Recommend the right products to the right people – with the help of data.
Instantly match and show products your customers are most likely to buy on different channels and touchpoints, ensuring high conversions and great UX!
What is AI powered recommender and how does it work?
Unlock the power of recommendation
What our clients report on average
Average sales conversion increase
Average CTR increase
Average purchase value increase
Regardless of your challenge, we can offer a suitable model for optimal recommendations to your customer
Our best-seller recommender engine helps you recommend top-selling products based on specific criteria (most frequently bought in a particular category and given time period), which makes it convenient for homepage carousel, leaflets, and search banners,...
This tool shows great conversion rate improvement within businesses that have many comparable products within the same category.
Higher conversion rates, increased revenues, and customer retention are just a few of the benefits of a trending product recommender.
For the store or e-commerce owner, the trending recommender engine also provides an insightful look into products that are starting to lose popularity, how newly added products are performing, and valuable information on revenue.
Recommending products most recently included in the assortment based on specific criteria (in a specific category and given period) can help you significantly speed up the sales process for the most recently included items.
Besides sales, you can also send new product recommendations or run a survey to understand your audience's needs better and then pitch a product through dedicated campaigns over Email or Viber.
Upon learning what the client is searching for in the current session, the system can then recognize and recommend similar products.
For example, when searching for black sports shoes, the system can display other black sports shoes available.
This way you keep your visitor focused on your offering and make him not leave for the competitor's website.
Same as above, related products are based on a real-time search the client does, but the system displays related products instead of similar ones.
Following the example above, when searching for black sports shoes, clients will be presented with items related to them, such as socks, shorts, etc.
Related products are a useful way to upsell and cross-sell to a customer with limited data available.
By looking at historic purchases, the system can „determine“ and suggest items that the client is likely to be going out of stock, such as printer ink or similar products that the client regularly buys from you.
This way you can show that you think upfront for your client and offer him a fully personalized product that he will really need in the short term.
Community-based recommender actually recommends products based on similarities between customers and products they have interacted with.
This recommender is considered an important part of cross-selling marketing strategies, especially for web-based B2C businesses.
Basket-based next gen recommender
Recommending products based on long-term patterns and preferences combined given the real-time occasion/intention the user is having at the touchpoint, which makes it convenient for real-time recommendations and e-commerce platforms.
Description-based recommender engine recommends products based on similarities extracted from their features (description, category, name,...) and help businesses offer products with the best product-feature customer fit.
Companies find this recommender convenient for improving the "add-to-cart”, search banners, and POS recommendations conversion rates in both, offline and online customers' journeys.
They are putting lights on the most hidden places giving you whole new perspective on business and making your vision much more clear.“– Srdjan Grabovac, Business Development Manager, Planeta Sport
– Aleksandar Popović, Direktor razvoja, Super Kartica