Finding the Gold in User Reviews The data science team from App Tweak explains that while most user reviews lack actionable feedback, sifting through them can lead to finding "golden nuggets" of useful information. They discuss methods for filtering reviews by rating and keyword, as well as using machine learning models to find topics in sets of reviews.

Creating a Machine Learning Model for Reviews The data science team from App Tweak discusses their non-supervised machine learning model that can find topics in sets of reviews and trace them back to individual reviews. They explain how this model can be used to monitor the evolution of a topic through time and detect trends, as well as turn topics into actionable items.

Importance of User Reviews for App Visibility and Conversion The customer solutions team from App Tweak emphasizes the importance of user reviews for app visibility and conversion. They discuss how low average ratings can affect app visibility and how positive ratings can increase conversion rates. They also stress the importance of knowing what people are talking about in user reviews to make informed decisions about app development and marketing initiatives.

Engaging with Reviews Engaging with reviews is important, and replying to them can have a big impact. Automating replies using GPT can be helpful. Using positive reviews in screenshots can also drive engagement.

Leveraging Reviews for Marketing People trust reviews, so using them in marketing can be effective. Analyzing reviews can help identify common topics and issues. Using this information, campaigns can be targeted towards specific apps or topics.

Challenges and Solutions Some apps have fewer reviews, making it difficult to leverage big data. Semantic engines can help cluster apps based on what they are about. The model is store agnostic, so it can be used with different review platforms. The problem of chat GPT hallucinating can be solved by limiting the higher knowledge that the model has.