Personalization & Recommendations: How AI is Changing User Experience in Apps

AI-driven user personalization and recommendation in apps is the ability to provide custom content, products or services to users based off of their prior data points or usage history.

The use of AI in personalization and recommendation in applications can lead to marked improvements across user engagement, acquisition, and retention. AI allows businesses to harness data around user behavior, engagement, preferences, and interests to create a truly personalized experience that encourages ongoing usage and drives conversions.

From a practical perspective, there are four main use-cases for AI-driven personalization and recommendation in apps:

1. Automated Content/Product/Service Recommendations: App owners can use AI-driven algorithms to personalize automated recommendations to users based on their usage history. This could be personalized product or service recommendations that are tailored to each user’s personal interests or content that is delivered at the right time in order to keep users engaged.
2. Search Query Optimization: AI can be used to optimize the search engine used within an app. This can include suggesting related topics to a user’s search, recommending products based on searches or usage patterns, and providing a more intuitive and helpful search experience overall. AI can also be used to not only suggest products that match a query but also to relevance rank results.
3. Targeted Ads: App owners can leverage AI to provide personalized ads to users based on their past actions and preferences. For example, a user who has recently purchased clothing items could be presented with relevant ads for shoes or accessories. This hyper-targeted approach ensures that ads are highly relevant and more likely to convert.
4. A/B Testing & Feature Optimization: A/B testing is a classic strategy for optimizing features within an app, but AI can be used to automate much of the process. Algorithms can be used to identify users with similar engagement patterns and then test different features or iterations of different features to see which performs best. This allows businesses to quickly identify the most effective tactics for optimization, ensuring that their app is continually performing better over time.

Overall, AI-driven personalization and recommendation can be an incredibly powerful tool for businesses looking to increase user engagement, acquisition, and retention in their apps. By leveraging AI to provide more personalized experiences, it is possible to not only drive conversions but also create deeper human connections with users.