Freeform Data Are Not Exactly Free
Now, you may think that this table is "too" simple. If a marketer is concerned about the terms and conditions attached to these offers, then I'd recommend creating sub-categories under the main Offer Code. It is a good idea to keep the number of variations in one code to a manageable size, anyway. We may create an "Offer Condition Code" to capture details, such as:
- Minimum required purchase amount
- Period/season specific
- Coupons required
- Store cards only
- Specific products only
- Limited to 1 gift per customer for 3 months
- Students only
Now, the combination of these two codes will produce all kinds of variations. Going further with it, if capturing the seasonal element is critical, then another sub-category called "Offer Season" could be assigned to:
- Memorial Day
- Fourth of July
- Labor Day
- Black Friday
- White Monday
If you feel bad for the U.S. presidents or Columbus for leaving them out, then you may include those holidays, as well. But you get the point. The whole idea is to avoid freeform data as much as possible from the data collection stage and on. It is unbelievable how many so-called surveys result in unusable freeform data, and we should have a word with the survey designer in such cases.
In the world of analytics, categories and tags are your friends. Have you wondered how music services like iTunes or Pandora auto-magically (I apologize for using this cliché) pick related songs like a personal DJ for you? I am certain they all rely on wonderful algorithms that calculate the distances among millions of songs. But the starting point of such a calculation is setting up useful categories and tags for each song, such as musical genre, artists, artist category, main instrument type, year released, year composed, original/remake, band type, band members, lead singers, composer, arranger, conductor, length, album, album type, song sequence in an album, collaborating artists, featured artists, etc. Going deeper, one can imagine obscure tags such as "One-hit-wonder of the 80s," "Guitar heroes of the 70s," "Girl groups of K-pop," etc. I don't care if they wrote computer codes to create such tags, or had a farm of young and hip interns go crazy with it. The point is that building a mathematical model is a stepwise exercise, and categorization is an important part of it.
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.