In-App Ratings to Uncover User Needs and Personalize Experience
As a product manager its very critical to understand the pain points and customer sentiment of a user. Through App Store ratings, product managers can analyze trends in the user sentiment over time across different platforms such as iOS and Android. Moreover, analyzing the reviews of users can help understand the magnitude of various issues on the app. There are tools such as App Annie and App Bot that help with advanced analytics based on app store ratings and reviews. These tools help in classifying reviews into various categories, obtaining visual trends on user sentiment and comparing ratings against those of competitors.
However, App Store ratings provide very limited insights into customer needs. There is no information on the customer’s plan type, historical engagement, features used, etc. Without such valuable information, the analytics is restricted to broad trends without real actionable insights. Moreover, the reviews left by the users are often not insightful with meaningless rant. There is an adverse selection problem with reviews since users who are unhappy are more likely to leave reviews on the App Store. Lastly, there are issues of fake reviews or unintended reviews on the app store that add noise to the data. For instance, in 2017, it was reported that CNN’s iOS app store was review bombed with malicious review.
Due to all these limitations from App Store ratings, In-App ratings are a better source of information for product managers. In-App ratings are the ratings provided by the users when they are using the application and are different from the ones on the App Store. There is more control over when to collect the In-App ratings from the user based on a wide range of trigger conditions that can be pre-determined. A user can be prompted to leave a rating or a review after performing a certain task or using a specific feature inside the application. Such data can be valuable to understand the customer sentiment not just for the overall app but for specific features. Based on these insights, a product manager can help improve features that have a lower rating or promote those with a higher rating.
The In-App ratings also provide opportunities for advanced analytics. For instance, correlations can be run on In-App ratings with plan type, duration of usage, session lengths, specific features used, etc. Such analysis can be powerful to understand what users care about on the app. One could argue that such insights can be obtained from market research and user interviews. However, what users self-report vs what they actually do can be quite opposite. Therefore, real time user data is often more reliable compared to market research. A product manager can come across various trends that are not obvious by running advanced analytics. It might be hard to establish causality from strong correlations. However, such analytics provide a ground to form a hypothesis that can be further tested through various methods.
In addition to advanced analytics models, In-App ratings can be used to train machine learning models. For instance, these ratings can serve as a valuable input to models that can generate a churn score. This score can be used to run targeted campaigns to retain users. Additionally, the users who provide a higher rating can be shown promotions to upsell premium offerings or establish loyalty programs. With such a wide range of benefits available from In-App ratings, product managers should definitely leverage them to better understand the user base, make impactful changes to the app and run personalized campaigns.