Top 8 user engagement metrics to track and measure

A key challenge for a Product-led growth (PLG) company is defining which user engagement metrics to focus on. After all, with a strategic approach to analyzing engagement metrics, you can improve the entire customer journey and boost your bottom line.

In this article, we explore which user engagement metrics are most important and look at how you can integrate product data with insights from across channels to drive more meaningful analysis.

User engagement metrics reveal the extent and manner of user interaction with your product. Qualitative and quantitative insights demonstrate the value users gain from your product and highlight the sources of that value.

Engagement metrics focus on user activity and interaction, while retention metrics measure loyalty. However, they are interconnected. More engaged users often become product advocates over time. This is because engaged users develop a deeper connection with the product, finding value and satisfaction that they want to share with others. According to Bain & Company report, customers who engage with a brand are 50% more likely to recommend it. Engaged users are more likely to advocate for a product because their positive experiences build trust and loyalty, making them natural promoters of the brand to their network

User metrics allow you to understand customer behavior in great detail. By building out the user journey, you can understand which features people love and return to and which areas of your product they struggle with. This means you can implement efforts to target churn and boost revenue.

When you integrate product data with user engagement metrics from across channels, such as a product analytics platform like Optimizely Warehouse-Native Analytics, the potential becomes even greater. For instance, your marketing team can improve campaigns with insights about which features people love, or your customer support team can proactively address common issues.

By giving you a complete picture of user behavior, user engagement metrics help you to align your product roadmap. Particularly with an easy, self-serve solution for analytics, different teams can work together to meet user needs and drive loyalty.

Here are the most important metrics for user engagement and a formula for how to calculate them. We then explore, in more depth, how you can integrate these into a customer analytics strategy that allows you to improve user engagement over time.

Product Engagement Score combines several engagement indicators to show how users interact with and derive value from your product. You can calculate it by working out the following:

While your PES gives an indication of overall product health and engagement, the individual aspects also provide useful insights. For example, by looking at the adoption rate, you can feedback to your marketing team on which segments of your users are most engaged. This enables you to boost upsells and upgrades.

Formula: Adoption rate + stickiness rate + growth rate / 3

Product Adoption Rate shows how many of your features users are engaging with and how many users engage with all your product’s features. For example, say there are 5 features involved in purchasing a product on your ecommerce app and if a user interacts with only 2 while making a purchase, they’d have an adoption rate of 40%.

Using behavioral analytics software like Optimizely Warehouse-Native Analytics, you can set stages to define adoption events. Then you can create multi-level funnels to visualize the user’s journey through several adoption events. This allows you to optimize each area of your product and customer support to boost engagement more precisely.

Formula: New active users / total users x 100

Your Product Onboarding Engagement Rate shows how effectively users are completing your onboarding process. You can measure it by calculating how many people complete a particular event such as an interactive walkthrough or tutorial.

Armed with this knowledge, you can improve your product onboarding engagement rate by feeding back to your customer support team. By using an intuitive product analytics platform like Optimizely Warehouse-Native Analytics, you can send them easy-to-understand reports in minutes.

Formula: Users who complete onboarding / total users x 100

DAU and MAU are user metrics that show how many active users you have on a daily or monthly basis. After defining which event you will count as “use,” you’ll have a baseline to refer back to when you make changes to your product. As we explored earlier, you can also divide your DAU by your MAU to understand how sticky your product is.

Improving DAU and MAU means analyzing data from across channels. You’ll need to understand, for example, which channels are driving the most engaged users in order to concentrate your marketing efforts.

Formula: Total MAU= Number of users who complete a defined action within a month

Total DAU= Number of users who complete a defined action within a day

Average MAU/Annual MAU= Sum of each month’s active users / 12

This user engagement metric measures how well you are increasing the number of active users in your product by assessing what percentage of users reach an activation milestone. This milestone could be anything from making a first purchase or using a particular feature.

You can better understand and improve your activation rate by segmenting your users into cohorts inside a product analytics platform like Optimizely Warehouse-Native Analytics. This allows you to personalize your approach based on the unique needs of different user cohorts.

For example, you might create one cohort of users who were highly engaged from the start and another who use your product relatively infrequently. This would influence your company’s marketing and customer support efforts since you could upsell more advanced features to the more engaged and adopt a more education-focused approach for the latter.

Formula: Users who reached activation milestone / total users

Feature adoption rate tracks how many users have adopted a specific feature. To calculate it most accurately, you need to consider how many people have been exposed to your feature, activated it, and used it several times. You should also look at core feature usage to understand if your main features are intuitive enough or if your users need help.

To improve your feature adoption rate, you can experiment with simplifying the onboarding process, sending regular reminders, and providing personalized in-app support.

Formula: Users of a specific feature / Total users

Tracking how many people convert from using your product for free to paying is key to understanding how your engagement initiatives are working. A higher conversion rate indicates that more people understand the value of your product through your trial, marketing campaigns, and customer support.

Improving the trial-to-paid conversion rate requires leveraging data from across channels to gain insights into user preferences and pain points throughout the customer journey. For example, with Optimizely Warehouse-Native Analytics, you can track how effective your marketing content is at driving conversion of specific segments or cohorts.

Formula: Users who convert to paid or premium / total users on freemium

Net Promoter Score indicates how likely your users are to promote your product to others. Your most engaged users will rank highly when you ask them a question like “On a scale of 0 to 10, how likely are you to recommend this product to a friend?” Those with a score of 6 or above are known as promoters while those below 6 are known as detractors.

You can boost your Net Promoter Score by nurturing relationships with your existing users. For example, by having your marketing team create a loyalty program for those who are already engaged with your product, you can increase the likelihood that they recommend you to others.

Formula: % promoters - % detractors

Though your product data can give you an idea of how engaged your users are, it can leave you with questions. You might be wondering why people are engaged with certain features and not others, for example, or why certain segments are more active than others.

To answer many of these queries, you need a solution that brings together data from across channels. By using a product analytics platform like Optimizely Warehouse-Native Analytics that sits on top of your data warehouse, you can access accurate customer support data, such as the number and types of support tickets raised, to understand issues that may be holding engagement back.

In the same place, you can view marketing analytics to see insights such as which channels are driving activation.

You’ll also be able to: