We tend to assume that executives want the right answers about what’s driving their businesses, and that they will gravitate toward analytics as a means to provide them. However, that isn’t always the case, and probably should never be assumed.
Here’s an example. A few weeks ago, at a conference for financial executives interested in analytics, I met a quite senior analyst for a company in the home furnishings industry (I’m not providing the company name for reasons you will understand if you keep reading). My speech at the conference was partly about the need to use analytics to understand what nonfinancial factors might be driving your business, so that companies can begin to address how to change or at least react to them.
After my speech, this home furnishings analyst came up to me and said that he had already done this at his company. He said he hypothesized that his company’s sales were highly dependent on housing starts, so he ran some regression equations with various degrees of lag on housing starts—about one year was optimal—to see how much of the variation in company sales it explained. I won’t say the exact R2 number, but it was high—well over 50%. So in essence, most of the company’s sales this year depend on how many new houses were built last year.
There’s nothing better than knowing what factors drive your business performance, right? Well, not necessarily. He told me that his executives seemed largely uninterested in this finding, and didn’t use it for financial and production planning purposes. They didn’t dispute it—they simply ignored it.
Being the naïve fellow I am, I asked why. He cited several reasons:
- They wanted to portray to the investor community that their growth would be higher than what the dismal numbers on last year’s housing starts would suggest;
- They believed that their management talents were sufficient to overcome a weak housing market;
- If their business was simply to fulfill the demand from the housing market, what value were they adding as executives?
In retrospect, of course, it seems obvious why they didn’t want to use the analytics. They were in full contradiction to their self-perceptions of their skills as managers. They also made it appear that no matter what the executives did in a poor housing market, they weren’t going to be successful in growing revenues. The executives proceeded without the analysis, and of course overshot their actual revenues in terms of their Wall Street forecasts and production plans. The company is profitable, but not very much so.
The lesson is that before you go to a lot of trouble to create an analytical model, make sure that someone actually wants the result. Individual initiative is great, but there is only so much persuading one can do when the very idea of an analysis contradicts an executive’s view of the world. Maintaining a close relationship with a decision-maker will help to prevent this type of problem.
