A international survey of 4,000 consumers from Blue Yonder, a cloud-based provider of predictive applications for retail, looked at shopping experiences online, in the supermarkets, in the discount retailers and in the mass merchants in the USA, UK, France and Germany.
The research found that:
- While 68% of shoppers have felt disappointed with the freshness of their grocery shop, 81% of grocery retail professionals are confident they are delivering the right experience in Fresh. This highlights a gap between perception and reality.
- Even more concerning for retail, 54% of shoppers have been put off a particular grocery brand due to unsatisfactory freshness.
- This rises to 74% when shopping with the discounters.
- The younger generations are less forgiving, with 70% of 16-35 year olds stating they are put off by lack of freshness versus 42% of over 55s.
There were also interesting regional comparisons: Germany had the highest standards in terms of fresh with 62% stating they are put off shopping with a brand when they do not deliver the right fresh experience, versus just 45% in the USA, 53% in France and 55% in the UK.
Grocery retail is struggling with Fresh
The research is set against a backdrop of declining retail profitability and significant changes in consumer lifestyles. Retailers are under pressure to deliver the best freshness and optimal availability to their customers, while also turning a profit.
Professor Feindt, Blue Yonder's founder, says: "The real battle for grocery success lies in fresh and getting that right profitably. We've seen the younger generations are demanding a better experience in fresh and it is up to the grocery retailer to deliver. 40 per cent of grocery revenue is driven by fresh, according to the latest McKinsey report: get this right and the rest will follow, including the customer and profits."
How to overcome these challenges with machine learning?
Christoph Glatzel, senior partner, McKinsey says: "Most traditional supply-chain planning systems take a fixed, rule-based approach to forecasting and replenishment. Such an approach works well enough for stable and predictable product categories, but fresh food is more complicated.
"Machine learning – based on algorithms that allows computers to "learn" from data even without rules-based programming - allows retailers to automate formerly manual processes and dramatically improve the accuracy of forecasts and orders."
Only by using machine learning will grocery retail keep pace with the increasing number daily decisions needed in fresh. Machine learning also uses and learns from the large amounts of internal and external data to make the best decisions and balance availability, waste, customer experience, margins and profit.