Bigger Is Better: How to Scale Up Customer Acquisition Smarter
Multivariate List Selection
This is list rental as well, but instead of working off a single common attribute, a data broker takes your set of multiple “selects” and produces a “count” of the number of matches. Examples can include age (usually banded, not to the year) and other demographic attributes like income, presence of children, and interests. It is relatively painless to create a target that sounds a lot like your target customer, if you work with reputable data vendors.
Combining multiple data points zeroes in on your target, and is usually expected to improve response. Since each select or targeting criteria adds cost to the list rental, your conversion requirements must go up to achieve your economic goals for the program. The same can be said for programmatic display buying. You pick the criteria for the target you want to advertise to, at a cost.
Most “look alike” marketing methods are little more than a multivariate selection.
The question it will help to answer beforehand, if you can, is are you choosing the right variables or criteria? If it sounds right — it may be, but that’s not a guarantee. Because data vendors don’t have a stake in the outcome and are typically responsible only for providing the data you ask for, list rental of this nature is often maligned for poor performance and exorbitant cost. This may not always be the case however — in many cases in my experience, the marketer didn’t define the target particularly well.
Target definition is no small matter, especially when your mandate is to efficiently become “bigger.”
“This underscores the real challenge — when picking the criteria for prospecting, you are, either consciously or unconsciously, presuming that the criteria you are choosing, is actually predictive of buying behavior.”
Multivariate Modeled Selection, The Next Level Up in Sophistication — and Efficacy
In many cases, it may not be enough to choose variables and simply pick criteria to define your prospect. So some proven math and statistics can become the marketer's best friend. Models are statistically derived targets. When we build a model we start with data, not opinion. We then use a marketing database like BuyerGenomics, to provide customer intelligence and understand the varying segments of buyers that exist — even if we didn’t have resolution to see them yet.