Harish Srigiriraju is an alumnus of Northwestern University with an MBA from the prestigious Kellogg School of Management. He now plays a critical role in the development and enhancement of digital products within a leading telecommunications company. Harish, a product management expert specializing in artificial intelligence and advanced analytics, has developed many innovative solutions to personalize the user experience.
Companies can no longer develop a product that suits all users and expect to retain them. Harish discusses how businesses can leverage analytics and artificial intelligence to personalize user experience. Companies must first gather information about user needs and preferences. With this information, companies can then develop user segments and customize the product for each of these segments. This process can be performed through a rule-based algorithm that is manual in nature or with automated artificial intelligence. Harish recommends initiating a simple rule-based approach and then eventually adopting AI models.
Network analysis is widely used in various industries to derive insights. A set of entities or objects and the relationship between them can be represented as a network. Harish has implemented a unique approach of applying network analysis to personalize user experience in software products. Using network analysis and tools such as Gephi, he was able to create a visual representation of how far users have traveled through the product. Through metrics such as correlation coefficients and average distance between nodes, he measured the ease of navigation in an application. Finally, by analyzing feature usage and the navigation path using network analysis, he recommended changes to make features more discoverable.
Harish also used in-app ratings and artificial intelligence models to customize the user interface throughout the app. App features can be ranked based on In-App ratings to increase user engagement. Additionally, In-App ratings can be a very powerful leading indicator of churn and can be used to develop user retention campaigns. Disgruntled users can be identified prior to deactivation based on their in-app rating. Discounts, an extended trial period, or other offers may then be offered to retain disgruntled users. More importantly, in-app ratings do not violate user privacy, as no personal information is needed to develop such personalization models.
Harish’s work on personalization has benefited millions of users across the United States through the products he manages. Its innovations have generated considerable interest in the product and analytics community. He has been invited to speak at various world renowned conferences. In these forums, Harish encourages companies to take proactive steps to adopt artificial intelligence for personalization, a move that will dramatically increase user satisfaction and revenue.
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