Ah, the chagrin of a one-hit-wonder begins.
Use of statistical models, with help of multiple variables and scalable scoring, would avoid all of those issues. You want to expand the prospect universe? No trouble. Just dial down the scores on the scale a little further. We can even measure the risk of reaching into the lower-scoring groups. And you don't have to worry about coverage issues related to a few variables, as those won't be the only ones in the model. Want to automate the selection process? No problem there, as using a score, which is a summary of key predictors, is far simpler than having to carry a long list of data variables into any automated system.
Now, that leads to the next point, "Filling in the gaps and summarizing the complex data into an easy-to-use format." In the age of ubiquitous and "Big" data, this is the single-most important point, way beyond the previous examples for traditional 1-to-1 marketing applications. We are definitely going through massive data overloads everywhere, and someone better refine the data and provide some usable answers.
As I mentioned earlier, we build models because we will never know the whole truth. I believe that the Big Data movement should be all about:
1. Filtering the noise from valuable information; and
2. Filling the gaps.
"Gaps," you say? Believe me, there are plenty of gaps in any dataset, big or small.
When information continues to get piled on, the resultant database may look big. And they are physically large. But in marketing, as I repeatedly emphasized in my previous columns, the data must be realigned to "buyer-centric" formats, with every data point describing each individual, as marketing is all about people.
Sure, you may have tons of mobile phone-related data. In fact, it could be quite huge in size. But let me turn that upside down for you (more like sideways-up, in practice). Now, try to describe everyone in your footprint in terms of certain activities. Say, "every smart phone owner who used more than 80 percent of his or her monthly data allowance on the average for the past 12 months, regardless of the carrier." Hey, don't blame me for asking these questions just because it's inconvenient for data handlers to answer them. Some marketers would certainly benefit from information like that, and no one cares about just bits and pieces of data, other than for some interesting tidbits at a party.
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.