A Recommendations engine is a powerful personalization tool that helps ease the product discovery process for your customers. The user engagement and conversion rate in a recommendation-based campaign is much higher than in generic campaigns. 69% of business buyers expect Amazon-like buying experiences and look for personalized recommendations.
Sherpa AI ‘Recommended items’ are the intelligent grouping of different items empowered with AI. These recommended items can be used to personalize your marketing campaigns.
- User item interactions / Events (e.g. Add to Cart, Purchase, Product viewed, etc.)
- Item Catalog
The recommendations are generated from the items in a catalog. You will need at least one type of user-item interaction event for recommendations to be developed. You can define as many events as required and can influence the algorithm.
Recommendation automates product discovery for your users. The Sherpa recommendation engine is exceptional in learning the behavior of users and recommending relevant items for each of them.
For example, let's take the example of a catalog having 10 items and N users. For some users, item5 will be more relevant, whereas, for others, item7 will be more relevant, and so on. The table below shows how recommendations might be served to various users.
No Code AI Platform
Developing a recommendation system from the scratch is a hassling experience. It requires a lot of time, effort, and expertise. With MoEngage’s Sherpa AI, you can set up a recommendation engine with no code and a minimal set of configurations in merely 24 hours!
You only need to define an item catalog and relevant events from the Sherpa user interface. You can also edit the recommendation settings and experiment with a different set of events. With your instructions, Sherpa handles all the data engineering and complex algorithmic tasks in the backend without further intervention.
Deep Learning Algorithm
The Sherpa AI leverages state-of-art deep learning algorithms to learn user behavior and provide the most relevant results. The algorithm accounts for users' behavior and session-based pattern shifts. The recommendations are generated even if no interactions are done by a user, also known as the ‘user cold starts problem’. Such users are recommended the popular items initially, and then, based on a few interactions by the user; personalized recommendations are shown for the user.
There could also be an ‘items cold starts problem', which happens when there are items that were not interacted with by users; for example, new items. The AI resolves this by promoting cold (or not interacted) items often, collecting feedback, and quickly making informed recommendations.
User intent can change quickly. Something a user was interested in yesterday may be irrelevant today. There is a need for recommendation systems that are personalized hotfoot with the rapid shifts in user behavior. The Sherpa AI can learn quickly from a user’s real-time interest and keeps the recommendations fresh and relevant. These recommendations are updated in a matter of hours.