Freeform Data Are Not Exactly Free
7. Automate as Much as Possible: No matter how expensive computing time may be, it is just a fraction of the cost of human labor. Once the patterns and rules are set, employ all available technologies to automate the process. That will also ensure consistency, right or wrong. But do not forget that there is no software that can just create categories and groups that are suitable for your goals on its own.
Last month, I discussed how we can create hundreds of meaningful statistics out of simple RFM data by combining them with other categorical elements, such as products and channels (refer to "Beyond RFM Data," where the concept of RFM, P & C were introduced). And such categorical data are abundant. We talked about products, services, channels, offer types and business titles. But we may also dig into markets, regions, member status, payment types, data sources, Web pages, call-center logs and any type of action that may happen at websites or stores. Create a categorization template suitable for specific goals and lay out proper categorization rules. Then you will be able to make any analytical dataset immensely more colorful. After all, that is what I meant by "Beyond RFM Data." The trick is to combine different types of data at the variable creation stage in preparation for the analytical steps. But if you give up on the freeform data, none of it would be possible, even with a simple field, such as Professional Title.
So, the final lesson is that you should never give up, never surrender. Making sense of seemingly impossible amounts and variations of data is the essence of the Big Data movement, anyway. Blessed are the ones who are innovative, committed, persistent and consistent. You didn't think that this whole thing would be that easy, did you? (Silly rabbit, Trix are for kids … ) But, like the crossing of the Atlantic Ocean, any challenge can be turned into a routine if you get to know the proven steps. The sea of data should be looked at the same way.
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 firstname.lastname@example.org.