Counting Context: The Sneaky Data that Moves Margins

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Counting Context: The Sneaky Data that Moves Margins
Counting Context: The Sneaky Data that Moves Margins

Sometimes, the stones unturned are under your very feet. At this exact moment, in boardrooms and break rooms, barrooms and conference rooms around the world, marketers are debating, head-scratching and feverishly researching how—with all the millions of bits of data possible—to deliver a more relevant marketing offer, deepen the customer connections and, ultimately, generate more revenue. At the same time, many of those many who are feverishly implementing new systems, employing new marketing technologies, and wrestling with massive math equations to make the system hum, are overlooking the basic datasets right under their noses that could change the entire equation.  D'oh!

While the “hard, fixed, undeniable” data is usually the most actionable—Johnny buys X, therefore we should offer him Y—it can also be problematic if not augmented with proper assumptions and real context found elsewhere. For the same reason you can't build out a floor plan until the foundation is set, marketers can't truly unlock that kind of hard data unless it's contextualized.

How, you ask?

Start by throwing out the idea of a prefabricated solution. Your data analysis won't come without establishing an understanding of the nuanced parts of your business—the data that's not being used. It's the data that's not in the system—or is in the system but deserves an asterisk. It lives between the lines.

Take organizational mapping, for example. How employees, departments, groups, and subgroups are organized differs from company to company and usually contains an anomaly, or “exception” to the business logic. Have a VP that manages marketing and retail sales and another that handles B2B and enterprise? Or a regional manager who has properties that offer a different matrix of skews? That's often where “soft” data hides.

As these departments offer up their reports, the crucial context is often lost. Nancy's group drove sales for Joe's. Everyone knows it, no one knows how much, everyone accepts it as “too tricky to untangle” and since “it seems to be working” the nuanced yet important data never comes to light and an opportunity is lost.

Oftentimes groups within companies are lumped together because it made sense to do so at a moment in time—but as the business grew those illogical patterns weren't addressed. And outside of retail where product hierarchy is well-oiled, knowing what to offer based on ancillary data points could make the difference between a sale and getting marked as spam.

Then there's mapping outside of the office. That is, how do you compare store locations based on geography? What identifiers are you using to classify your retail locations in such a way that they can be compared to other data points? You don't, for example, want to compare a rural store with one that's located in a mall and it's not fair to compare rural stores without considering other deciding factors such as proximity to an interstate or parking. There's destination versus walk-by traffic, location in proximity to other businesses, etc.  All of these seemingly “unquantifiable” factors can often escape the Big Data formulas but impact the marketing strategy and optimization process nonetheless.

You get the point. There are, hidden between the lines, a factor or set of factors that if addressed and calculated in can make a huge difference in your approach to judging ROI, scheduling your marketing investments and the like. Stop writing those things off as “just too hard to quantify” and start thinking about what you should allow in when you make your decisions. While the soft data may be harder to harvest and more slippery to grasp onto it can also make a huge difference when it comes to making decisions, generating better consumer experiences and, ultimately, increasing the ROI.



Mike Caccavale is CEO of Pluris Marketing and an expert in cross-channel offer optimization.

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