In Store Buyer Identification and Personalization via Mobile Phone Location

Openwave today announced their Contextual Merchandising Solution to Help Operators Monetize Off-Net Traffic. This system “enables the delivery of personalized content and advertising that is recommended to subscribers based on their user context and profile… [by] promot[ing] …complementary content and services based on a current usage patterns.” Every where around me I find companies getting better at helping me identify products that I would be most interested in. It began most obviously with Amazon, but has quickly evolved and many other online retailers take advantage of this capability such as Netflix (one of my personal favorites). However, I think the real opportunity is in taking the technology beyond the basic CRM data insights garnered from usage patterns and connecting it with insights from ones personal network and their “window shopping” patterns.
Let me start by explaining my thinking on personal network recommendations. In this context the system would be able to not only understand the individual’s buyer patterns, but also those of their friends. Through input from the user, the system would gain intelligence on which friend the individual user most aligns with on a given product category. The power of the personal network for purchase decisions is one that we should be able to bring to the mobile experience. An example would be someone shopping for a new digital camera and making note of their favorite one when out shopping, maybe even communicating they bought it. Then, when another friend is out shopping for a digital camera (or online) the system could let them know what ones their friends liked and purchased. Another example is picking out a hotel in a city you haven’t ever visited before. Wouldn’t it be nice to know if one of your friends stayed somewhere in that city and loved it?!
The second key input I think that would add valuable feedback to the user, would be to leverage their “window shopping” buying patterns. Imagine the insights that could be extracted from the foot path of a customer. How long do they pause in one area, where do they start, where do they abandon the store, what did they miss? To leverage this information you would use location based services to identify their foot path and then overlay it with a store map. You could also figure out where they went next (might be your competitor). With this information you could then redesign the layout of your store. Or, even more valuable to you and the customer, you could communicate directly to them when they’ve missed something you know they’d like as they walk out the door. Now, there are all kinds of considerations around privacy and the “creepiness” factor in this, but remember this, when American Express first launched computer telephony integration (CTI) with their interactive voice response (IVR) system to be able to answer the phone and say, “Hello Mr. Archer, how can we help you today?” people freaked! Today, caller ID is commonplace and expected (so frustrating when you enter your account info and the customer service rep asks for it again).
High end retailers (think “Nordstrom’s experience”) and big-box stores could dramatically increase in store sales if they leveraged such a capability. It could be a key differentiator…until everyone does it.

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