Adoption of Machine Learning Techniques Can Help Prevent These Shortfalls
A new international survey of 4,000 consumers from retail predictive applications firm Blue Yonder reveals that shopping experiences—both online and in-store—are not meeting today’s customer expectation of purchasing goods anytime, anywhere.
The research explored consumer shopping habits online, in supermarkets, in discount retailers and with mass merchants across the U.S., UK, France and Germany.
According to the research, 81% of shoppers say they are unable to get produce they want in-store, online and at discount retailers—yet 91% of grocery retail professionals are confident they are meeting customer expectations of availability. Of those that struggle with availability, 35% state they are let down at least once a week. This lack of availability is even felt when shopping online, with 69% stating they have issues. This rises in the supermarkets to 85%.
This highlights that replenishment is not working as well as the retailers think—which has wider implications for profitability:
- 30% of all shoppers abandoned their carts if they were unable to find the produce they wanted, with 28% saying that they felt unsatisfied when buying a similar product as a substitute.
- Lack of produce availability has caused 20% of shoppers to stop shopping with a retailer permanently or for a period of time, with this figure rising to 31% for online retailers.
Grocery retailers are overconfident of produce availability
This research was undertaken against a backdrop of enduring profitability issues in the grocery retail sector, and significant changes in consumer expectations. Given the paradigm shift that online retailing has caused in recent years and the subsequent shift in consumer expectations as to what constitutes ‘good’ availability, retailers are under intense pressure to provide optimal availability to their customers, while also turning a profit. Increased competition from new market entrants—who utilize data-driven approaches and automation at their core—are further intensifying this situation.
“We all understand replenishment is incredibly difficult to get right, especially in regards to fresh grocery,” said Michael Feindt, chief scientific advisor and founder of Blue Yonder, in a news release. “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.
“The demands on grocery management show no sign of abating in the always-on world. Yet, despite this, we have found in our previous survey of 750 grocery retailers that 46% of grocery directors admitted that their replenishment decisions are driven by gut feeling,” he added.
How to overcome these challenges with machine learning
It takes more than a gut feeling to deliver the best freshness and availability. 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.
Solutions based on artificial intelligence and machine learning technology learn from customer data, predicting their behavior and empowering grocers to determine the effect of each consideration, on each product, across all locations. Machine learning can effectively incorporate factors that gut feeling can’t, such as the impact of weather, holidays and promotions. This can be done on a daily basis, resulting in hundreds of millions of daily forecasts. Retailers using machine learning have seen a reduction of up to 80% in out-of-stock rates without increasing waste or inventory.
Download the complete report here.
Source: Business Wire; edited by Richard Carufel
