Personalization
and Smart Recommendation
AI-driven personalized product recommendations
Planning on taking your online store to the next level with a recommendation engine tailored to your needs?
Not sure how to optimize your business for growth and increased revenue?
Trying to apply generic product recommendations for a dynamically changing product offer but finding it is not working?
Looking to enhance your catalogue with hundreds of relevant attributes with intelligent tagging?
Afraid of losing customers to competitors because your site can’t make smart suggestions?
If so, we’ve got a solution for you!
We’ve created a whole new way of doing things using an AI-driven personalization and recommendations tool that allows customers to find exactly what they’re looking for, whether browsing online or shopping via a mobile app. Our solution gives you hyper-relevant product recommendations from day one, and it continues to learn and improve with every click, search query, and purchase.
Display the right products today with smart personalization and recommendations.
How we could start
First stage
Second stage
We gather a sufficiently large sample of your data, including ‘clicks’ and ‘views’ of your products taken from your online shops, the history of your product’s offer, and your product catalogue structure.
Third stage
We will connect your online store directly to our recommendation engine to ensure a constant feed of orders and products that will enrich the machine learning models. Reinforcement learning algorithms further enable them to optimize each recommendation. We need both machine learning to find optimal recommendations and reinforcement learning to adapt to changes on the online environment and balance short- and long-term benefits. We will communicate the suggestions to your customers directly or through your IT environment.
Case Study
Dynamic Recommendation for E-commerce
Background & Problem
Our client is a leading fashion e-commerce platform in Poland. With more than 100,000 daily visitors, its website offers more than a million products, including clothes, footwear, accessories, and beauty. The company’s first challenge was providing recommendations for a dynamically changing list of currently offered products. Every once in a while, a different set of products is active for online advertising. An additional challenge was to include the products without a sales history, with only a short sales history, or a sales history from other platforms.
Solution
The project provided with our partner, I.T. Company O.C., aims to replace the existing recommendation engine with a state-of-the-art approach. We created a solution that engages reinforcement learning combined with machine learning (Contextual Bandit algorithms) to continuously adjust the list of the best products for the recommendation. To leverage the recommendation capability, algorithms also map different product attributes, including product descriptions, to provide CTR assessment in the case of an absent or limited sales history.
Results
Our approach has increased the conversation rate (measured by CTR) by 30%. The solution also enables the company to include products with no or insufficient sales history into recommendations