The 4 Important Dimensions Of Feature Adoption Analytics


It is well known that companies that use user onboarding software ensure user proficiency quickly thereby driving the metrics of product adoption like retention and active usage. Similar to product adoption, feature adoption is another user activation metric that is measured not by logins but by the user interaction with a specific feature.

To ensure the success of feature launches, software developers and product managers need to ensure its performance of 4 key dimensions of feature adoption analytics. They are:

  • Adoption breadth: This helps measure the acceptance of a feature across a targeted user base or segment. Thus it would not be wrong to say that this feature analytic shows the initial appeal of the introduced feature.
  • Adoption depth: This is a measure of the number of times a feature has been used by key users. This analytic look into the ease with which the software can be used. If the feature appears difficult even after using the feature adoption software then it can hamper its wide acceptance. Thus this analytic needs to be closely monitored to solicit feedback on the feature’s acceptance.
  • Adoption time: This analytic measures the time that is taken by users to start using the feature. Product managers would ideally like to ensure quick adoption of every new feature introduced; hence this dimension plays an important role in determining whether the adoption process is aligned to the existing plans and goals set by the developing company.
  • Adoption duration: This helps to measure the duration of usage of the users after they start using a new feature which has been introduced. Ideally only when users use it for longer durations would they be willing to pay for the addition of a new feature. Thus this analytic keeps a track of the retentiveness of the feature and uses its data to understand if the feature will provide real value even after its initial novelty.

Each feature launched provides an opportunity for value addition. But unused features tend to have a converse effect since it lowers the perceived value for the customer and negatively affects their willingness to continue paying for the product. Thus new features need to score well on the above 4 analytic dimensions.