If you've recently started a business, then you know how many decisions you have to make each and every day. The same goes for a successful business that has been operating for years. The difference is experience and a little known psychological technique called probabilistic thinking. Without realizing it, seasoned businessmen and women take comfort in reaching new business goals because they know what will probably happen if they take certain actions.
This may sound like an oversimplification of how established businesses make decisions, but it really has a sound foundation in the theory of probability. So, first let's define a few terms, then explore probabilistic thinking and help you reach your business goals.
Probability Theory as defined by Britannica.com:
"A branch of mathematics concerned with the analysis of random phenomena. The outcome of a random event cannot be determined before it occurs, but it may be any one of several possible outcomes."
Wakely Actuarial delivers actuarial services for your business using math and statistics to help and support business owners when making important decisions about product development, experience analysis, or the impact of financial projections.
Instead of a mathematical or statistical frame of reference, possible outcomes or answers to business decisions are derived intuitively. More emphasis is placed on the likelihood of an occurrence or event based on your awareness of past or present facts. The question may be, "Can you accurately predict the probable outcome of a right business decision versus one that is wrong?"
The answer to this question does have a mathematical answer based on probability theory, but that would take a lot of statistical math, some analytical analysis, and a lot of real data that most businesses don't have access to. However, you can use probabilistic logic to help you make good business decisions.
3 Frameworks that Shape Probabilistic Thinking
- Bayesian Thinking - Thomas Bayes was an 18th century minister and philosopher that thought long and hard on solving problems on the basis of chance. His theoretical body of work (Bayes' Theorem) emphasized adjusting your thinking and decision-making about what may happen - according to what you already know, along with any new information you encounter.
Bayesian philosophy will combine thinking and information that is derived from multiple sources, such as experimentation, real-world data, empirical data, market sector observations, your target customer, your financial situation, etc.
- Expect a fat-tailed curve - If all the outcomes of a single business decision were charted on a curve, instead of a symmetrical bell curve, the statistician would more likely see a fat-tailed curve. The difference being, in a bell curve the rise and fall of the curve is perfectly symmetrical and highly predictable. But, with a fat-tailed curve (as is often seen when analyzing this type of data), there will be a greater cluster of more common outcomes and smaller clusters of extreme outcomes.
- Asymmetries - Mostly anything that is perfectly symmetrical is generally pleasing to the eye and to the mind - just look at nature. And there is confidence that when we know how one side looks, then you also know exactly how the other side looks - if it is symmetrical. But, business decisions are asymmetrical (in an abstract sense). An expected or desired business outcome, especially with probabilistic thinking can be overestimated or underestimated.
In essence, you can have greater confidence when making business decisions if they're based on current and accurate data. Also, if you can point to similar outcomes in similar circumstances (even if this information is taken from another company's experience) consider it a good form of probabilistic thinking. Stay mindful of broken symmetries that can be used to your advantage when constructing business strategies. The asymmetrical nature of business will help you take into consideration unexpected changes and events that would be impossible to foresee.
Using Probabilistic Thinking in Business Decisions
If you start at the beginning and use a Bayesian philosophy when deciding on a business course of action, then you'll first gather any hard data such as competitor's market share, economic forecasts, or buyer demand. Probabilistic thinking is certainly not a shot in the dark approach, but is instead a very valuable decision-making tool,
Like Thomas Bayes, you'll look at the big picture. You'll examine what is the most logical outcome for the specific course of action, based on history or second-hand experience. Like an actuary, you'll enlist the help of statistical models, math, and logic. We live in a fast-paced, mobile world. So, you'll also want to get your hands on any real-time data to gain instant insight on what the economy and your customers are doing.
Next, you'll have to recognize when the data is pointing to the fat-tail portion of the curve. These are the areas that will be the most likely outcomes of business decisions. Why? Because this is what happens most often and it's probably what will happen the next time. Of course, this information may not be revealed in a plotted curve.
Maybe it's a pie chart, a graph, or an actuarial report. Still, since you don't know all the variables that the future holds, you'll need to recognize that this information is not perfect. It's a realistic estimation of what will probably happen (all things considered) when you make a certain business choice. If you want your business to compete, thrive, and win - then there will be a certain amount of uncertainty that is worth taking the risk.
And finally, when you add in that last layer of probability, asymmetry, then you'll realize even if you've seen this exact same situation before, there is a reasonable probability that it will be different this time. To your business advantage or injury, the best you can do is to use probabilistic thinking to reduce the chances of failure or poor outcomes due to bad business decisions.
What We’ve Seen In Our Business
When we analyze company experience for rate increases, a mathematical formula can apply to the amount of increase that a company can justify by direct comparison to original expectations. However, we also can predict from prior experience in the market that policyholder behavior may impact the results in that shock lapse and even anti-selective lapses can occur. As such, we may model scenarios trying to predict the impact on financial results when considering various levels of rate increase along with shock lapse and anti-selective behavior. This analysis then allows the company to decide on a reasonable level of rate increase (possibly lower than justified) that produces the most favorable financial result. This situation is a classic example of how a company can avoid getting in the weeds of the data by using probabilistic thinking to ultimately make a better business decision.