What Is Feature Usage Rate and How to Measure It

Tracking product analytics, such as feature usage, is useful for understanding feature adoption and user engagement. Plus, it also helps gain valuable insights on how to enhance the customer experience and improve user retention.

But what exactly is the usage rate and how is it calculated? We answer all these questions and more down below, so let’s dive in!

TL;DR

• The feature usage rate is an engagement metric that measures the number of users who have adopted a specific or new feature within your product.
• Apart from regularly measuring feature usage rate, there are certain moments when you should pay special attention to it. These moments could be after initial onboarding or a new feature launch, or when you see a sudden drop in activity.
• Here is the 4-step process that makes measuring feature usage rate easy:

1. Goal-setting – why do you want to track usage?
2. Metrics selection – which key metrics are most relevant to your goal?
3. Data collection – use tools that automatically track and collect usage data
4. Perform analysis – what is the usage data telling you about the feature adoption rate?

• Apart from these steps, here are proven best practices to improve your feature usage analysis:

1. Segment users for a more relevant analysis.
2. Use data visualizations to easily identify trends.
3. Perform heat map analysis for a quick overview of feature engagement.
4. Collect user feedback to understand what they think of a new feature.
5. Find the right analytics tools for deeper insights into feature usage behavior.

• While focusing on feature usage rate, don’t entirely ignore other SaaS metrics. Some alternative key metrics to focus on include feature adoption rates, user retention and engagement, and customer satisfaction scores.
• If you’re looking to improve your feature usage rate, book a Userpilot Demo to see how we can help you with it.

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What is the feature usage rate?

The feature usage rate measures how many users have engaged with and adopted specific features in your product or service.

A high feature usage rate signals a healthy product with a strong product team that introduces successful features that users need and easily use.

While the two may seem interchangeable, keep in mind that feature usage is not the same as product usage. Product usage focuses on the user’s behavior toward the overall product, whereas feature usage only focuses on a particular feature.

When should you track the feature usage rate?

Measuring the feature usage rate is important to uncover the relevance and impact of a specific feature on your customers. To further understand user engagement, it is good practice to regularly measure this metric.

However, there are certain instances across the user journey where tracking feature usage becomes an absolute necessity. Some of these instances include:

• After user onboarding: Track feature usage among new users to gauge the impact of onboarding guidance in driving feature adoption.
• New feature launch or product update: Check the performance of new features or updates to see if they’re beneficial in improving the user experience or not.
• Sudden drop in user activity: Measuring feature usage at these points helps determine adoption drop-off points, thereby narrowing down improvement areas.

How to measure feature usage rate?

You should follow certain comprehensive steps to perform an effective feature usage analysis. Before going any further, you should focus on the basics, such as goal setting, metric selection, and data collection.

And that’s exactly what we’re going to begin with, so let’s get to it.

Determine your tracking goal

If you approach your feature adoption analysis without a goal in mind, you’re going to end up taking a very haphazard approach.

Instead, ask why you want to track feature usage in the first place. This will help define your objectives and guide your efforts. Plus, once you set a goal, you will also better understand what data to look for or which key metrics to track.

You can use goal-setting frameworks to get started i.e. the S.M.A.R.T one.

Define key metrics to measure feature usage

After goal-setting, finding the right metrics to track becomes much easier. For example, if your goal is to measure customer engagement with a particular feature, then you can look at engagement metrics such as:

• Number of clicks
• Time spent
• Frequency of use

It makes sense to use these metrics for the above goal because they track the frequency of engagement touchpoints as well as the level of involvement.

Implement tracking to collect feature usage data

Tracking metrics manually is a lengthy process. You have to collect data, compile and compare it, and repeat the process regularly for meaningful results. In short – it burdens your product team.

Instead, try tracking tools that enable you to seamlessly automate and scale your data collection efforts. For instance, Userpilot lets you easily tag features to track user behavior and perform feature audits.

You can also use event tracking if you want to track multiple events as a group in instances where a process may have multiple events.

For example, let’s say you have a billing app with an auto-billing feature. To utilize the specific feature, users have to click on it, fill in auto-billing details, click set up, etc. All these count as multiple events, all within the same process.

Use collected data for feature usage analysis

Lastly, it’s time to analyze the data and make sense of it so you can achieve your goal.

Let’s suppose your goal was to understand if the feature usage rate increases after users finish onboarding. Tracking usage behavior provides you with enough data to answer that question. You might see that engagement metrics have decreased after onboarding, resulting in a low feature usage rate.

This data also signals that there might be issues in your onboarding that are causing such poor feature adoption. To boost usage and adoption, you could try improving onboarding guides and introducing feature enhancements.

Best practices for analyzing feature usage rates

Now that you know the basic steps, it’s time to take your feature usage analysis to the next level. To help with that, we’ve made a list of all the best practices you need to analyze the real impact of your features.

Segment your users when tracking the feature usage rate

For a more granular analysis, group users into various segments. This way, you can notice patterns specific to certain user segments and uncover insights into how different groups engage with features.

A common user segmentation approach is to group users into active and inactive ones. As you start tracking, you’ll see that active users engage with feature A while inactive users don’t.

This insight provides room for improvement. To solve the low usage rate among inactive users, you could try implementing tooltips to drive feature discovery as they come into the app.

Visualize usage data to understand user behavior patterns

Visualizing data always helps make better sense of it. So use graphs, analytics charts, diagrams, and other visual aids to your advantage. Seeing the data presented in such a way reveals behavior trends and patterns that would otherwise be less apparent in raw data.

You can also use trends to visualize usage data in Userpilot. Visuals can show you when users are most active if they are utilizing a new feature, or just compare various features’ usage rates for the same segment.

Leverage heat maps to analyze feature usage rates

A heat map is a visual representation of user interactions, so it’s a great way of tracking what touchpoints users engage with the most.

Plus, heat map analysis is also easy to perform. Just a mere glance at the map is enough to give you a quick overview of the high and low activity of different features.

Gather customer feedback for specific feature insights

To get a holistic picture of your feature usage rate, supplement quantitative data with qualitative insights gathered through customer feedback.

For example, you can trigger instant in-app feedback collection with interaction-based surveys using Userpilot.

This helps you gain actionable insights into why users are not using your feature. Since this feedback is in real-time, when the user is most engaged, it becomes a great resource for future improvement.

Use an analytics tool to track changes over time

To continuously monitor and compare feature usage over time without overburdening your product team, choose to implement an analytics tool, like Userpilot.

Userpilot can help you monitor key metrics automatically with its analytics dashboards, like the Product Usage Dashboard, without requiring any setting up.

That isn’t all, though. There are tons of other analysis reports you could have access to in minutes. Some of these include the trends of active users over time, user stickiness, top features and events, and the most engaged users.

Other alternative metrics to feature usage rate

Here are some more key metrics you should know of as well. Use these in conjunction with the feature usage rate instead of solely relying on one alone:

• Feature adoption rates: Tracks how likely users are to stick to your feature because of the value it provides. To calculate, divide the feature’s monthly active users by all the active users during that period.
• User retention: Measures the number of users who continue using your product’s features after a given period. To calculate, subtract the newly acquired users from the total paying users at the end of a period. Then divide the result by the total paying users at the start of the period.
• User engagement: Measures the quantity and quality of user interactions with your product or feature. Examples of engagement metrics include conversion rate or activation rate.
• Customer satisfaction: Evaluate your users’ opinions and gather information about their experience with your product or features. Examples of such metrics include CSAT, CES, or NPS surveys.

Conclusion

You can’t afford to keep churning out new features while users refuse to engage with them. Instead, focus on the root cause behind your feature adoption problem: your feature usage rate. And to tackle your dropping usage issue, all you need is this article.

We’re sure that by setting clear goals, selecting the right metrics, and leveraging analytics tools, you’ll be able to improve usage in no time!

Want to get started on increasing your feature usage rate? Get a Userpilot Demo and see how you can improve feature adoption and user satisfaction.

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