There are six ways to increase revenue with product usage data:
- increase revenue by increasing trial conversions
- increase revenue by spotting up-sell opportunities
- reduce churn by spotting declining usage
- increase revenue by finding and mobilizing evangelists
- increase revenue by improving usability
- increase revenue by increasing prices
Collecting and understanding each of the four dimensions of product usage enables us to maximize the revenue increases that result from this analysis.
Product usage dimension one: frequency
This is the easy one. How often does a user engage your application? Think “logins per week”.
Declining frequency, or no use at all, is a strong signal for churn risk.
And that’s about it. Don’t be tempted to infer too much from this one metric, such as “active use = satisfied user”. You’re covering the basics of measuring any use versus no use.
Dimension two: feature usage
Now we’re getting to the juicy stuff. Imagine all of the features and functions in your application.
- At a global level, which features are used the most? Which are not used at all?
- And for a specific user, which features do they use?
- And for a group of users inside a business customer of yours, what’s their feature usage pattern as a group? Is the product widely adopted, or only by one person?
To get started, consider the granularity of what you’re trying to measure. Don’t go too deep at first by measuring every button action in your user interface. Otherwise you get a list of dozens or even hundreds of unique actions. Which is too much data and introduces “noise” into your analyses. Even powerful statistical analysis will yield inconclusive findings if too many unique actions are thrown into the mix.
Instead, start with a list of 3-6 high-level features. Where “feature” is a fairly high level concept like “uploaded a photo” or “shared with someone else” or “created a new project”. You can always refine your approach to get to 10-20 features in your list. You might even go beyond that once you master how to interpret and act on the data.
Remember: start with a few features to measure and grow only when it suits your needs.
Dimension three: configuration
Many products, especially business-to-business products, are highly configurable. Even in a consumer app, users can configure many settings like notifications.
The act of configuring an app beyond its out-of-the-box settings is a form of user investment in your app. And with that investment can come loyalty.
Just like feature usage, the goal is to get an understanding of the patterns of configuration settings that correspond to loyal users.
Looking at patterns on configuration settings also helps you spot new segments of users, and areas of the user experience you might want to optimize.
Dimension four: volumetrics
Volumetrics means the degree to which your app is used. For example, you’re the vendor of an online project management app. How many projects, over a period of time, does a user run in your app? How many projects in total does a given customer run?
Another example: you’re the vendor of an online backup/sync/share app. How many files does a user upload, and of what size?
Most apps have 1-3 primary data objects worth measuring. The greater the volumetric usage, the greater the dependency of the user on your app.
Like measurement of features and configuration settings, volumetric measures can also lead to insights about user segmentation and user experience issues.
We’ve reviewed the four dimensions of product measurement: frequency, features, configurations and volumetrics. We’ve talked about the revenue insights that can be gained, such as trial conversions, churn prevention and up-sells.