Correlations are a poor way of assessing predictive ability

Firstly, I’d like to blow away the myth that correlations above 0.5 are spectacular in the social sciences. On pages 32 and 33 of my book “How Brands Grow” I present some car repeat loyalty metrics and market shares for the USA, UK and France. A quick calculation shows a higher than 0.6 correlation between repeat rates and market share. These sorts of correlations between brand performance metrics are the norm.

Secondly I want to highight the misleading claims of consultants peddling special brand health metrics who often claim correlations of say 0.7 between their special score for brands in a category and their sales – they say how amazing this is, and how it is proof of predictive ability.  Well here are my attempts to predict tomorrow’s temperature in Adelaide Australia, each prediction and reading is taken about a month apart starting in Summer and ending in Winter. I get it right that the temperatures go down as Winter arrives (big deal, it’s a bit like predicting that growing brands will increase sales a bit next period) but otherwise my predictions are miserable, they are always wrong, sometimes too high, mostly too low. The correlation however is very near perfect, 0.99 to be precise.

Actual temperature (Celsius) is in the left column, and my hopeless predictions in the right column:

39   43
32   36
24   22
21   18
17   15

r = 0.99

The moral of the story is that correlation is not a good indicator of predictive ability.

 

Professor Byron Sharp

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