Increase your e-store’s revenue with personalized product recommendations
As shown in the diagram above, our solution first collects and analyzes a lot of data about users’ behavior in your shop — what products they look at, what products they add to their carts, what products they order, and so on.
Our solution uses this collected data to train the Deep Neural Network built by our in-house data science team. This network analyzes what users do during a web session or visit, as well as the product catalog data, to provide very relevant and precise recommendations to users.
The solution also collects such information as which products are selling the best, which products are currently popular and which products are underexposed, all while capturing feedback. Over time, the solution automatically combines all of this information so as to display individualized product recommendations that are optimized to maximize the user’s click-through rate. As the solution is constantly learning, the technology automatically adapts when products are added or removed, or when product descriptions are updated, or when user behavior changes over time, etc.
From the merchant’s point of view, the no-code user interface is simple, composed of drag & drop elements that allow you to create the right types of product recommendations for various pages of e-store. Plus you can create different strategies and rules to govern your recommendations — for example, you can have the solution push certain products during the holiday season. Finally, the solution helps you track how successful your strategy has been so you can refine your approach (by using A/B tests, for example) and make informed decisions about your inventory.
I hope you will take a look at this session and watch the live demo that will show you just how easy this solution is to use and how many benefits it can bring to customers and merchants alike. See you there!