Chicken or the Egg? Data or Analytics?
- Target universe is too small: Depending on the industry, the prospect universe and customer base are sometimes very small in size, so one may decide to engage everyone in the target group. But do you know what to offer to each of your prospects? Customized offers should be based on some serious analytics.
- Predictive data not available: This may have been true years back, but not in this day and age. Either there is a major failure in data collection, or collected data are too unstructured to yield any meaningful answers. Aren't we living in the age of Big Data? Surely we should all dig deeper.
- 1-to-1 marketing channels not in plan: As I repeatedly said in my previous columns, "every" channel is, or soon will be, a 1-to-1 channel. Every audience is secretly screaming, "Entertain us!" And customized customer engagement efforts should be based on modeling, segmentation and profiling.
- Budget doesn't allow modeling: If the budget is too tight, a marketer may opt in for some software solution instead of hiring a team of statisticians. Remember that cookie-cutter models out of software packages are still better than someone's intuitive selection rules (i.e., someone's "gut" feeling).
- The whole modeling process is just too painful: Hmm, I hear you. The whole process could be long and difficult. Now, why do you think it is so painful?
Like a good doctor, a consultant should be able to identify root causes based on pain points. So let's hear some complaints:
- It is not easy to find "best" customers for targeting
- Modelers are fixing data all the time
- Models end up relying on a few popular variables, anyway
- Analysts are asking for more data all the time
- It takes too long to develop and implement models
- There are serious inconsistencies when models are applied to the database
- Results are disappointing
- Etc., etc...
I often get called in when model-based marketing efforts yield disappointing results. More often than not, the opening statement in such meetings is that "The model did not work." Really? What is interesting is that in more than nine times out of 10 cases like that, the models are the only elements that seem to have been done properly. Everything else—from pre-modeling steps, such as data hygiene, conversion, categorization, and summarization; to post-modeling steps, such as score application and validation—often turns out to be the root cause of all the troubles, resulting in pain points listed here.
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.