It’s not news that the state of retail is changing. The constraints inhibiting some retailers from keeping up with market disruptors like Amazon and Walmart are creating a tipping point in terms of leveraging AI to automate everything from inventory to promotional marketing. We hear a lot about AI, but it’s not always clear what it can do for large enterprise grocers and retailers.
Taking into account all the moving pieces required to run an enterprise retail business, it’s the most operationally heavy vertical in the world. The idea that generic AI can address all the challenges retailers are facing today was never a feasible approach. When retail executives are looking for solutions, they don’t set out looking to buy AI at the outset, they’re looking to solve real, tangible and significant business problems like how to grow their loyalty base, reduce store level stock-outs and overstocks, increase revenue, improve promotions and reduce manual processes.
The fact is, they need more from an AI company than just deep learning, global infrastructure and top talent; they need a specific use case, a product, ROI, and most importantly, a fast timeline to implementation and value. Retailers operate day to day, week to week and month to month, with targets to hit at each interval. Solutions must have time to value that spans months not years.
Using AI and data science to automate business decisions in merchandising and marketing operations is the solution for retailers to remain competitive. This low-hanging fruit is only the entry-point for enterprise AI, with the potential to go much further.
Think about store experience — using security cameras to reduce loss prevention, determine how staffing levels impact sales and improve the smoothness of self-checkouts. Take it even further to actually understand what people want; what they want to buy, why they want to buy it and which channel they prefer to engage with. Retailers have integral data at the ready empowering them to cater to their customers in very specific ways. Enterprise AI can transform and optimize this data to enable retailers to tune into customers’ needs and shopping habits, whether it’s through promotional planning, loyalty management or product selection.
Rubikloud partnered with a national grocery retailer to help minimize the amount of product spoilage due to forecast inaccuracy. Discarding expired product was having a negative effect on the retailer’s margin. To solve this problem, Rubikloud’s machine learning forecasting system considered multiple factors, such as cross-product and halo effects, to intelligently adjust the forecast.
By considering cross-product effects, an insight was uncovered that had a direct effect on spoilage. The retailer’s existing system was over-forecasting when a highly cannibalistic product was on promotion – leading to a large amount of expired inventory in future weeks. With this insight applied to Rubikloud’s forecasting, among many others, the retailer’s forecast accuracy improved by +7% for a key traffic-driving category. Extrapolated to full chain, Rubikloud’s overall forecast accuracy improvement would represent approximately $40 million in margin impact potential.
Using Rubikloud’s enterprise AI solutions, a $2 billion mass retailer also saw the below results:
- 40% increase in forecast accuracy leading to up to $12.5 million saved by holding less excess inventory in store.
- 31% reduction in stockouts leading to up to $7.5 million in sales.
- 50% reduction in time spent managing a promotion campaign process, saving up to 1,600 full-time employee days in a year.
Investment in enterprise AI that is focused on a specific problem or use case will help retailers make better decisions that result in efficiencies, increased revenues, and better customer experiences and allow retailers to see time to value on their investment in a matter of weeks and months versus years.
This piece is adapted from a Retail TouchPoints special report and was originally published on Rubikloud's blog.
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