The Problem With Precision

The Problem With Precision

Why are we so enamored with precision? Why is it that if we can’t get an exact number, then we don’t want any number at all? There seems to be a misunderstanding among many marketers that we must clear up. It’s much better to be generally right than to be precisely wrong. If you’re looking to project sales volume for a specific customer segment 3 years out and you’re asking for an exact sales volume number, then you’re asking the wrong question. Again, it’s much better to be generally right than to be precisely wrong.

This way of thinking is the norm in other disciplines such as finance. For example, a financial risk analysis involves assessing the probability of a variety of outcomes under a range of input assumptions, and making an educated investment decision. The same strategy can be effective in the marketing world, too. Rather than picking a number, we should evaluate a range of different scenarios and make an educated marketing decision based on the likelihood of various outcomes.

A set of techniques that can help you understand the likelihood of various outcomes are called “Monte Carlo Simulations.” Monte Carlo simulation methods are useful when modeling phenomena with significant uncertainty in inputs such as the calculation of risk in business. Let’s look at a visual to drive this point home. The “Bell-Curve” below is an example of a Monte Carlo simulation output. This brand is looking to better understand what their projected revenue would be considering a variety of factors with unknown values in the future.

It’s important to note that the learning below did not come from a small number of simulations. We ran one million simulations.

The x-axis in the graph below is potential revenue. The y-axis shows the frequency for which the simulations landed within a particular revenue range. We listed this as “probability.” For example, 30% of the simulations returned revenues in the range of $1.8-2.1 MM, while 27% fell in the range of $2.1-2.5 MM and another 25% in the range of $1.6-1.8 MM. So, given a certain range of input assumptions, there’s an 82% chance that the revenue would fall in the range of $1.6-2.5 MM. With this quantification, you can evaluate the risk/reward potential of the marketing activity under evaluation. Back to our example … if you had an 82% chance to win a bet, would you place the bet? I know I would!

bell_curve_example

The principle of “it’s better to be generally right than precisely wrong” is true in a lot of instances but it’s particularly valuable in planning. When we do market sizing analyses for businesses they often look at a few core questions:

  • What is the overall size of my market?
  • Where is the market shifting and where are the opportunities for growth?
  • What is the potential revenue for New Product X in the market?

By connecting general market data (growth rate, size, etc.) with internal sales data, you can begin to make generally accurate predictions about your business’ future sales and potential in the market.

3 Takeaways

  1. It’s much better to be generally right than to be precisely wrong.
  2. Rather than picking a number, evaluate a range of numbers and make an educated decision.
  3. If you have an 82% chance to win a bet, would you place the bet?

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