He who divides and shares,always takes the best part.

- Chilean Proverb

The hunter does not rub himself in oil and lie by the fire to sleep.

- Nigerian Proverb

The watched chicken never lays.

- Congolese Proverb

There never was a five-pound note but there was a ten-pound road for it.

- Scottish Proverb

Nike’s decision to eliminate individual product categories — who could marshal exactly this kind of expertise — in favor of a generic one-size-fits-all data model created a problem that you can probably predict. This model performed the equivalent of handing out flyers for your pizza shop in the pizza shop’s lobby and signaled the business to reorganize from attracting new customers to pumping more money out of the customers they already had.

Unfortunately, that’s not what online advertising does. As Phil Bastien calls out here the most common outcome of advertising data is that if you buy a couch, it must mean that you need more couches.

The hard limits of data
It is wrong to suppose that if you can’t measure it, you can’t manage it — a costly myth. — W. Edwards Deming

Data isn’t worthless. Data is really, really valuable for telling you what has happened in the past. Great expense has gone into producing data that can tell you what’s going on in the present. But as the 7- and 8-figure salaries of quantitative analysts at hedge funds show us, using data to extrapolate what will happen in the future is one of the most challenging things you can try to do with it.

Human marketers and merchandisers know this — it’s why you might see paper towels next to the BBQ sauce or buns and hotdogs together at the store. Humans could enrich “you are buying bread” with the qualitative data point “toppings go on bread” to create a delightful shopping experience. But getting data — even data fed to an AI — to do this is much harder than you’d think.

Typically, the way one would do that is by harvesting what’s called warm data — the qualitative data that gives the numbers their meaning — and then using that to tell a story about where the numbers are going.

With the benefit of time and distance, it’s easy to condemn Nike for making this mistake. It’s even easier to believe that we can avoid it — because we would simply pick the right data to follow, instead of the wrong data.