Amanotes’s pain points & use case

Last year, Amanotes were on the hunt for new ways to scale up their user acquisition strategy, while maintaining high customer satisfaction with their huge user base. They looked toward building a response strategy to deal with user reviews, which they predicted could bring in positive outcomes for the retention rate & user acquisition. Amanotes wanted to set up a system that would mass reply to their mountain of user reviews to see what return they got from doing so.

The logic they used to justify this was:

Most significantly, these insights were a rich source of qualitative data that has helped Amanotes better understand their users’ needs. Through Semantic analysis, reviews were automatically tagged and categorized, offering a level of granularity that helped them understand what to prioritize.

The team could segment & track reviews according to the particular topic & user need, such as ‘Ads’, ‘Subscription’, and ‘Bugs’.

Amanotes’ user base is huge, and as previously mentioned, many reviews were left unanswered on the app stores. However, a decent chunk of reviews were not actionable or insightful to the team — such as one sentence reviews (“nice game!”) or flamers (users saying irrelevant or negative things without providing reference to a specific element of the game). These could be easily tackled using auto-reply or Report a Concern automation. 

The Amanotes team used Semantic tags to filter reviews which revealed product insights. These would be passed to a product analyst, who would sift through them. When the analyst found a hint of a valuable insight, such as on gameplay, content, or monetization, they would extract it and analyse it beside app usage before presenting both qualitative and quantitative data in a monthly presentation to the team. Based on the findings, there would be an informed decision on how to take action on the insights gathered. 

This resulted in a watertight product feedback loop that provides Amanotes the insights they need to improve their product and customer experience.

By utilizing Semantic analysis, Amanotes learned that users who don't like ads aren’t going to buy a VIP subscription to remove adverts. This was consistent with findings from app usage data, where they saw that the majority of their subscribers were paying for a subscription before even seeing any ads.

They also learned from reviews that their subscription price was too expensive, and many users preferred a one-time "Remove All Ads" In-app purchase (IAP) instead. Amanotes realized that providing this type of purchase could improve IAP revenue, retention, and app ratings at the same time.

 Another reason they learned for why users left low ratings was because of a lack of preferred content (such as songs from K-pop groups), but that these users were much more likely to update the review positively when responded to by the support team. This indicated to Amanotes that though these were sensitive users, they could easily won over with the right approach. Of course, all of this was discovered through AppFollow's Semantic analysis.