In the mid-2000s, a man went to his local Target supermarket to complain to the manager.
His teenage daughter, who was still at school had received a personally addressed flyer from Target. The flyer was advertising maternity products, babywear, baby furniture, nappies and infant formula.
“Are you trying to encourage my daughter to get pregnant”? Questioned the angry father.
The manager was very apologetic and couldn’t really explain why it happened.
Some weeks later the store manager rang back to apologise once again. It was then that the man admitted, “actually, there were some things happening at home that I was not aware of”.
It turns out his teenage daughter was in fact pregnant and she hadn’t told her parents yet.
Did the supermarket know the girl was pregnant before her own parents?
The answer is yes they did and it’s because of a man named Andrew Pole.
He’s a data scientist and economist and he started working for Target in 2002. His job was to use the masses of data Target collected from its customers via there loyalty program and use it to increase sales.
Target knew that customers buying habits & routines are very consistent. Once a person buys a brand or product they tend to stick with it. The same is true for the shops they frequent.
But there are a few times in peoples lives when they do change their habits. This is usually around significant events such as a marriage, divorce, moving house or changing jobs.
But the biggest event where people are most likely to change their buying habits is when they have a baby.
Target’s marketing department knew this and they wanted to get marketing material into the hands of these expectant mothers as early as possible so the would make additional purchases at Target stores.
They asked Andrew Pole if he could identify when a woman was in her second trimester.
Andrew came up with a list of 25 purchasing changes that a woman makes in the various stages of her pregnancy.
For instance, at around the third-month women switch form scented soap to unscented soap. In the fourth month, they start to buy supplements such as calcium, magnesium and Zinc. In the fifth month, they start buying cotton buds & hand sanitiser.
When Target’s marketing department started identifying these buying changes. They sent out the mailers with just baby and maternity products.
Clearly, that was a bit blatant and crude. So they started sending mailers that still contained the baby and maternity products but they included other products like lawnmowers, plants and appliances. That way it wasn’t so obvious.
Targets annual sales increased by 50% between 2002 and 2010.
With results like this, organisations around the world have embraced data and invested heavily in data analysts.
The great thing about all this data is we can make really good graphs and convince ourselves that we are making better decisions because we have more data.
Except all this data doesn’t mean we make better decisions. We can be blinded by data too.
Hillary Clinton had a data guy called Robby Mook and the Clintons had a lot of faith in Robby and his data.
Bill Clinton had a hunch that they needed to connect with white middle-class voters in the midwest. I’d say, Bill has a track record as being quite an insightful politician.
But Bill’s opinion was dismissed because Mook’s mathematical models showed they would win easily there.
Hillary didn’t visit Wisconsin once during the campaign, because it was a safe Democratic seat.
Except the data was wrong and it wasn’t a safe seat and Trump won it. The last time a Republican had won Wisconsin was Reagan in 1984.
Trump’s win is often explained as a result of under-educated, emotional & clueless middle voters. Maybe the Clinton loss was partly due to the reliance of hyper-educated and overly logical advisors?
All data comes from the same place, the past. It’s not good at predicting anomalies or non-typical events.
The data didn’t predict a Trump win or Brexit. The data didn’t predict that RedBull, a traditional Thai drink that tasted kind of gross, would become Cokes biggest competitor.
The data didn’t predict that Northern English mining towns would vote for an Eton educated Boris Johnson, but last month they did just that.
It seems to me data is only good if its combined with insight. At which point it becomes smart data.
Unusual things happen when smart data is involved.