I see a pattern emerging. Individuals, often those with some mathematical sophistication, gain access to their company’s Big Data and demonstrate amazing abilities to increase marketing productivity using the data. They move up the totem pole to a less technical/clerical position to one of leadership, strategy, creativity and reward.
This started with Marketing Mix Modeling (MMM) years ago but now has a second coming with the data exhaust coming off of digital.
One fly in the ointment is that in service of quick wins the protagonist in these stories often just uses digital data because it is handy -- which of course leaves out TV, in-store and other powerful marketing influences. When you leave out stimuli in analytics of stimulus-response, the response caused by the missing stimuli gets credited to the stimuli you have included. In a recent case one of the largest research companies in the world presented a massive analysis of digital data, admitting that the ROI credited to digital was 1500% off the mark established by MMM (with MMM firmly believed to be the right numbers by client management).
This post summarizes a short course I taught on April 8 on “The Holistic Use of Big Data.” You can watch the whole webinar here. The high points I advise:
Don’t just look at online sales, digital ad cookie exposures, direct mail and other easy in-house data; if you look you will find people who can provide TV set top box and offline purchase data, in-store stimuli, the ability to measure store traffic linked to cross-media ad exposure, more depth of household level demographics and many other important stimuli. TiVo Research/TRA is one example. They are already direct matching at the same-household level all of the following types of data and more.
Check out all the companies you know have set top box and/or offline sales data and compare what they offer in terms of making a grand Big Data match with all of the Big Data you already have.
Because display and feature in-store stimuli tend to be more powerful than media (not always), make sure to compare suppliers based on the percentage of stores that are audited weekly for these measures.
Compare suppliers in terms of the projected weighted sample versus universe estimates by demographics and geographics.
Make sure that households with DVRs are either included with full program episode-level actual DVR playback measurement, or are excluded.
Use the safe harbor method to direct match by household name/postal address and yet blind the research company to all such personally identifiable information (PII). The methods of doing this are now well known and practiced by Experian and Acxiom/ LiveRamp among others.
Seek sample sizes of at least hundreds of thousands for the “net inner circle” of the overlapping databases where you have every data point (e.g. digital, TV, offline sales, etc.) on every household in this charmed inner circle.
Avoid the use of fusion or other indirect matching/inferencing/lookalike techniques when analyzing and reoptimizing ROI. So far the only publicly known, ARF published, client-verified success cases in increasing ROI by making cross-media and creative shifts using Big Data are those where pure hard data were exclusively used, and all matching was direct at the same-household level.
If someone can send me a case where ROI increases verified by the client’s own sales data was caused by making media/creative changes in which fusion/geo-matching was involved, I will promptly publish that first example of the other kind, not seen by me as of yet.
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