Metrics at their best deliver game changing insight and wisdom. At their worst, they are misleading , irrelevant and a pain in the arse to collect.
So, what are the two characteristics that make a great metric?
The metric is a leading indicator.
A Leading indicator is a reliable measure of what will happen.
For example, if you have the data that shows that for every lead you generate, you convert 5% at an average purchase price of $50, and those customers buy twice a year for an average lifetime of 3 years, you can calculate with some confidence what each lead is worth to you. In this case, it would be: 100 leads X 5% X $50 X twice a year X 3 years = $1500.
The metric is causal.
The most common mistake I see, is metrics that confuse cause with correlation. There are many things that correlate, despite the fact that there is no relationship between them. One does not cause the other.
For example, there is a correlation between ice cream sales and drownings, which on a graph looks identical, but there is no causation between the two. Look deeper, and you might see that on sunny days, more people eat ice cream, and more people also go to the beach, swim, and therefore risk drowning. There is also a close correlation between ice cream consumption and a shark attack. This second correlation would also suffer from very ‘thin’ data, which make any sort of causal relationship even further from the truth. However, a glance at a graph, which takes on some credibility as someone has actually created a graph, would suggest there is some causation.
For a metric to be of any real use, it has to be the catalyst that changes behaviour, and delivers a predictable result. It is not always easy to sort the causal from the correlative. When you need some experienced wisdom, give me a call.
