Complimentary products meet Customers needs

AI engine analyzes customer behavior to recommend complementary products to drive sales


Cross-sell and Up-sell drive revenues. A customer bought an iPhone. It’s likely that he/she will be willing to buy an iPhone case or other iPhone accessories. Can eCommerce companies compare the behavior pattern of a customer to those of the other customers in real-time and recommend other products that the customer may likely to buy?


Today there are machine learning models such as collaborative filters, content-based filters, or a combination of them that can help Identify those products that sell in conjunction with each other, as well as links between customer purchases over time.


Customer data can be explicit and implicit. Explicit data is provided intentionally by the customer, while implicit data is not intentionally but collected through various data streams such as search history, clicks, order history, etc.

After collecting and storing the data, various filtering algorithms are used to extract the relevant information required to make the final recommendations. The various filtering algorithms that are used in building a recommendation engine are content-based filtering, collaborative filtering, or multi-criteria filtering that is combinations of other filtering algorithms.


  • Use results for cross-product selling, forming selling strategies, and overall managing of product merchandising.
  • Identify which products are purchased in addition to promoted products. Identify products that drive the purchase of primary items.

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