Picking the right sales metrics: Steal a page from Moneyball
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Picking the right sales metrics: Steal a page from Moneyball

One of the key lessons of Moneyball, the legendary book and film about the statistical revolution that transformed Major League Baseball, is that the yardstick you use to measure success makes all the difference.

In baseball, there’s only one metric that matters: How many games did the team win by the end of the year? The problem, of course, is that it isn’t much help for managers who are making decisions about which players to sign, which position to put them in, what to do when a left-handed batter is facing a right-handed pitcher who’s behind in the count, or any of the other countless decisions that make the difference between winning and losing.

Before Moneyball, managers used some combination of gut instinct and such time-honored but unproven metrics as batting average to figure out who was contributing to the team’s success, who was holding it back, and what to do about it. But it didn’t take much analysis to see that many of these metrics — including batting average — didn’t tell you much. In baseball, a walk is as good as a single (better, if there’s a man on first). Yet walks weren’t measured, which led to counterproductive behaviors. Managers didn’t reward players who got a lot of walks, so players with an eye on their future market value didn’t try very hard to get any. A better metric — on-base percentage — not only helped predict a season’s success, but also got managers, coaches and players focused on the right behavior.

It’s not too hard to see a sales analogy in all this. In a 2012 article in Harvard Business Review, author Michael Mauboussin tells a story about a similar “aha” moment at his company. They’d naturally assumed that their largest customers were also their most profitable. So, much like baseball managers relying on batting averages, the company managers were using revenues as an indirect measure of success. When they started measuring profitability, a much different picture emerged. Mid-sized customers generated the best profits. That meant certain sales behaviors contributed much more to the win than they were getting credit for.

Relating metrics to desired outcomes may seem obvious, but it’s easier said than done. Measuring profitability, to cite just one example, requires companies to consider fixed and variable costs, how best to link them to revenue and how the various moving parts come together in the end. And that’s before you even begin to argue about how to move the needle. I’ve been in those meetings. They’re murder.

But if the alternative is to keep promoting the same behaviors to produce the same middle-of-the-pack results, the pain is worth it.

Mauboussin lays out a four-step process to get the right metrics in place:

1. Define the governing objective

Again, it’s not as simple as it sounds.

More sales, you say? Plenty of companies have sold themselves into oblivion. More profits? It’s easy enough to juice profits in the short run by slashing expenses. Long-term value? How long is the company — and its shareholders — willing to wait? These are deep discussions, but necessary ones. Top management needs to be in alignment about what drives economic value for the company.

2. Develop a theory of cause and effect

This is where gut instincts and data come together. Assume, for example, that you believe long-term revenue growth and profitability are driven by customer loyalty. It makes all kinds of sense. But are there data to test it? For example, have low revenues and profits been associated with high customer churn in the past? Based on past history, can the cost of customer acquisition be measured, and compared to the cost of customer retention?

3. Identify activities and behaviors that support the governing objective

To continue our example, if the objective is long-term growth and profits, and the path to achieve that is customer loyalty, what behaviors are needed to increase customer loyalty? And how can those behaviors be promoted?

You can’t put all the burden on reps, by the way, any more than a baseball manager can only look at players’ behavior on the field. Are hiring managers recruiting the right kinds of candidates? Are sales managers coaching the right behaviors? Do salespeople have tools that can help them win greater loyalty? Is senior management doing enough to identify and head off perverse incentives (for example, winning short-term “loyalty” through unprofitable price concessions, or continuing to tie sales compensation only to hitting the numbers)?

Also, as any team owner knows, you need an ongoing system of follow-up to make sure that the right behaviors are sustained over time. Training doesn’t end when the season starts.

4. Evaluate and re-evaluate your stats

Statistics are a tool, not a magic bullet. There’s no guarantee that you’ll set up the right metrics on the first try. Maybe you discover, for example, that customer loyalty is too broad a metric, and that loyalty based on relationships is more valuable than loyalty based on price or convenience. The more you learn, the more you’ll be able to tease out the metrics that best predict ultimate success.

Source: Maubossin, M. The true measures of success. Harvard Business Review, October 2012.

1 Comment

  • Ludovic Vuillier says:

    Thank you for this article! I often have this discussion with clients who either measure nothing (which leads to no data available for developing strategy) or everything (which leads to too much data to be useful and a waste of time).

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