Fast-forward to the 21st Century. There is still a beauty of knowing who the potential buyers are before we start engaging anyone. As I wrote in my previous columns, analytics should answer:
1. To whom you should be talking; and
2. What you should offer once you've decided to engage someone.
At least the first part will be taken care of by knowing who is more likely to respond to you.
But in the days when the cost of contacting a person through various channels is dropping rapidly, deciding to whom to talk can't be the only reason for all this statistical work. Of course not. There are plenty more reasons why being a statistician (or a data scientist, nowadays) is one of the best career choices in this century.
Here is a quick list of benefits of employing statistical models in marketing. Basically, models are constructed to:
- Reduce cost by contacting prospects more wisely
- Increase targeting accuracy
- Maintain consistent results
- Reveal hidden patterns in data
- Automate marketing procedures by being more repeatable
- Expand the prospect universe while minimizing the risk
- Fill in the gaps and summarize complex data into an easy-to-use format—A must in the age of Big Data
- Stay relevant to your customers and prospects
We talked enough about the first point, so let's jump to the second one. It is hard to argue about the "targeting accuracy" part, though there still are plenty of non-believers in this day and age. Why are statistical models more accurate than someone's gut feeling or sheer guesswork? Let's just say that in my years of dealing with lots of smart people, I have not met anyone who can think about more than two to three variables at the same time, not to mention potential interactions among them. Maybe some are very experienced in using RFM and demographic data. Maybe they have been reasonably successful with choices of variables handed down to them by their predecessors. But can they really go head-to-head against carefully constructed statistical models?
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.