About a dozen years ago, when I was working for a large financial services firm, one of the senior executives asked me to take on a project to better understand the company’s profitability.
I was in the equity division, which generated fees and commissions by catering to investment managers and sought to maximise revenues by providing high-quality research, responsive trading and initial public offerings. While we had hundreds of clients, one mutual fund company was our largest. We shuttled our researchers to visit with its portfolio managers, dedicated capital to ensure that its trades were executed smoothly and recognised its importance in the allocation of IPOs.
Part of my charge was to understand the division’s profitability by customer. So we estimated the cost we incurred servicing each major client. The results were counter-intuitive: Our largest customer was among our least profitable. Customers in the middle of the pack, which didn’t demand substantial resources, were more profitable than the giant we fawned over.
What happened? We made a mistake that’s exceedingly common in business: We measured the wrong thing. The statistic we relied on to assess our performance – revenues – was disconnected from our overall objective of profitability. As a result, our strategic and resource allocation decisions didn’t support that goal.
This article will reveal how this mistake permeates businesses, driving poor decisions and undermining performance. And it will show you how to choose the best statistics for your business goals.
Many business executives seeking to create shareholder value rely on intuition in selecting statistics. The metrics companies use most often to measure and communicate results – often called key performance indicators – include financial measures such as sales growth and earnings per share growth in addition to nonfinancial measures such as loyalty and product quality. Yet, as we’ll see, these have only a loose connection to the objective of creating value. Most executives continue to lean heavily on poorly chosen statistics. They have a gut sense of what metrics are most relevant to their businesses, but they don’t realise that their decision-making may be skewed by cognitive biases.
Through my work, teaching and research on these biases, I have identified three that seem particularly relevant in this context: The overconfidence bias, the availability heuristic and the status quo bias.
OVERCONFIDENCE: People’s deep confidence in their judgments and abilities is often at odds with reality. Most people, for example, regard themselves as better-than-average drivers. The tendency toward overconfidence readily extends to business.
AVAILABILITY: The availability heuristic is a strategy we use to assess the cause or probability of an event on the basis of how readily similar examples come to mind – that is, how “available” they are to us. One consequence is that we tend to overestimate the importance of information that we’ve encountered recently, that is frequently repeated or that is top of mind for other reasons.
STATUS QUO: Executives would rather stay the course than face the risks that come with change. The status quo bias derives in part from our well-documented tendency to avoid a loss even if we could achieve a big gain. A business consequence of this bias is that even when performance drivers change – as they invariably do – executives often resist abandoning existing metrics in favor of more-suitable ones.
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