DALLAS: Supermarkets need to adopt machine learning techniques to overcome a disconnect between retailers' perceptions of product availability and those of shoppers, a new study suggests.
Blue Yonder, a provider of cloud-based predictive applications for retail, polled 4,000 consumers and 750 grocery professionals across the USA, UK, France and Germany and found that 81% of consumers claimed they were unable to get produce they wanted in store, online and at discount retailers.
It also reported that 91% of grocery retail professionals were confident they were meeting customer expectations of availability. And it cited a previous survey in which it found that 46% of grocery directors admitted that their replenishment decisions were driven by gut feeling.
While many grocery retailers realize they are under pressure to deliver the best customer experience possible, few understand how far they are from their goal, Blue Yonder stated.
"Replenishment is incredibly difficult to get right, especially in regards to fresh grocery," noted Professor Michael Feindt, chief scientific advisor and founder of Blue Yonder.
"Disruptive shopping behaviors have made increases in demand more variable while grocery shopping missions based on trust, freshness, choice and – of course – value, all add to the complexity of replenishment decisions."
Its recommendation was to step up use of machine learning which can effectively incorporate factors that gut feeling can't, such as the impact of weather, holidays and promotions - and can do all this on a daily basis.
Retailers using machine learning have seen a reduction of up to 80% in out-of-stock rates without increasing waste or inventory, it said.
That can have a significant impact on a retailer's figures, as 30% of all shoppers said they had abandoned their carts if they were unable to find the produce they wanted.
More damagingly, lack of produce availability had caused 20% of shoppers to stop shopping with a retailer permanently or for a period of time, a figure that rose to 31% for online retailers.
Blue Yonder, a provider of cloud-based predictive applications for retail, polled 4,000 consumers and 750 grocery professionals across the USA, UK, France and Germany and found that 81% of consumers claimed they were unable to get produce they wanted in store, online and at discount retailers.
It also reported that 91% of grocery retail professionals were confident they were meeting customer expectations of availability. And it cited a previous survey in which it found that 46% of grocery directors admitted that their replenishment decisions were driven by gut feeling.
While many grocery retailers realize they are under pressure to deliver the best customer experience possible, few understand how far they are from their goal, Blue Yonder stated.
"Replenishment is incredibly difficult to get right, especially in regards to fresh grocery," noted Professor Michael Feindt, chief scientific advisor and founder of Blue Yonder.
"Disruptive shopping behaviors have made increases in demand more variable while grocery shopping missions based on trust, freshness, choice and – of course – value, all add to the complexity of replenishment decisions."
Its recommendation was to step up use of machine learning which can effectively incorporate factors that gut feeling can't, such as the impact of weather, holidays and promotions - and can do all this on a daily basis.
Retailers using machine learning have seen a reduction of up to 80% in out-of-stock rates without increasing waste or inventory, it said.
That can have a significant impact on a retailer's figures, as 30% of all shoppers said they had abandoned their carts if they were unable to find the produce they wanted.
More damagingly, lack of produce availability had caused 20% of shoppers to stop shopping with a retailer permanently or for a period of time, a figure that rose to 31% for online retailers.
Data sourced from Blue Yonder; additional content by Warc staff
