We all make predictions everyday, pundits and media bloggers more so than the rest of us. Business folks in startups and enterprises are no exceptions – we predict the market size, our share of the market, revenue growth, effect of a marketing campaign etc. These predictions drive our decisions to launch a product, enter a new market or acquire a new business.
Most if not all of these predictions are just made up, with no legs in data, with no appetite for refinement and made solely to convey confidence than nothing else. Most predictions are black and white with no room for gray areas. This is because leaders are encouraged to show confidence and what better way to show it than by assertion. And confidence of course is usually confused with competence.
Those who want a way out deliberately make vague predictions like, “it is not that far out when Amazon Kindle will be free” and others line up escape clauses like, “if we execute well on the market opportunity it is all ours to take”. Worse, the ultimate copout which is seemingly well balance but wrong, “50-50 chance“.
We all making predictions with no basis in reality, no understanding of base rate, no intention of seeking data to refine our estimate, no desire to state our prediction as livelihoods, and definitely no patience to explain why we believe our prediction is correct.
There is a better way – Probabilistic Thinking. Jason Zweig of The Wall Street Journal points us to a book called Superforecasting and the associated website Good Judgement Project that show us and teach us how to get better in predictions and overall decision making.
“Superforecasting: The Art and Science of Prediction,” co-written with the journalist Dan Gardner, is the most important book on decision making since Daniel Kahneman’s “Thinking, Fast and Slow.”
You can cultivate these same skills by visiting GJOpen.com and joining the next round of the tournament. Or you can try refining your own thinking.
I have written here on decision making under uncertainty and how scenario analysis and likelihood assignments can help us make better decisions. The Good Judgement Project kicks it up several notches and offers us a solid framework to improve our prediction skills and decision making skills.
Take a look at this question at GJOpen.com on predicting Twitter CEO situation by the end of this year.
Unlike the media pundits who state with confidence, “it is going to be Jack,” this question asks you to consider more options and assign probabilities to each outcome. In addition it asks you enter a rationale to explain your choice of probabilities. You can’t get away with, “50-50 chance Jack will be appointed CEO”.
When you take what they teach you in this project to predictions and decision making in your business you are bound to improve quality of decisions and business output. I am 90% certain you will improve your decision making because this method teaches you a repeatable, defensible, data driven three step approach.
- Start with the base rate – What is supported in the history? If 90% of startups before you took 7 years to get to $200 million annual run rate, start with the notion yours will take as long.
- Ask what needs to get done to exceed base rate – If you re going to grow faster what needs to line up, what do you need to do? Look at those that grew faster than the base rate and see what drivers helped them. Are those endogenous drivers (actions the company took) or exogenous (external and random)? Write down your estimate of likelihood of you beating the base rate and your rationale.
- Take action. Collect Data. Refine the estimate and repeat – Decision making is not an event, it is an iterative process.
Are you ready to check your need to speak in absolutes, make bold predictions that have no uncertainties and instead practice probabilistic thinking?