With the ever-rising expectation of getting whatever we want, whenever we want it, companies are no longer waiting for orders to be placed. Instead, they are moving towards predictive fulfillment – using data and machine learning to foresee what their customers will buy and when, so they can send it to them pre-emptively.
Predicting the future used to be the preserve of prophets or fairground fortune tellers. Now, businesses are getting in on the act. Companies across different sectors are using machine learning to forecast what people are going to buy and when they are going to buy it – with so much accuracy that they’re willing to risk cooking, packing or shipping it before it has even been ordered.
‘What we are doing now in business, by default, is guessing,’ says Eric Siegel, former Columbia computer science professor and author of the book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie and Die. But, by bringing in data to inform your decisions, you can predict a whole lot better than guessing.
‘Business is a numbers game – you’re always playing the odds,’ says Siegel. ‘And now you are playing the odds more effectively because each prediction is essentially a probability. Each one will directly inform and improve the chances of making the right decision.’ Because computers are better at interpreting data than humans and can crunch numbers at speed, they can dramatically boost productivity.
‘Efficiency speaks to profit and the effectiveness of the organisation,’ says Siegel. ‘It’s the thing that you’re always trying to optimise no matter what mass-scale operations you’re doing.’ And what could be more efficient than shipping products before they have even been ordered?
Originally published in Contagious Magazine issue 48.
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The complete article covers examples such as Amazon, which “filed a patent for ‘anticipatory shipping’… to send packages to locations where it predicts someone will order them,” and the delivery-only restaurant Maple, which plots “delivery routes… plans the menus, forecasts how many ingredients to buy and tells the cooks what to cook and when.”