What is Feature Usage and When to Measure It

Introduction

Did you know that some of your product's features may be hurting your user retention rates?

By tracking how users are interacting with specific features, you can identify which ones are most valuable and which ones may need improvement.

In this article, we'll explore how to use feature usage data to identify potential issues and keep your users engaged and loyal.

What is feature usage?

Feature usage is a metric used to measure how often and in what ways a particular feature in a product is being used by the users.

It helps product teams to understand whether users are finding a particular feature useful or not.

By measuring feature usage, you can identify which features are popular among users and which ones are not, and make data-driven decisions about what to prioritize in future development efforts.

Why to measure feature usage?

Understand user behavior

Measuring feature usage helps to understand how users are interacting with your product. It can provide insights into what they find valuable, which features are popular, and where users may be struggling or experiencing issues.

Prioritize product development

By measuring feature usage, you can identify which features are being used the most and which ones are not. That helps to prioritize future development efforts and focus on building the features that users actually want and need.

Optimize product performance

By identifying which features are causing performance issues or using a lot of resources, you can make targeted improvements to improve overall product performance.

Better understand the user needs

You gain a better understanding of their users' needs and preferences. This helps to make more informed decisions about what features to prioritize and how to improve the overall user experience.

Benefits of improving feature usage score

Increased user engagement

By improving feature usage, you can increase user engagement with your product. Users will be more likely to continue using it and recommend it to others if they find it useful and easy to use.

Improved user experience

Focusing on improving feature usage can also lead to providing a better user experience. By identifying which features are causing confusion or frustration, you can make targeted improvements that will make your product more enjoyable and satisfying for users to use.

Better product roadmap

By understanding which features are being used most frequently and which ones are not, you can make more informed decisions about your product roadmap. Your team can focus on building features that your users actually want and need, which can help you stay ahead of the competition.

More efficient development

Your development process becomes more efficient. By focusing on the features that are most important to your users, you can avoid wasting time and resources building features that won't be used or that don't provide significant value.

Competitive advantage

By improving feature usage, you can differentiate your product from competitors and gain a competitive advantage in the market.

Risks connected to focusing on feature usage

Neglecting other important metrics

Focusing solely on feature usage may lead you to neglecting other important metrics, such as customer satisfaction or retention rates.

Overlooking important features

By focusing solely on feature usage, you may overlook features that are not used frequently but are still important to certain user groups. This may lead to dissatisfaction among these users and result in lost business.

Encouraging feature bloat

Focusing solely on feature usage may encourage your team to add more features to the product in an attempt to increase usage, even if those features are not necessary or useful to users.

Losing sight of the bigger picture

Focusing solely on feature usage may cause your team to lose sight of the bigger picture, such as the overall goals of the product and the company. This may result in a product that is not aligned with the company's mission or vision.

Inaccurate data

Feature usage data can be inaccurate or incomplete, especially if users do not use the product consistently or if data collection methods are flawed. Relying solely on this data may lead to incorrect conclusions about user behavior and product development priorities.

Solution: compose a set of metrics that will provide your team with a big picture. Don’t focus solely on measuring feature usage. Choose metrics that correspond with your goals and focus on business goals, not skyrocketing a single metric score.

How to measure feature usage?

Analytics tools

Many analytics tools, such as Google Analytics, offer features that allow product teams to track user behavior within the product. These tools can track which features are used, how frequently they are used, and how long users spend on each feature.

In-product tracking

You can also build tracking mechanisms within the product to track user behavior. This can include tracking which features are accessed, which buttons are clicked, and which pages are visited.

User surveys

Your team can also survey users to understand which features they are using and why. Surveys can be sent out periodically or triggered when users access certain features.

User testing

You can conduct user testing sessions to observe how users interact with the product and which features they use. This will provide qualitative data about feature usage that complements quantitative data.

Feedback channels

You can also gather feedback from users through channels such as customer support, social media, or community forums. This feedback can provide insight into which features users find valuable or frustrating.

Feature usage metric - alternatives

Net Promoter Score (NPS)

This metric measures how likely users are to recommend the product to others. It is based on a simple survey question: "How likely are you to recommend this product to a friend or colleague?" Users rate their likelihood on a scale of 0-10, and the NPS is calculated by subtracting the percentage of detractors (users who gave a rating of 0-6) from the percentage of promoters (users who gave a rating of 9 or 10).

Customer Acquisition Cost (CAC)

This metric measures the cost of acquiring a new customer. It takes into account all the costs associated with sales and marketing, such as advertising, salaries, and overhead. A low CAC indicates that the product is efficiently acquiring new customers.

Churn rate

This metric measures the percentage of users who stop using the product over a given period of time. High churn rates indicate that users are not finding enough value in the product to continue using it. Product teams can track churn rates over time and identify patterns or factors that contribute to high churn.

Engagement metrics

These metrics measure how engaged users are with the product. Examples include time spent on the product, number of sessions per user, and depth of engagement (such as the number of pages or features accessed per session). High engagement indicates that users are finding value in the product and are likely to continue using it.

Revenue metrics

These metrics measure the revenue generated by the product. Examples include total revenue, revenue per user, and revenue per feature. By tracking revenue metrics over time, product teams can identify which features or user segments are most profitable and prioritize development efforts accordingly.

Which one to choose?

• Net Promoter Score (NPS): NPS is a good metric to use when you want to understand how loyal your customers are and how likely they are to recommend your product to others. Use NPS when you want to measure the overall satisfaction of your user base and track changes in sentiment over time.
• Customer Acquisition Cost (CAC): CAC is a good metric to use when you want to understand the efficiency of your sales and marketing efforts. Use CAC when you want to track the costs associated with acquiring new customers and identify areas where you can improve efficiency and reduce costs.
• Churn rate: Churn rate is a good metric to use when you want to understand how many of your users are leaving your product and why. Use churn rate when you want to identify patterns or factors that are contributing to user attrition and prioritize efforts to retain users.
• Engagement metrics: Engagement metrics are good to use when you want to understand how users are interacting with your product and which features or content are most engaging. Use engagement metrics when you want to identify opportunities to improve user experience and increase user retention.
• Revenue metrics: Revenue metrics are good to use when you want to understand the financial performance of your product and which features or user segments are most profitable. Use revenue metrics when you want to identify opportunities to increase revenue and prioritize development efforts accordingly.
• Feature usage: Feature usage is a good metric to use when you want to understand how users are interacting with specific features of your product. Use feature usage when you want to identify which features are most popular or valuable to users and which features may need improvement or removal.

Combine a set of metrics to assess the health of your product

To fully understand how your product is doing, you need to track more than one metric - you need to grasp a full picture. Below there’s an example of the combination of metrics that could give you a more accurate picture of your product’s performance. That's the tactic used by the top fintech software development companies.

1. Customer satisfaction: with this metric, you can track how satisfied customers are with your product. It can be measured using surveys, reviews, or customer feedback. High customer satisfaction indicates that the product is meeting user needs and expectations.
2. Retention rate: this metric helps you to follow the percentage of users who continue to use the product over time. High retention rates indicate that users find value in the product and are likely to continue using it in the future.
3. Conversion rate: with this metric, you learn what’s the percentage of users who take a desired action, such as making a purchase or signing up for a trial. High conversion rates indicate that the product is effectively communicating its value proposition and driving user action.
4. Time to value: following this metric allows you to learn how quickly users can derive value from the product. High time to value indicates that the product may be difficult to use or lacks key features that users need to achieve their goals.
5. Feature usage: with this metric, you check how frequently and how many users are using each feature of the product. It can help you to understand which features are most important to users and prioritize development efforts accordingly.

Remember: Measuring is only the beginning. You need to analyze the results and act on them. 

Summary

Measuring the right metrics, like feature usage, can provide insights that lead to significant improvements in product performance. Explore the world of software development metrics and compose a set that will work best in your case.

Looking for more learning resources?

Continue your learning process with one of these articles:

• how to use software development metrics
• how to deal with technical debt.