Predictive marketing may sound like a new thing, but if you ask any retail buyer, they’ll tell you they’ve been doing it since fashion became a thing. The definition of a fashion buyer’s job is to figure out what customers will want next season–and in what quantity.
Big data changes the game. Predictive analytics, the science of interpreting data to make informed marketing decisions, is on the rise–using data to figure out what you want before you know you want it.
If you think it sounds like magic, you’re not far off. Magic works precisely because magicians know exactly where the audience will look, or what number they are likely to “guess.” They may not call it predictive modeling, but that’s what it is.
Your customer data may be more valuable than you think. One obvious example: A woman who buys maternity clothes now is going to be shopping for tiny clothes and assorted baby paraphernalia in a few months. But that’s only the tip of the iceberg.
The overwhelming amount of data available today makes it possible to consider every angle to tailor offers with terrifying accuracy, and that’s not a bad thing.
Predictive analytics can look at buying habits of one customer and all customers, day of the week and time of day when people like your customer are likely to buy and the buying habits of a specific customer. It can factor in data from trends, seasonal information, update age or stage of life data and make incredibly sophisticated recommendations.
While it may sound creepy, in effect, predictive data analysis delivers ads that won’t annoy consumers. Young people who like to mountain bike will get offers for bike accessories, eco-friendly water bottles, the latest in spandex fashion and anti-chafing spray. Cat ladies will see offers for the latest miracle litter boxes, catnip toys and, presumably, Roombas and shark costumes, instead of random ads for adult diapers, Viagra or weight loss supplements.
I read that phrase in a Marketing Week article and loved it so much I had to borrow it.
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