By Venkat Rajan, Vice president, analytic services, Symphony Teleca Corp.
You're at a home improvement store when you get an offer for a patio furniture set on your cell phone. You think to yourself, "Interesting. This is exactly what I need! I should take a look and see if it's what I've been looking for."
How was an offer presented to you that seemed to practically read your mind? If you study the scenario shown in Figure 1 (below), you will see that no single activity indicates a need for a patio set. These activities by themselves don't provide enough insight to what kind of offer you should get, but there it is. How did it happen?
Figure 1: Patio furniture real-time promotion
Today, we have all the technology components and most of the data to make this happen. First, the home improvement retailer has data about your prior shopping activities. Through analysis of this structured, internally available, transaction data, the retailer can categorize you into the right behavior segment by evaluating characteristics such as basket size, frequency, price sensitivity, and style. It is then a matter of targeting and deciding what kind of offer to make. The retailer can stop at that point and make an offer that fits your behavior and how it wants to influence you. For example, if you are a frequent shopper who buys products in certain categories, the retailer may want you to buy more or buy a product in a related category.
That is not enough. You may not be in the market for buying products in those other categories. If the retailer can integrate unstructured data from Web logs, Facebook, Twitter, and other sources and find the relevant insight into your current behavior, the targeting can be more, well, "on target."
Additionally, by integrating location-based real-time data, the retailer can make that relevant offer to you now, while you have an expressed intent to buy, because you're currently in the home improvement store.
