Truly relevant similar products recommendations help both new and returning visitors by taking their attention, encouraging them to spend more time discovering products and helping them in making a choice.
Kea Labs analyses a meaning of textual information of items, such as description or product name.
Kea Labs evaluates importance of characteristics in each particular category, distinguishes their values and use them in comparison.
This is incredibly useful for complex products, such as Electronics.
Recommendations go even deeper and analyze product images. Kea Labs compares product shapes and color palette.
This is very efficient for Fashion, Apparel, Shoes or Furniture stores.
The most efficient way to increase revenue is to sale related products to customer. But most of recommendation engines do not provide enough relevance.
Naive algorithms recommend products only based on statistics and user actions (such as views or purchases). In contrast to them Kea Labs uses various techniques to improve the relevance:
Product matching algorithm combines analysis of users actions with the power of items comparison algorithm.
It groups products and detects relations even for non-popular or new items.
You may have often seen the cases when recommended products do not really fit each others.
We respect product characteristics and detect equipment and consumable products which fit each other.
With a statistical-based Recommendation Services it’s impossible to recommend related products while algorithm has not yet gathered sufficient amount of data.
We have predefined models for most of the general niches and adjust them for each store during the integration.
Later these models get automatically trained by the actions of every user.
Kea Labs automatically bundles products by discovering of regularities and stable connections between them. Bundles may also contain services, like installation or extra warranty.
With a help of our Dashboard or a simple data feed you can define custom bundles or adjust automatic ones.
Fashion designers rarely make stand-alone pieces and usually create collections of items.
Kea Labs uses multiple techniques to detect and recommend products from the same collections. Those algorithms also work in other e-commerce niches.
Kea Labs near in real-time analyses the whole path of users before and after search, and identifies insights to your management and merchandisers.
Search analytics allows you to evaluate search quality, measure impact of search to conversion, understand customer demand, and helps to guide your business decisions