Welcome to the Persistence ProjectBy Michael Raynor, Distinguished Fellow, Deloitte Research |
As we review the most popular studies of success, the lack of consensus is striking. Depending on whom you choose to believe, a strong acquisition engine can be central or irrelevant, reward systems definitive or a distraction, and strategic focus obligatory or optional – to name only three characteristics.
To sort through the confusion, we have embarked on our own research project. A quixotic quest? Perhaps: on its face, what we’re up to is no different from what’s already been tried. But there is reason for optimism. Our intent is to make The Persistence Project a different kind of study. One problem with much current research is the inability to tease out the essential elements of competitive success. A lot has been learned about how to do this kind of research better, and we intend to deploy the most recent advances in field research design and execution.
First, what constitutes greatness is too often held to be self-evident. Determining whether a company has been good enough long enough to be heard above the roar of a dynamic economy is no small undertaking. Getting this first step right is critical. The rigor of clinical analysis doesn’t much matter if the companies being studied aren’t great – but are instead merely lucky.
Choosing truly great companies to study, not just lucky ones, is central to the success of our project. Thus we are going public with our method. The monograph featured on this site, A Random Search for Excellence, explains why we believe it is both important and difficult to identify firms that are “more than lucky” before attempting to find noteworthy causes of their performance.
Second, we hope to analyze and synthesize relevant data in the new agora of blogs and wikis. Thanks to the emergence of mass collaboration using social networks we’d like to subject our work to scrutiny as it evolves. This will, we think, serve two purposes. First, it provides an opportunity for smarter and more informed people than we to look at our data and set us straight where we’ve made errors of fact or reasoning. Second, since there’s no such thing as “bias-free,” the best one can hope for is to be open to as many different biases as possible.
We invite you to read the monograph and comment on the blog. You’ll find a collection of topics to choose from. If we’re missing something you believe is important, be sure to let us know.
Thanks for joining the conversation.
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