Food markdown tactics: three levels of maturity
Food markdown tactics can help retailers sell products approaching expiry, but better financial results do not always mean less food waste.
At our recent food waste working group meeting, retailers described how they were improving:
- discounts;
- store routines;
- and customer communication,
We also talked through where those objectives could pull apart.
The discussion brought our food surplus management maturity model to life. Its three levels are:
- developing;
- established;
- and leading.
These categories help distinguish limited markdown coverage and data from broader, measured programmes and dynamic pricing.
The examples shared in our meeting illustrate those capabilities.
Developing: building confidence in markdowns
At the developing level, markdowns may cover only a few categories, with limited data to show how well they work.
Concerns about what discounted food says about a retailer’s freshness can also restrict their use.
One participant placed their business between developing and established, despite already piloting electronic date management and optimised markdowns.
The retailer reported reduced losses in the pilot, but still faced internal concern that a wall of yellow stickers could undermine its fresh-food image.
It was exploring different label colours, sizes and wording.
The participant also suggested that too many markdowns could be a sign of ordering, production or store problems that left too much food unsold.
Established: making markdowns a consistent store process
In the model, established retailers apply markdowns more widely and have better data, while relying on fixed discount amounts or percentages.
The meeting heard how those foundations remain important even after a business introduces dynamic pricing.
One retailer was tracking whether products had received a markdown before leaving the sales process for redistribution.
It wanted to give food a chance to sell before moving it to another route.
So, it added markdown compliance to an existing performance programme that rewarded stores for meeting their targets.
The retailer then reported an improvement. But, it was still examining the impact of timing, workload and differences between busier and quieter shops.
Another retailer described building a single platform for markdowns and expiry-date management around the needs of different teams.
- supply chain wanted revenue recovery;
- risk teams wanted fewer out-of-date products;
- and store colleagues wanted to avoid throwing food away.
The retailer was already using an algorithm to set discounts, but wanted the system to make it easier for store colleagues to find products approaching expiry.
Leading: adapting discounts to demand
Leading markdown capability uses algorithms to adapt discounts to factors such as stock and demand.
One retailer described replacing a single fixed discount with a choice of discount levels, with timing that varied by store and product.
Electronic shelf labels allowed prices to change without colleagues repeatedly replacing stickers.
A sticker still identified the discounted item and directed shoppers to the shelf label for its current reduction.
The retailer had also extended dynamic markdowns to bakery products near closing time.
Another retailer’s experience exposed a challenging trade-off.
Its algorithm sought to minimise the combined financial loss from discounts and unsold stock.
In trials against control stores, it reported lower total financial losses, but higher food waste.
Smaller discounts preserved enough revenue to outweigh the additional write-offs financially.
The retailer said donations had not risen enough to absorb the extra unsold food.
It also reported that results after rollout were being limited by store colleagues’ acceptance of the suggested discounts, and was using performance reports to identify where adoption needed attention.
How do food markdown tactics affect both margin and food waste?
Our discussion shows we need to distinguish the sophistication of a pricing tool from the outcome it delivers.
An algorithm optimising financial loss can produce a different result from one prioritising waste reduction.
Our research on the true cost of unsold food adds another consideration: the labour and handling costs attached to each exit route.
The wider food surplus management research describes leading markdown practice as continuing experimentation.
The meeting showed what that means in practice:
- adapting prices;
- checking what happens in stores;
- and recognising when an improvement in margin comes with more food left unsold.
