Where Is the Data Movement Going?
The first group of people who applied statistical techniques to target marketing were equipped with clunky mainframes that could only read punch cards. That was around the time when Moon Shot was in full-force, so one may think I am talking about ancient history in the age of Big Data. But those brave souls – mostly in the publishing industry — paved the mathematical way for modern-day data scientists.
With computers that were a few million times slower and equally more expensive, they literally had one chance to get the statistical work right for Christmas campaigns. And even in those days, Christmas came around once a year, and even with such antiquated computers, targeting based on statistical modeling actually worked. I would compared such an endeavor to calculating the reentry trajectory of a space ship by hand. Lest we forget, tools may have changed, but not the mathematics.
Fast-forward to Present Day, and we now accumulate more data in a day than our ancestors ever did since the invention of paper. We have means to collect and store data during about every glance you take, and every move you make. We now have data mining tools that practically build models with a few clicks and, soon enough, we will be able to set analytical parameters by simply stating the business goals to a computer. Statisticians? Data scientists? Someday in the near future, a machine will replace most of their functions.
Then, why is that most of us are not impressed with the majority of marketing messages? How is it that key performance metrics of marketing efforts did not improve at the rate of increase in computing speed and capacity? Granted that we the consumers are constantly being bombarded with too many mindless sales pitches, but how is it that a typical response rate (not clickthrough or page views, but actual conversion rate) of a typical 1-to-1 campaign still hovers under 1 percent, and at times, way below that mark? Doesn’t that mean that the marketing efforts are failing more than 99 percent of the time? Pioneers of data-mining techniques did better than that with computers barely faster than using an abacus.
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.