• Similar products

    In this Block of Recommendations we offer Customers Similar Items with a higher price by using the up-selling technique.

    How do we select the items? We look at:

    • product’s name and description;
    • all the parameters of the product (e.g. display diagonal);
    • price of the product;
    • colors represented on the photos.
  • Related Products

    Cameras and SD-cards, fishing rods and fishing lines – if items complement each other, they will be shown as Related Products. The cross-sell mechanism raises your income by stimulating Customers to buy more.
  • Также алгоритм анализирует фотографии и учитывает цвет товаров, что очень важно для магазинов одежды, дизайна или декора.
  • Items from one Collection

    These Recommendations are relevant for Furniture, Clothes and Accessories where style and marks of a certain Collection really matter.
    Besides, Kea Labs can promote a particular brand or a group of products.
  • Recently Viewed Items

    Customers can easily find the Products they have viewed due to Reminder Block.
  • Personalisation

    Preferences of each of your Clients can be defined by the algorithm which is based on Customer Behavior Analysis. It allows us to make high-quality Personalized Recommendations even for web-sites with a minimal traffic.
  • Popular in the Category

    Every Category of your Store has its hits. Kea Labs finds the most popular products and offers them to Customers.

Recommendations also support:

  • Discounts and Promotions

    Customers will see the prices of the Recommended Products together with the Special offers of your Store. Kea Labs Recommendations support Store’s Promotions by raising the priority for the promoted products.
  • Trends

    Customer interest in certain products often depends on the season. Kea Labs considers these changes and edit the Recommendations according to them.
    For instance, in winter it shows the following Items:

Results

As data is growing, Kea Labs team constantly tunes algorithms for each Store individually.
This is an average Statistics of Kea Labs. You will receive such form after a month of work with Kea Labs.:

What is the difference between Kea Labs Advanced Recommendations

and other Recommendation engines?

  • 1. Control of the Algorithms

    Systems that works with no human participation, often make mistakes in Recommendations, for example, they can recommend to buy scissors with a cup just because some two customers did so.
    Kea Labs Analysts Team sets up the categories individually for each Store and minimize such cases practically to zero.

  • 2. High effectiveness

    Due to initial setup customization, Kea Labs Advanced Recommendations start making a profit earlier than regular ones, which at first need some time to collect statistics.

  • 3. Extended Algorithms

    creates connections between all similar goods, not only those viewed together.
    That is also the reason why the New Items quickly appear in Recommendations, that does not normally happen to regular systems.

  • Startup
  • All essential algorithms
  • Responsive design
  • Theme customization
  • Automatic monitoring and tuning
  • Deep analysis of attributes and properties
  • Complex products with SKU’s
  • up to 20 000 visitors monthly
  • 149 EUR/month

  • Register
  • Big Store
  • Margin analysis
  • Automatic trends and demand analysis
  • Recommendation of components
  • Expert support and guidance
  • API
  • up to 80 000 visitors monthly
  • 699 EUR/month

  • Register
  • Growing
  • All essential algorithms
  • Responsive design
  • Theme customization
  • Automatic monitoring and tuning
  • Deep analysis of attributes and properties
  • Complex products with SKU’s
  • up to 20 000 visitors monthly
  • 125 EUR/month

  • Register
  • Big Store
  • Margin analysis
  • Automatic trends and demand analysis
  • Recommendation of components
  • Expert support and guidance
  • API
  • up to 80 000 visitors monthly
  • 625 EUR/month

  • Register

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