Seasonal AI spending under scrutiny
- AI can help create campaign variations, target offers and check store execution, with each application requiring its own measure of success
- Missing displays and poor replenishment can waste advertising investment and leave competitors to capture demand.
- Customer data, current buying patterns and weather conditions should inform seasonal targeting and timing
- Store alerts need clear responsibilities and prompt action to recover sales before the promotional window closes
- Producers should assess AI against campaign costs saved and additional margin earned
By the time a Christmas promotion reaches the supermarket, a brand owner has already placed several expensive bets. The festive packaging will catch someone’s eye, the discount will persuade them to buy and the display space will justify its cost. Yet all that preparation can come undone because the fixture hasn’t been assembled or the stock is waiting behind a door shoppers can’t open.
An underperforming campaign can then send producers hunting for faults in products customers barely had a chance to buy. Was the flavour too adventurous, the pack too large or the advertising too weak? Sales records show what passed through the till, but they don’t establish whether the agreed promotion reached the shop floor in the first place.
With Halloween approaching and Christmas campaigns following close behind, there’s plenty riding on getting those basics right.
The US National Confectioners Association reports that Valentine’s Day, Easter, Halloween and the winter holidays accounted for 62% of confectionery sales in 2024, when the market exceeded $54bn. Circana also found that a third of US baking-aisle sales occur in the fourth quarter, giving suppliers a concentrated opportunity to sell – and less time to recover sales lost to missing displays or poor replenishment.
AI now has a role at several stages of that spending. Generative tools can draft advertising copy and produce creative variations, predictive systems can help select audiences using customer data, and image recognition can inspect photographs of the resulting store displays. Producers could use those capabilities to get campaigns out faster and investigate why they underperform, provided they’re clear about which problem each tool is supposed to solve.
From festive advert to empty shelf
Kalliopi Vlastos, VP of sales at retail execution technology supplier Form, has heard accounts that suggest some businesses still depend on senior executives stumbling across problems. “Retail executives have told me, on multiple occasions, that they’ve gone shopping themselves only to discover that an endcap is set up incorrectly or an agreed-upon planogram hasn’t been followed,” she says.
An endcap is the display at the end of an aisle, while a planogram specifies how products should be arranged. Negotiating promotional space and agreeing the assortment takes time and money, so discovering that the arrangement hasn’t been delivered during somebody’s shopping trip should prompt scrutiny of how it’s being monitored.
Form’s platform provides one example of how AI enters that process. Field representatives take photographs using a mobile device, and image recognition identifies products and promotional materials in the pictures. The company says its software can categorise the findings by brand, product and promotion, then make reports available to field teams and managers.
The photograph therefore becomes something a business can analyse across locations, rather than an image somebody has to inspect individually. Comparing the findings with the agreed requirements can help flag an incorrect assortment or missing promotional material while there’s still time to intervene.
Consider a hypothetical Christmas biscuit promotion: a representative photographs the end-of-aisle fixture, the software identifies the visible packs and the results are checked against the expected range. If an everyday assortment has occupied space intended for the festive launch, the team can investigate and request a correction during the visit.
“Typically, store photos have existed as a record of a point in time,” says Vlastos. “The opportunity for brands today is to turn these images into instructions in real time so a rep can act on them before leaving the store.”
Brand owners can spend heavily on advertising that brings shoppers into stores, only to lose the sale to a competitor because their product isn’t where it should be. Those missed purchases become harder to recover once the occasion passes: a pumpkin-covered party pack loses much of its appeal after Halloween, while clearing Christmas stock through January markdowns can eat into the margin the promotion was supposed to deliver.
However, AI is also being used further upstream, where the campaign itself takes shape. Creative automation supplier Storyteq describes using AI to generate messaging variations and adapt approved campaign material for different audiences and formats. Its guidance also warns that automated seasonal content can fall back on clichés without careful human direction.
That leaves producers with two separate investments to assess: whether AI helps them make better, cheaper marketing materials, and whether it helps those materials translate into purchases.
5 ways to put AI to work without wasting more money
1. Give creative tools a proper brief
Asking a generator for ‘a festive snack campaign’ invites familiar snowflakes, sharing messages and Christmas puns that could belong to almost any brand. Supply the approved product info, target shopper, occasion, tone and offer instead, then use the tool to develop variations that the marketing team can assess.
Storyteq advocates keeping people responsible for the central creative concept while using automation to adapt it across channels. A producer might apply that approach to different messages for a gifting assortment and a party-sharing pack, with staff checking that generated copy and imagery accurately represent each product.
2. Use customer data you can actually access
Predictive marketing tools can analyse purchase and engagement histories to help identify audiences for different offers. Klaviyo, a customer marketing platform used for channels including email and text messaging, describes AI capabilities that use those records to group customers by their behaviour and help create targeted campaigns.
Brands with their own online shops could use previous purchases to distinguish customers who buy gifting assortments from those who favour everyday snacks, then tailor seasonal offers accordingly. Manufacturers selling mainly through supermarkets need to establish what retailer or retail media data they can access, how detailed it is and how it can be used before committing to personalised campaigns.
Store-level sales figures can help identify promising locations for an offer, while individual purchase histories can support customer targeting. The campaign needs to reflect the evidence available, with the marketing team checking that the selected audience and proposed offer make sense before launch.
3. Check whether shoppers are ready for the season
NIQ’s latest UK figures show that warm September weather helped lift ice cream sales by 11.7% in the four weeks ending 3 October 2026, while hot chocolate fell 11% and ambient soup declined 5.4%.
Those sales-value movements suggest shoppers weren’t following the autumn script. Producers using AI to analyse campaign or category performance should include current conditions alongside historical results, then review whether suggested messages still suit demand. Generating more versions of an autumn campaign won’t resolve a timing problem if customers are still shopping for summer.
4. Connect store findings with somebody who can act
An image recognition system may flag an apparent shelf gap, incorrect product or missing sign. The representative still needs to establish what caused it and who can resolve it: replenishment requires available stock, price corrections may need retailer approval and a missing fixture might require equipment elsewhere.
Agree to those responsibilities before expanding the programme. Test recognition on the actual festive packs, too, including similar flavours, promotional sleeves and reflective bags, so the team understands when a result needs another photo or human review. Fresh ranges will still require shelf-life and rotation checks beyond what the image establishes.
5. Measure the purchase, rather than the output
Generating more adverts, finding more display faults and producing more reports can all demonstrate activity. Producers need to establish whether that activity saves costs or earns additional margin.
Creative teams could compare production time and cost with their usual process, while store trials record faults, correction times and sales afterwards. Similar locations running the same offer can help assess the effect of intervention, allowing for differences in demand, price and stock allocation. Include software and follow-up costs, and check whether extra sales simply moved from another pack in the producer’s range.
Make 2027 budgets pay for what worked
This year’s campaigns can provide a more useful basis for 2027 planning if producers connect the marketing records with what happened in stores. A heavily advertised product that repeatedly disappeared from the shelf needs a different response from one that was available throughout and attracted little interest.
Vlastos argues that discovering execution failures before the season ends gives brands a chance to recover business.
“It’s not especially helpful to learn after the holiday season that a majority of displays in a campaign weren’t compliant,” she says. “Learning that while the promotion is still running allows the team to flag the issue and resolve it in real time, with real results.”
Brand owners should demand evidence of those results before increasing spending. Dated photos and correction records can also strengthen retailer negotiations, particularly where promotional space repeatedly failed to deliver what was agreed.
The same discipline applies to AI-generated marketing. Faster production is valuable when it reduces costs or gives a team time to test stronger ideas; a larger pile of near-identical festive adverts offers little reason to increase the budget.
A producer shouldn’t have to wait until January to learn that the Christmas display never went up. AI can help expose that failure sooner, just as it can help prepare the campaign that sends shoppers looking for it. But producers need to judge the investment by whether it cuts campaign costs or helps sell more products profitably before the season ends.
