easily accessible, it is just a matter of time before they work their way into everything your company does. The previous section briefly mentioned a few examples of how you can use usage data to inform decisions. Below you'll find three in-depth examples of ways that your team can turn product metrics into product actions.
To better illustrate this use case, let's say your team has decided to launch a new feature that allows users to use GPT-4 inside your product.
How does the team arrive at this decision? Customer feedback data includes requests for this feature. However, the team is well-equipped with their product's usage data and already has a clear sense for what users want.
Now that the feature has been launched, your product managers are wondering how they can improve the feature. A few key metrics help the team hone in on a few targeted improvement areas.
Using product metrics in this scenario has allowed the team to make significant decisions with high confidence:
The Sales team for a B2B SaaS product -- let's say a website-builder tool, like Webflow -- has been considering adjusting their pricing structure.
From customer and prospect calls, the Sales team suspects that the free tier is too generous. Looking at their overall usage data, they can confirm this.
The question then moves to how the pricing tiers should be adjusted.
From the findings above, the Sales team proposes two major changes for new customers:
After implementing the pricing changes outlined above, more new customers start using your product on a paid plan, and more free-tier customers are upgrading to a paid plan. In turn, your average revenue per customer is on the rise.
A few months ago, the team added user onboarding flows to help users find your product's two main features.
The team isn't sure whether these flows are improving the product experience. Your product managers dive into usage data on an A-B test format (before/after the user onboarding was introduced) and uncover some key takeaways.
The top-level metrics, like churn and upgrade rate, have not changed. However, the onboarding flows have been effective at driving attention to the important features. The product challenge is making the features stickier.
Instead of wasting time on solving a problem that doesn't exist ("fixing" the user onboarding flows), the team knows where to prioritize their efforts. EPD changes are now more likely to change important metrics, like reducing churn.