eCommerce Analytics

A couple of weeks back, I gave you a set of three hyperlinks that describe efforts to predict the next product that each of your customers will buy. Many companies that initially sprung up around this field have taken Amazon or Netflix and their collaborative filtering approach as the method for predicting what customers will like next. More strikingly, recommendation engine companies have patterned their offerings after Amazon and Netflix as well. That is, the output pieces are built around website infrastructure, meaning that the recommendations live online and are tailored specifically for customer visits. Alas, there are more than a few businesses that still make a majority of their money offline and need a solution that fits the non-electronic areas of their business. What about the merchandising arm that just wants to know how to determine the quantity of each product to buy from the manufacturer or wholesaler for the coming quarter?

Read Using Next Best Product Recommendations to Determine Inventory Levels »

The core of Istobe’s technology is an engine that predicts the next product or product category that a customer wants, a la Amazon or Netflix. However, we try to make this technology more applicable to other processes within the retail realm, such as direct marketing and merchandising, by looking at actual sales data. The typical recommendation engine (like richrelevance) only lends itself to realities in the online world, since they take advantage of website data exclusively. The Istobe engine is a bit more universal, using actual sales transactions to build models and predictions. In either case, the goal of either approach is to predict the next best product for your customers. I’ve compiled a good starter kit containing three links for retailers and others who may be interested in learning about Next Best Product approaches.

Read Predicting the Next Best Product for Your Customers: A Hyperlinkset »