I frequently see people report results in terms of whole numbers. We had 1,000 phone calls. We had 50 orders. What I often see missed is putting these numbers in the context of a statistical performance. Metrics that allow you to assign a target, report on gaps and create plans to close the gaps.

1,000 phone calls are great if you spent $10,000 in media ($10 cost per call). If you spent $100,000, then it’s not so good ($100 cost per call). Same thing with orders. 50 orders from 100 calls are great (50% conversion of calls to sales). 1,000 calls are not so great (5% conversion).

See how the perspective changes of what’s good, and how you can quickly start to see the gap, allowing you to brainstorm on ways to close the gap?

Seems simple, and something every direct marketer knows. However, I’m amazed at how many times I see people report numbers without applying this type of logic and statistics to their reporting and insight. Even direct marketers. They report conversion statistics on their front-end metrics, but then will state that returns on the back-end are 100 units. What? What percentage of customers are returning the product? That’s what you want to know.

My simple but golden rule as a mathematician and marketing analyst has served me well over the years:

Everything has to add up to 100%.

When confronted with numbers, I immediately look for the breakdown in percentages. And if the numbers don’t add up to 100%, there is usually a quality problem in the data that you have to fix first. If they do add up to 100%, then what are the interesting ratios and percentages that I want to eliminate or increase? Once I have these benchmarks, I can then start to compare this metric over time. Which is just another way of saying Trend Analysis.

I encourage everyone, when confronted with numbers, to try and apply this simple rule. It allows you to see the data in a new light, and ask the appropriate questions to give you the insight you are seeking from the data. And it is an easy QC process to ensure the data is even accurate to begin with.

Seems so simple. Try it. I’m guessing you will surprise yourself with the value you’ll see in catching bad data or bringing you a new insight.