Climb mountains, not molehills

When your data is myopic, it can lead you in the wrong direction. That's a lesson that The Coca-Cola Company learned famously with its failed attempt to launch a sweeter, milder Coke in response to consumer taste tests. And it's a point I find myself making time and again in my conversations with heads of marketing and other business leaders. Now, thanks to a stunning recent blog post from Eric Ries, I have a new way of explaining this point: "the local maximum problem". Eric visualises the myopic use of data like this:

Whenever you’re not sure what to do, try something small, at random, and see if that makes things a little bit better. If it does, keep doing more of that, and if it doesn’t, try something else random and start over. Imagine climbing a hill this way; it’d work with your eyes closed. Just keep seeking higher and higher terrain, and rotate a bit whenever you feel yourself going down. But what if you’re climbing a hill that is in front of a mountain? When you get to the top of the hill, there’s no small step you can take that will get you on the right path up the mountain. That’s the local maximum. All optimization techniques get stuck in this position.

For example, last year I did some work with a marketing agency that, in Michael Porter's terms, had a adopted classic "best cost provider" strategy. This is the strategy in which the firm offers a reasonable amount of everything the market requires: low prices, high quality, and variety. It provided a wide array of services, but not every single service you could imagine. It's pricing was reasonable, but not bargain-basement. It's quality was good, but not stellar. It was a viable enterprise competing with other "best cost provider" agencies in its space, but it wanted to kick-start a new era of profitability and sustainable growth. The question, then, was whether a "best cost provider" strategy was optimal for this agency, or whether it should instead focus on providing the best possible pricing (productise everything, offshore when possible, ditch all complex services, etc), the best possible service (invest in skills, specialise around a smaller service area, etc) or the broadest possible range.

The agency was determined to answer this question with data. That data would point to the profitability of each of its service lines and each of its customers, but it would all be predicated on existing conditions. Yes, this data is part of the answer, especially when it comes to optimising around a current strategy. However, it's not enough to determine if your strategy is the right one. Thanks to Eric Ries I now have the perfect visual to explain this. The visual starts with a hill, and an arrow showing how quickly the company can climb it guided by existing data. It then crosses to a larger landscape, in which each hill is an alternative strategy. The question is: Does the company have the vision and data to assess which of those alternatives are molehills, and which are truly mountains?

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