It's been a while since my last post so lets get some simple things out the way.
Recommendation engines are increasingly becoming big news in the web arena. I've commented about this before but I'll repeat a few things if you missed them.
There is no sure fire single strategy to creating the best recommending engine, different products/content/activities in different markets are simply sold in different ways. However, in the mass retail market there is definetly huge value that can be added by adding those extra products to the basket and a lot of this can be done through automation.
So next time a software vendor tries to sell you an "engine" ask yourself whether it can be configured to adapt to the following 4 areas that you need to build a good recommendation engine:-
Business Rules: product placement through business strategy and good decision management
Product affinities: products purchase or seen with other product, similar price points etc.
Customer Segmentation 1: what do customers who look like X generally want?
Customer Segmentation 2: what do customers who look like X want right now based on all the recent behavioural information we have?
There are others as previously mentioned, but I think you need to be considering how these areas are catered for, analysed and optimised to ensure you get your greatest ROI for your software purchase.
Carry on recommending!
