New Generation AI Algorithms

At Gravity Field, our recommendation algorithms are powered by a combination of static product feed data and real-time user interactions.By analyzing behavioral patterns among millions of users, our self-training algorithms build and develop sophisticated formulas that consider various parameters including product views, add-to-cart actions, purchases, and many more. The algorithm assigns weights to each of these interactions in understanding individual customer preferences. Leveraging this comprehensive data the algorithm predicts the most compelling series of products to be offered to each customer.

Our Gravity Field recommendation engine utilizes the most advanced self-training algorithms based on machine learning models, such as deep learning. Unlike traditional “old school” recommendation algorithms that rely on a static formula designed in advance, the Gravity engine takes a dynamic and real-time approach. Through continuous learning and optimization, our recommendation engine analyses your customers’ interactions in real-time, adapting and building a unique “formula” that is tailored made for your specific App or Web platform.

Machine Learning models such as Deep Learning excel at considering hundreds of variables and factors to provide personalized product recommendations based on real users’ interactions. Unlike traditional approaches that rely solely on static feed data or obvious product correlations. For instance, a particular user might be recommended to purchase red trousers with a blue shirt, even though those products might not seem a match at first glance. This recommendation is based on the behavior patterns of other users with similar preferences and purchase histories.

By utilizing the collective knowledge of the user base, the Deep Learning model can provide novel and relevant product combinations that align with individual tastes. These algorithms make your product recommendations remarkably more intelligent and give them a natural “human” feel - just like your customer is talking to a live and experienced consultant in a store. This is why giants like Netflix or Spotify widely use such algorithms.

Beyond looking at the Past (i.e., personalization based on historical data) and the Present (i.e., real-time experience optimization), Gravity Field looks into the Future of each campaign’s run and predicts its performance for each of your specific audiences.

This is done by the Gravity Predictive Targeting engine that goes beyond traditional targeting methods by not only identifying the best matches between the running experiences and your audiences, but also predicting the impact of scaling those matches.

You have the flexibility to test your own recommendation algorithms and combine them with the power of other Gravity Field features, such as microsegmentation, AB-testing, and AI optimization. You can seamlessly integrate your algorithms into our platform via API.

You can conduct extensive testing and comparisons of your in-house algorithms versus Gravity algorithms, and analyze results, to identify the most successful combinations for scale-up.

Take full control of your product recommendations by adding your custom business logic to the recommendation algorithms to make your recommendation experiences more individualized, and sophisticated, and give them a “human-like” feel, to  earn better margins. Configure your merchandising rules for the placing of recommendations or specific product slots in them, manage product bundles, and sell product placements to your vendors or suppliers.