eCommerce Analytics

For a long while, it seems as though email marketers have followed the idea of nothing ventured, nothing lost when it comes to email marketing. There was simply no reason to get better at email marketing because it didn’t cost all that much more to send out 1000 more emails. So what did it matter if the clickthrough rate could be optimized? It was easier just to buy 1000 more names and blast them all. Extra names = increase. Right? Well, now that every retailer out there is sending more emails, it’s making the forest that much thicker - and the path to your product that much more difficult to navigate. Couple that with the fact that a poor economy is just about the time that acquisition seems too pricey and we have a perfect climate for belt-tightening via smarter use of technology. Behold, the age of product recommendations has come to email.

Read Retail Email Marketing Embraces Product Recommendations »

I just got finished perusing ReadWriteWeb’s series on recommendation engines since, as a recommendation engine company, we have a keen interest in the subject. One thing that is abundantly clear is that recommendation engines still reside in the realm of technology, as opposed to business. Despite all of the success of Amazon and Netflix in using their recommendation engines to drive revenue and customer satisfaction (both companies are in the top 40 in customer satisfaction), the idea of recommendation engines still hasn’t quite caught on. One of the reasons is that old channels die hard and recommendation engines are still perceived as a purely ecommerce play. But they don’t have to be. And following these four suggestions will make recommendation engines more palatable to multi-channel retailers who need to take more time migrating online.

Read 4 Recommendations for Recommendation Engines »

I somehow missed Brian K. Walker’s excellent end of year post Ten Themes for 2009: eCommerce Technology. It’s a good read and, given our focus, this prediction jumped out at me.

Predictive merchandising becomes ubiquitous, and the crowd begins to separate. “Predictive merchandising” is also referred to as “automated merchandising” or “personalized product recommendations”. Whatever term you like (or are marketing) we will see this area are the “product reviews of 2007”, where we go from stepped up interest and demand to a default feature. The incumbent concerns and cultural hesitations of merchants and marketers will be replaced with an enthusiasm for the improved customer experience and ROI.

I disagree wholeheartedly.

Read Forrester: Predictive Merchandising to Become Ubiquitous in 2009 (They’re Wrong) »

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 »