In the dawn of the big data era, retailers hired data scientists who used descriptive analytics to understand the causes of previous successes and failures to help improve future pricing, promotions and assortment. Today, data scientists at many big retailers use predictive analytics to get directionally accurate forecasts for a handful of scenarios. But since these analyses focus on past results and don’t provide detailed recommendations, they can’t help retailers make the quick changes required in today’s omnichannel world and especially in stores to maximize revenues and profits.
We’re now entering the age of prescriptive analytics. Machines can now do more than crunch mountains of data—they can also recognize patterns humans can’t, learn from their mistakes and make specific, real-time recommendations that people without doctorates can understand.
Using the new approach, retailers can tap into in-house and external data to identify the handful of SKUs with the biggest impacts on basket size and profit and then make simple weekly or even daily recommendations to category managers to adjust pricing, promotions and assortment in each brick-and-mortar and online store to boost revenues, profits and customer loyalty. In fact, we expect prescriptive analytics to raise same-store sales by 2-5%.