Palisade customer Andrew Rudin, of Contrary Domino Partners, was recently featured in Customer Think, a global online community of business leaders covering customer experience and social business. Rudin’s piece comes in three parts:
“Revenue Uncertainty – Part 1: Known Unknowns, Unknown Unknowns, and Everything in Between,” discusses the multi-faceted aspect of risk.
“Revenue Uncertainty – Part II: Putting Uncertainty to Work at Your Company,” explains how to harness probabilistic tools to counteract the qualitative and subjective influences of human opinion in evaluating risks in a company.
“Revenue Uncertainty – Part III: How to Model Revenue Risk,” concludes the series with information on how to use statistical models and Monte Carlo analysis to develop a more realistic vision for revenue achievement under a set of assumptions or conditions.
Interested? More info on these informative pieces below:
In the Part I of the series, Rudin asks, “How do vendors sort through the universe of data, artifacts, anecdotes, and information to develop sufficient knowledge to place bets intelligently?” He recommends parsing out the uncertainty into three categories—the Known-Known’s, the Known-Uknown’s, and the Uknown-Unknown’s. “In the last twenty years, we’ve made great strides in adding to the corpus of known-known’s, and we’ve come a long, long way in learning how to discover the known-unknown’s. But we’re still left dangling, because we know that categorization only takes us so far. We still must answer, “now what?” And for that, we need mathematical rigor,” Rudin explains in his piece.
In Part II of the series, Rudin describes how bringing quantitative risk management can be difficult: “This yin-yang of risk seeking and risk aversion between and within individuals creates immense organizational challenges because people – not algorithms – still make most of the high-level, strategic decisions in an enterprise. And executives suffer a love-hate relationship with uncertainty by sometimes confronting it, sometimes sweeping it under the rug, and sometimes, doing both.”
In his final installment, Rudin discusses how companies can better plan for risk by using distribution models, running Monte Carlo simulations and looking at the different potential scenarios, and asking additional questions after examining the probabilistic data. “After all, what could be easier at the outset than pointing toward a revenue hill and encouraging your team to go take it?” Rudin writes. “But risk modeling will help you figure out whether there are any obstacles in between, and it will enable you to understand and estimate their magnitude. That insight will help you get past them, better ensuring your success.”