Channie Mize, SVP & Global General Manager for Retail at Periscope By McKinsey, gives her view on why prescriptive analytics is the new horizon for insight driven decisions in the grocery business.

Grocers analysing data to help manage categories is nothing new, but the use of prescriptive analytics for category management is transforming how they raise profits and engender customer loyalty. But what is new about prescriptive analytics and what makes that much better than traditional approaches?

Traditionally, at the start of the big data era, supermarkets hired data scientists who used descriptive analytics to understand the causes of previous successes and failures to help improve customer loyalty, pricing, promotions and assortment. Today, category managers at many supermarkets 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 don’t help grocers make the quick changes required in every store to maximise revenues and profits in today’s dynamic marketplace.

This is where we get to Prescriptive Analytics – which is the latest and most advanced technology to excite grocery category managers. It differs in that it uses machine learning, pattern recognition and models to proactively suggest next steps or actions based on analysis across complex criteria and data. The objective of prescriptive analytics is not only to predict future outcomes, but to make recommendations based on those outcomes. It is reliant on new transactional and non-transactional data tracked via the consumers’ digital footprints across multiple platforms.

Machines can now do more than crunch mountains of data – they can also recognise patterns humans can’t, learn from their mistakes and make specific, realtime recommendations that are easy to understand. Using the new approach, supermarkets can tap into in-house and external data to identify the handful of products 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.

Prescriptive analytics takes the heavy lifting off the category manager and allows the science/machine to do more of the work enabling greater operational capacity. This means that as a category manager, rather than fighting the data, which we have all done, you can focus on the job of making the decisions that improve your category performance.

Prescriptive analytics is not just for big retailers either, it is helping level the playing field for all grocery retailers, but especially bricks-andmortar stores because the tools make timely, tailored, granular sets of recommendations that category managers can accept or reject based on their real-world experience and business knowledge and the tool’s decision support.

For the customer, prescriptive analytics helps simplify their shopping experience by preempting their needs and suggesting products that they want before they are yet aware of wanting them – thus motivating even the most reluctant of shoppers.

Harnessing prescriptive analytics takes time. To get the full value of the new tools, workflows and methods, most store managers need to change the way they think and work. In our experience, widespread adoption also requires testing, patience and the right champions if it is to be a success.

Prescriptive analytics is much more than software and data science – it’s a way to combine the strengths of humans and machines to make better, faster decisions. Using it to make significant performance improvements therefore requires organizational and mindset changes. But it does not need to be an overnight change, planned incremental steps in individual categories can help build confidence in the technology and lower the risks. For those grocers prepared to try something new, there is a real opportunity impact the bottom line and wow customers.

 

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