Understanding what features your customers use and which features are most valuable is critical to your Product strategy. This tutorial will explain the key metrics to measure engagement and how use Google Analytics to track feature usage.
How to Measure Product Feature Engagement and Value?
Usage
The most basic measure of a product feature is whether anyone is even using it. Luckily, this is very easy to track using Google Analytics once you have event tracking set up on your site. In the example video, we are using the Google demo account which is of the online property that sells Google-branded merchandise. If you want to follow along and have the data look the same, sign up for the demo account and in the date range in the reports select 1/1/2018 - 2/28/2018.
In Google Analytics, total events tells you how often the feature was used and unique events tells you how many different sessions used the feature at least once. You can find these metrics following these steps:
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Navigate to the Behavior - Events - Overview report
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The top line chart shows you total events by day across all events. You can add unique events to the graph using the drop down
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Scrolling to the bottom you see data summarized by the category of events. Depending on your event tracking set up, it might make more sense to view specific “actions” or “labels”. For the demo account, the “actions” level describes the critical e-commerce steps as individual events.
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You can see that “Quickview Click” is the most used feature on the site. You can get more detailed information by clicking on the event itself This brings you to a similar report but filtered to that specific event
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You can again add “unique events” to the line chart
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Scrolling to the bottom you can see the total events and unique events for the feature (84,616 and 61,878, respectively)
Also of interest is what percent of sessions engaged with the feature. Unfortunately Google Analytics does not make it easy to add that metric. But you can quickly calculate it yourself by getting the total number of sessions in the same time period from the Audience - Overview report (175,811). So the usage rate is 61,878 / 175,811 = 35%
Success Rate After Feature Engagement
Often, you’re not interested in the feature for its own sake, but because you want that feature to lead users to completing a desired action. Using the previous example, the Quickview feature is probably only useful to the Google Merchandise Store if after using it, people go on to complete a purchase.
Your “success” doesn’t have to be a purchase. Perhaps you define success as having the user come back more often (increasing retention rate).
Unfortunately, these types of metrics are not well suited for Google Analytics. While you could create custom segments of those who engaged with the feature and those who did not, you wouldn’t get a fair comparison. This is because users who used a feature like Quickview are probably already on the purchase path, and those who did not have likely self-selected out of the purchase path altogether (perhaps even bounced). So you can’t fairly compare all users who did not use Quickview to those who did. Instead, you should consider launching your feature as an A/B test and compare performance of those who had the opportunity to use your feature and those who did not.
Customer Satisfaction
Another key measure of your feature performance is how much your customers like it. A feature could have high usage and be critical to complete a purchase, and yet be a feature that your customers struggle with.
There are a couple other ways that you can get a sense for how much your customers like the feature:
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Mine support tickets your customer sent about the feature
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Send a survey about the feature to a set of customers who have used it
Conclusion
Understanding what features your customers use and which features are more valuable is critical to your Product strategy.