Sales leaders often have to make decisions based on limited data.
The goto solution for this is to look at ratios, comparing performances. But there are often natural random variations in these measurements. And simple ratios can be misleading—even when you see large differences, particularly if you have small samples.
If you want to know how sound a comparison and your decision is, you need to perform a statistical test of significance. Fisher's exact is a good way to do this.
Here’s a typical example. We want to see how good salespeople are at forecasting.
Let’s say there are two salespeople and they each Commit three deals to close in the quarter.
Salesperson | Commits | Won | Not Won |
Amy | 3 | 3 | 0 |
Bob | 3 | 1 | 2 |
Amy is making 100% accurate Commit calls. Bob is at 33%. Looks like Bob is bad at forecasting Commits. Should you reward Amy and penalize Bob? Could that observed difference be due to randomness?
One way to answer these questions is to do a statistical test of significance. And since this is a small sample size, you can use Fisher’s Exact Test. [1] (For larger samples there are alternatives.)
Use our Fisher’s Exact calculator here: https://login.funnelcast.com/fisher/
(Un-gated link. Detailed instructions on page.)
In this case, we can reduce the problem to the following table. And get a p value of 0.4.
What’s that mean? [2] A p value of 0.4 means that there’s a 40% chance that the observed difference is due to random chance. That’s quite likely. Using the social-sciences 0.05 threshold [3], we can’t say that they are different at forecasting.
You can of course decide how confident you want to be about the decision. For instance, if you are ok being wrong about a decision 40% then go for it. Or, if that's all the data you have to go on, and you need to make a decision, then hope for the best—knowing you'll be wrong 40% of the time.
Note that this is just a test of whether Amy and Bob are performing differently. But let’s say that there is a 90% accuracy standard for Commits. Are Amy and Bob meeting the Commit standard?
You can use the Fisher’s Exact calculator to answer that question. In this case, we create an ideal process to compare Amy and Bob to. Say 9,000 won, 1000 not won. [4]
Comparing Amy's (3 – 0) Commit forecasting to the idea process, we find that Amy’s forecasting has a p value of 1.0000. It is indistinguishable from the ideal. Good for Amy.
But Bob’s (1 – 2) Commit forecasting has a p value of 0.02801. Using the 0.05 threshold, Bob is likely not meeting the standard.
It's very difficult to compare intuitively two points statistics. People are really bad at assessing probabilities. So for guidance on it, do a statistical test of significance. Use the Fisher's exact test. [5]
Measurements presented without a sense of their variability are meaningless. You can't tell if they are really different from each other or even representative of the underlying process. You should seek a statistical significance test for every measurement you explore.
Funnelcast uses math like this to help you sell more. You don't need to understand the details. We do the heavy lifting. Just follow the Funnelcast sales coach's advice and work on the things it highlights.
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[1] Fisher's test assumes independence between Amy and Bob. Maybe that is so, maybe not.
[2] Statistical tests are based on a null hypothesis. In this case, the null hypothesis is that Amy and Bob are equally good at forecasting. The p value estimates how probable that is. The smaller the p value, the less likely is the null hypothesis. For sufficiently small p values, we reject the null hypothesis and conclude that there is a difference between Amy’s and Bob’s forecasting.
[3] In social sciences, the most common p value to reject the null hypothesis is 0.05. If the p value is less than 0.05 then there's a less than 5% chance that Amy and Bob are equally good at forecasting Commits.
[4] Those familiar with the Fisher's Exact formula may question the numerical stability of calculations based on such large figures. The calculator uses a numerically stable approximation.
[5] Fisher's exact is designed for small data—typically fewer than 10 observations per sample measurement. The Fisher's Exact calculator works for larger samples as well. You can use other tests for larger samples, such as the Student's t-test.
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