Freeform Data Are Not Exactly Free
3. Buyers, Not Product: As explained already, for marketing purposes, when in doubt, buyer categorization must be the primary goal. Buyer categories are definitely not the same as product categories. Product taxonomy designed for inventory and website management is better than nothing, but they aren't suitable for target marketing.
4. The More Specific, the Better: During categorization efforts, the most specific category in the master table should be considered first. Do not get lazy and just assign an item to "Home Electronics," when it could be under "Home Electronics > Home Theater System > Audio Equipment > Speakers."
5. Cut Out the Noise: Even in short product descriptions, there are many noises. For instance, is it really necessary to categorize every color of shoes a woman bought? Great, you now know that she bought a pair of red shoes in the spring of 2014. Types of shoes, designer, brand and the price range are important, but the "red" part? You don't need it, unless you will have a "red color only" sale one day. And even so, she may never respond to it, as she has a pair of red shoes already. No matter how interesting the category or tag may sound, cut it out if it doesn't help sell more products.
6. Consistency Over Accuracy: Do not forget that we will be using these categorized data in model development, and an important part of that exercise is recognizing patterns in consumer behavior. If you keep changing your mind about the category for the same item, it will mess it all up, for sure. People are often confused because of the English version of "Category Descriptions" (as in "That handbag should not be in women's accessory, as it is a fashion brand!"). But once it is categorized, it is just a set of assigned numbers that provide patterns for the analytical programs in later stages. The worst thing to do is to put in conflicting categories or tags for the same item from different transactions. Also, if the brand or merchant name are so important, those should be separate variables, like in the earlier examples for professional titles and offer types.
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.