Data whisperers

This is the era of “Big Data.” Or so a lot of pundits and conference speakers tell us relentlessly.

Of course it’s true that with better technology–and myriad new track-able customer interactions–we are now faced with a huge, expanding and sometimes overwhelming amount of customer data. Big data indeed.

For many, the focus has been on what data to collect, how to collect it and which technology to deploy. That’s a mistake.

More data without a customer insight strategy is irrelevant. New technology without humans who know how apply it is a waste of money.

Truly customer-centric companies invest first in 3 core things: a clearly articulated customer growth strategy, a robust customer insight plan and people who can apply useful intuition to a growing mountain of data.

I call this last group the “data whisperers.”

Data whisperers blend a highly technical skill set with pragmatic knowledge of how to usefully segment, attract, engage and retain your most valuable customers.

Data whisperers see the forest AND the trees. They are the secret sauce that make big, complex and expensive technology solutions actually generate actionable customer insight. They help your organization treat different customers differently.

So read the blogs, review the literature and enjoy the conferences.

Just don’t lose sight of what makes the difference between big data and big insight.





3 thoughts on “Data whisperers

  1. My experience is there are two paths. One is to come up through the analytics ranks with a strong general manager mentor who gives the person plenty of exposure to the broader business strategy and plenty of practice with working on implications and testing. The other is the person who takes the more generalist route, but invests in learning the key analytics tools. In both cases, lots of practice in practical application is key.

  2. While I agree as to the paths from whence whisperers come, Theresa’s query as importantly turns on her word choice “find”. Ultimately, the largest-in-category retailers will have to invest heavily to ‘make’ their own and not ‘buy’ or ‘borrow’. If insight is to be THE differentiation strategy of last resort, then the quality of the models, analyses, and primary research needed to scale-up insights into campaign-readiness must themselves be differentiated. The first competitive threshold will be reached when Tier I retailers model what is causing each consumers’ choice to buy a particular 4/6/8P over another alternative 4/6/8P. Mass marketing’s correlation ain’t gunna be good enough in a competition for customer-centric relevance; we need models of causation.

    Twenty years ago, a large (and expensive) team made such models for two of IBM’s largest and most profitable businesses. The multi-disciplinary team included both of Steven’s types of whisperers. At the final presentation of Phase 1 results for North America, the head of Marketing asked why — with all the primary information, the third party data, all the custom research, etc. that there was (and IBM had it all) — why a company as powerful as IBM could not ever before get to the insights created by that team.

    Knowing the answers to that question, apropos of your retailer-employer’s circumstance, will be critical in choosing both how and to what degree to fortify a customer-centric strategy with quantitative weaponry. A program of continuously improving the right models is far better than hoping a big data, big bang singularity will create a new world in ways beneficial to your shareholders. Formalized heuristics of causation can make far more effective predictions than quantitatively rigorous models of correlation. A right-minded strategy supported with sound executional logic can always be improved with incrementally greater accuracy and depth.

    How analytically deep to go will be a cost/benefit trade-off. A well-structured customer-centricity strategy ought not be.

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