Machine Learning Based Product Design: The Case of Mobile Apps

Firms strive to continuously improve their products in order to compete effectively in the market. Typically, firms update their products by adding novel (differentiating) features or by imitating their competitors. With the advent of social media, there is also the possibility of obtaining customer input on their desired features. Customers post reviews, which include suggestions for improvement. In the case of mobile apps, user feedback may include suggestions of novel features or features that are already present in competing apps. Leveraging the information on reviews and version release notes of iOS apps, we build a novel deep learning algorithm based on transfer learning and named entity recognition techniques to identify four types of app features - developer initiated innovative, developer initiated imitative, user suggested innovative and user suggested imitative. Further, we evaluate the impact of these feature categories on the demand. Our results suggest that only developer initiated innovative and user suggested imitative features help increase the demand. We also find that the impact of user suggested innovative features is negative. However, this negative effect is limited to features that are contextually ”distant” from user suggestions. Contextually ”close” innovative features suggested by users do have a positive effect on the demand. To the best of our knowledge, this is the first study to consider reviews as a source of user ideas and evaluate the performance impact of the ideas mentioned in the reviews. Our study also provide a novel deep learning model to extract suggested features from user generated content.