Freeform Data Are Not Exactly Free
First, we created a multi-level product category table into which all SKU data would be assigned. It was multi-level, as we set up 20 to 30 major categories (from apparel to video entertainment), and broke each major category down to more specific categories. For instance, Apparel would be broken in to women, men, children, large size, petite size, big and tall, etc. And then women's apparel would be further broken down into fashion, formalwear, eveningwear, casualwear, underwear, loungewear, footwear, swimwear, bride wear, etc., for example. I now see that Google's product categories took that type of multi-level structure, and if you visit any major shopping sites, such as Amazon, and drill down their product categories, you will recognize such layers there, as well. The major difference? We did not categorize products, but we categorized buyers who bought those items.
And that is the punch line. Buyers, not the product. Why? It was because our goal was to predict individuals' future behaviors. We were not doing this for inventory management or website efficiency. So, when in doubt, it was perfectly OK for us to assign different categories to the same product, depending on the context. An easy example of that would be baking soda. Buyers of baking soda could be buying it for baking, deodorizing, dental hygiene, household cleaning and the list goes on. And we must recognize such differences. Similarly, let's take an example of a fancy weather station that tells time, temperature, atmospheric pressure, humidity, etc. One can buy that item from a nautical catalog or website, or from an executive gift catalog. If you force that item into a nautical category regardless of the context, you may end up sending nautical product offers to a gift buyer in the future. Not the end of the world, but not ideal, for sure. Again, buyers before products.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at email@example.com.