Perhaps you are already convinced and ready to draw behavioral economic insights into the critical decision-making processes of your organization. Or maybe you’re still a little skeptical and need more arguments; if so, think about the last big business decision that your company threw itself into.
It can be a major acquisition, large investment, a new IT system, the change of your strategy or the launch of a new product. In all likelihood, three things were part of that decision: A certain degree of data collection and analysis; the judgment and insight of one or more senior executives; and a – formal or informal – process in which a lot of data and assessments were translated into a decision.
When it comes to big decisions of this caliber, like most people, you’re likely to insist on thorough analysis and data to ensure you’re making the best and most informed decision. But here’s the problem: contrary to what most of us think, a thorough analysis in the hands of leaders with good judgment do not automatically turn into good decisions. The third factor, namely the decision-making process itself, is crucial for the quality of the decision, and this is where the behavioural economics comes in.
Not too long ago, two Harvard professors teamed up with McKinsey to examine the outcome of critical decisions in 1,048 global companies between 2004-2009. They were interested in investigating the factors that had led to the best results in a variety of decisions such as investments in new products, M&A and large capital investments.
Specifically, leaders were asked to answer the extent to which they had used 17 different practices to reach their decision. Eight of these practices had to do with the volume and level of detail of analysis, e.g. whether they had developed a detailed financial model or performed sensitivity analyses. The remaining nine practices dealt with the decision-making process, e.g. whether they had explicitly explored, raised and discussed significant uncertainties or views that contradicted those of the senior leader. The way the process practices were selected was by identifying some of the process properties that had been shown to be effective in overcoming biases through experiments and academic studies.
What was the conclusion? The companies with the best results had statistically fewer biases in their decision-making processes. In fact, the study showed that in widely different strategic decisions, the decision-making process meant significantly more than the nature and type of analysis – companies with good decision-making processes outperformed analysis-focused companies by a factor of 6, i.e. 500 percent better!
This was because by eliminating or reducing biases, companies managed to improve their ability to verify their ideas, assess the data base, incorporate the right views at the right times and make a more realistic assessment of the company’s capacity.
This is not the same as saying that the analysis was unimportant, since a closer view of the study showed that almost no decisions made through a good and debiased process were supported by a bad analysis phase. That’s because one of the factors that a debiased decision-making process will sniff out is poor analytical work. The reverse is not the case, as a superb analysis is useless in a bad decision-making process.
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