As I wrote earlier, I'm building a "marketing analytics services" business -- once again, here's why. If you're a prospective client interested in the what of the offering, and the ideas below make sense to you, drop me a note.
Based on direct client experience, here are three themes I've worked out so far about how to approach building a firm's "marketing analytics" capability:
- Analytics should answer high-priority questions about how to grow the business. In one client engagement, we've built a simple financial sensitivity model to help us figure out where the biggest leverage from analytic "nuance" would be, and made sure up front that the potential payoff is worth the research effort.
- Analytics shouldn't be pursued without ready access to, understanding of, and trust in the underlying data. In another situation, we're untangling several years' worth of Webtrends report cruft. It's not only unclear what the reports are saying but also what data they are based on. More than likely, we'll start from scratch, and rebuild a very limited set of reports which we completely understand. We'll make better, faster decisions as a result.
- Analytics shouldn't get more than three months ahead of the ability to act on the answers. Each segmentation or test "cell" you define is pointless unless you can actually execute against it. Beware diminishing marginal returns and exploding operational complexity of finer and finer slices of the pie, and remember that "modularity" only goes so far before you spend so much time building towers of abstraction that you never actually get to market.
So, if your analytics project isn't clearly tied to answering a question a senior operating executive cares about, or you can't "see the data" in the analysis, or there's nobody around to do something with the insights, you're "overhead", at risk, and rightly so. A good alternative in these circumstances is to partner with an academic researcher who's willing to do the work and give you an advance peek at the insights in exchange for access to your data set.
I'm not much on slogans or buzzwords (though after listening to this, maybe I should be). But my head kept turning over the words "pragmatism" and "analytics". Finally, thunder struck: