While the recent volatility in the economy has, for many companies, put a damper on marketing results so far this year, there are some emerging opportunities.
My firm has done a lot of analysis around utilizing econometric data to improve marketing models. The result: we have found that this data, when used correctly, can add 5% to 15% lift in predictive power at a minimal cost.
In performing this test, we collected data from a number of government/pseudo-government agencies at the Metropolitan Area (CBSA) level. Some examples of the type of data collected include Unemployment Filing Claims, Retail Sales, Bank Deposits (among many others). We selected certain attributes (driver elements) and developed time-series models to capture the trend line. These time-series models were then used as incremental inputs to existing marketing models to gauge the lift provided to a hold-out validation dataset. In a number of trials, we always achieved a K-S lift of 5%, and occasionally achieved a K-S lift of greater than 10%.
Does this make business sense? We think that, everything else being equal, individuals that appear to be homogeneous using ‘traditional’ marketing data will perform differently based on real-time trends in their neighborhood. For example, assume that Person1 and Person2 have identical marketing demographics (income, age, etc.), but that Person1 lives in Seattle and Person2 lives in Atlanta. As it happens, Person1 is lucky enough to own a home that is steadily increasing in value, and neighbors that are planning trips abroad and major purchases. Alas, poor Person2 is well aware that his home is decreasing in value (as evidenced by the sea of ‘For Sale’ signs on his street), and happens to have several neighbors that have recently lost their jobs. Do they have the same purchase behavior?
We believe that these results do make sense, and potentially offer a ‘new frontier’ for predicting response/purchase behavior.