One of the important strategies in B2C and B2B e-commerce initiatives alike is the desire to increase AOV (average order value) as part of digital marketing tactics. Organizations seek to adopt buyer experiences similar to those found on Amazon where previous transactions and purchase history as well as catalog data is constantly analyzed to predict customer preference and product similarities. This allows customer experiences to be enriched by dynamic product recommendations throughout the purchasing funnel with the result of increased relevancy and conversion.
Long gone are the days where customers are willing to explore deeply nested navigation hierarchies to arrive at their product of choice. Instead, highly optimized search experiences allowing customers to filter and facet the result set quickly to arrive at their product assortment of choice has been the poster-child for years.
Nonetheless, search increasingly is required to be “smarter” and the shift to intelligent commerce has brought about the desire to anticipate, predict and understand what customers are looking for. In particular for B2B environments, this is extremely important since B2B buyer journeys are nothing like the shopping experiences B2C customers desire. Instead, B2B buyers are typically purchasing products they know. This means even more so that immediate findability is of paramount concern. But it doesn’t stop there. B2B buying is all about replenishing stock levels and local inventories. Therefore, systems which are able to recommend those products which are most often purchased in the past, or which are compatible with products typically replenished, are able to provide relevant B2B purchasing experiences. This is because these systems can greatly reduce the amount of time spent locating and purchasing these items. Ultimately this leads to increased repurchasing behavior, hence boosting AOV and CLV (customer lifetime value).
To recommend items your customers want, organizations don’t need to turn to excessively expensive solutions and can instead benefit from powerful AI and ML powered data analytics services available today. There are three major flavors when predicting which items are more likely to be of interest to your customers. Recommendation engines can make use of customer data such as past purchases, click-stream activity, etc. to drive product recommendations which fall into the following categories:
1. Frequently bought together:
These types of recommendations can be helpful, for instance, on the product detail page or on the cart and checkout pages to promote products, which are likely to be purchased together in the same order.
2. Item to Item recommendations:
Use these types of recommendations to promote dynamically (without seller involvement) which items fit together. The classical term for this is “up-sells”. However, managing up-sells manually on a product-by-product basis has been a painful seller activity of the past. Modern data analysis can now increase product catalog discoverability from click patterns and attribute comparisons.
3. Personalized product recommendations:
This, of course, is the classical and most typical use case of analyzing a customer’s past order history in an effort to predict their unique preference and choice.