Case Studies and Blueprints

At a recent talk, we discussed misconceptions in business thinking. People offered compelling examples of how the dogma of the day did not apply to their experience. A common theme emerged: “I followed what X did, but it did not work for me.” I think case studies and other business insights are too often used as blueprints. They are not prescriptive and can be biased by the halo effect, a term coined by Phil Rozenweig.

Humans want answers. We want to understand not only what happened, but why it happened. But ‘why’ is often immeasurable, and we subconsciously use the nature of the outcome to assess the importance and value of the inputs. Starting with a known, quantifiable result (e.g. financial performance) biases our interpretation of how we achieved that outcome, especially when the inputs cannot be measured objectively. It's probably most effective to explain by example. Let's say you knew a coffee shop was doing well, and you were tasked with visiting the store to determine if and how the manager and her store layout contributed to the success. You are more likely to describe what you see as, “Cozy ambiance and dedicated manager who effectively supervises her baristas.” If instead you knew the coffee shop were struggling, you may describe the exact same coffee shop as, “Too packed, almost suffocating, and a micro-manager who slows down the entire operation.” It's the exact same manager and layout! With no metrics to quantify a manager’s efficacy or the effectiveness of a particular store layout, we let performance influence what should be a more objective assessment. If we can't measure it, we should not let "the halo effect" created by performance affect our conclusions.

The danger of the halo effect is not only that we interpret situations incorrectly, but also that these delusions creep into business thinking. We glamorize certain case studies and view their path to success as the path to success. We put their traits on a pedestal above all others. But this is not right. We do not have perfect information about what determines success, and over indexing on one example may dismiss many more important qualities that are not as salient. The recipe for a successful coffee shop does not mean it should be exactly like Starbucks. If we remember that outcomes are not perfectly correlated with inputs, we may recall that case studies and ’top 10 lists to success’ are biased; they can serve as convenient frameworks to understand examples of success, but they are not explanations of how to be successful.