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Moneyball for Sales?

(This is a guest post by Yi Kang of the Marketing Leadership Council,our sister program for heads of Marketing.)

The PhDs are hired, the software installed, the data collected, and now the rest of the company waits eagerly for profit to climb – why shouldn’t it, now that we have advanced analytics? The popular book/movie Moneyball shows us that the Oakland A’s hit a homerun using sabermetrics like this, so then why can’t we? After all, if Billy Beane only had one Paul DePodesta, shouldn’t we do even better with an entire geek squad?

We all secretly wish for a magic weapon to vanquish competition. Where better to place our faith than in an analytical model churning out intimidating, neat lines of data? It’s a perfect deus ex machina to get out of a sticky situation.

However, as with anything complex, we forget that it’s one thing to own analytical infrastructure and another entirely to be able to use it well.

Regardless of whether you’re advanced enough to implement agent-based modeling or you’re just taking baby steps beyond bar and pie charts, a few ground rules remain the same when it comes to acting on analytics:

  • Set targets because your analysis is only as good as your data. Whether you’re pulling data from the CRM system or launching a new survey to gauge consumer loyalty, you should know up front what you’re looking for, starting with a list of hypotheses to prove or disprove. Ask yourself which metrics, markets and consumer segments are relevant. Gathering the analytics and non-analytics people together at this stage will help you figure out what is both interesting and feasible to test. If you’re pulling from existing data, be generous in allotting time for data cleaning, especially if it comes from disparate business lines and geographies, because something as simple as an erroneous currency or unit conversion could derail your conclusion.
  • More/ fancier analytics does not equal higher “analytical maturity”. For many companies, the arrival of analytics elicits two common responses – either a “yay for hard, definitive proof” or a “who cares…I use my gut”. In their survey of 5,000 employees at 22 global companies,  our sister program for IT executives, the CIO Executive Council, found that 43% of all employees are unquestioning empiricists, while another 19% are visceral decision makers – meaning they’re overly reliant on or lack appreciation for data.  Only 38% are informed skeptics, people who apply judgment to data, are aware of its limitations and their own biases, and are ready to teach.
  • Build your team of “T”s. “T-shaped” talent describes people with deep knowledge in one area (the “|” part) and are comfortable operating less deeply in a host of others (the “―” part). For an analytics person, that means statistical expertise complemented by a healthy dose of business acumen and communication skills.

It’s easy to get lost in the numbers and forget all about intuition and judgment. In general, the measurement and analysis of data is a challenging undertaking for a vast majority of companies – in fact, 80% of organizations are less than satisfied with their overall metrics management activities.

How are you tackling analytics in your organization? Let us know in the comments section below.

SEC Members, be sure to check out our new interactive sales metrics benchmarking tool as well as our study on inflecting sales performance through metrics and analytics.

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