If you dig data, you could still choke on the 25-pound Historical Statistics of the United States, available for only $940.50 at Amazon.com. P. J. O'Rourke dissects it in The Atlantic pointing out the problem with data. Americans are eating less red meat and more vegetables, but still getting fatter. Divorces are down, but suicides are up. And -- get this -- from 1973 to 1994, smoking rates fell from 4,148 cigarettes per capita to 2,493, yet lung and bronchial cancer diagnoses are up 34%.
The message: Stopping smoking is dangerous, because without cigarettes you may get cancer.
The trouble with data like this is it is too easy to jump to conclusions, and to assume a change in A must drive a resulting change in B. Marketers run into this all the time measuring campaign results. Three common mistakes in advertising measurement are (a) setting metrics up too broadly so you cannot accurately track individual responses to individual ads, (b) neglecting to consider the impact of competitors on your results, or (c) failing to evaluate how shifts in ad channels affect each other. We've seen many clients with declines in print response AND increases in internet response who cannot connect these dots. In other cases, we've seen advertising results fall for no apparent reason -- until we begin tracking the major competitor moves whose gravity is causing our client's prospects to swing out of orbit.
Measurement is hazardous, because the wrong assumptions can lead to the wrong decision. Cause and effect, or effects with no cause? Until we figure it out, it's probably best to stop smoking.
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