The complexity of products and services in B2B requires assistant guidance in their correct assembly. Therefore, recommendation engines need to suggest items based on their compatibility. Your B2B ecommerce platform should provide cross-category recommendations that curate relevant product bundles and suggest complementary parts and accessories without the need for manual search. The so-called “wisdom of crowds” data shall be employed to surface similar products when the searched items are out of stock.
Suppose, however, that two similar products fulfill a customer’s need to an equal degree, but one of them is priced higher. In that case, recommending the most profitable item makes perfect sense. Therefore, by suggesting the most relevant and compatible products, sellers don’t necessarily have to forego increasing revenue – they, in fact, can do both.
Another important component of a recommendation system in B2B is based on previous browsing and order history. Memory-based collaborative filtering builds correlations of products based on the client’s historical records and then adopts such correlations to predict future interests. Thanks to the recurrent nature of business operations and contractual obligations, it’s relatively easy to predict future orders. By carefully analyzing data of previous purchases (which are mostly driven by customers’ sales forecasts or production plans), you can predict your customers’ demand, sometimes even more efficiently than the customers themselves.
If there’s not enough data for a particular customer, recommendation systems usually base their predictions on the data for customers with similar purchase history (or equivalent industry profile). There are various types of calculation formulas in recommendation engines that determine the degree of customer similarity. One of them is based on creating the purchase vectors that establish corresponding coordinates between customers (such as the purchase of similar products, the total number of purchased items, the average amount spent on shopping, and so forth).
Completed auto-reorder forms or auto-recommended frequently purchased items, at the re-buy interval right for each business unit, is essential for good customer experience and retention.
Although out of scope of what is typically referred to as recommendations, the B2B ecommerce platform needs to respect account-specific catalogs, contractual price commitments, user specifications, and compliance norms. In short, business customers, upon logging into their accounts, shall see the prices and products contractually agreed on with the seller (something we have discussed at length in the previous article on personalization).