An ICSC survey of roughly 1,000 shoppers, published on Oct. 9, found that 49% are worried about affording holiday gifts this year and about half say personal or household debt will weigh on their budgets, even though retail sales are projected to grow 4.3% to 4.9% and top USD 1.7 trillion. The split matters for omnichannel operators because it points to a season in which footfall holds up while basket economics tighten. Nearly eight in 10 shoppers expect to spend the same or more than last year, so the pressure is concentrated in discretionary lines rather than in traffic volume.
Key Conclusions
The practical consequence is that demand is not disappearing, it is being redistributed. Households that feel stretched do not stop shopping; they trade down, split baskets across formats and delay discretionary purchases until a promotion lands. For retailers running both stores and digital channels, that behaviour shows up first in average basket value and promotional dependency rather than in visit counts. Teams that only watch traffic will read the season as healthy right up to the point where margin compression becomes visible in the P&L, which is typically several weeks after the behaviour has already changed.
A second consequence is that fulfilment capacity has to be planned against basket size, not order volume. Smaller, more frequent orders raise picking and delivery cost per unit even when total revenue is flat, and they concentrate labour demand into short promotional windows. The affordability signal in the ICSC data therefore is not only a merchandising problem. It is an operations problem that requires forecast granularity at the category and channel level, because the same headline growth rate can sit on top of very different unit economics depending on how customers choose to shop.
How The Affordability Squeeze Shows Up In Basket Data
Basket data is the earliest reliable read on affordability stress. The first signal is a widening gap between units per transaction and revenue per transaction, which indicates that customers are buying similar volumes at lower price points. The second is a rising share of promotional lines inside the basket, which suggests that full-price demand is weakening even when total spend is stable. The third is a shortening of the consideration window, visible as faster conversion during promotional events and slower conversion outside them. None of these signals appears in headline sales figures until much later.
Splitting Baskets Across Formats
Stretched households also split their shopping across formats in ways that are easy to miss. A customer may buy staples in a discount format, discretionary items online during a promotion, and top-up items at a convenience store close to home. Each of those transactions looks unremarkable in isolation, but together they mean the retailer loses the basket consolidation that used to fund free delivery and loyalty rewards. Tracking cross-format behaviour by household rather than by transaction is the only way to see the pattern early enough to respond, and it requires identity resolution across channels that many retailers still treat as a data project rather than a commercial necessity.
The Loyalty Cost Of Smaller Baskets
Smaller baskets carry a hidden loyalty cost. Loyalty programmes are usually funded by the margin that consolidated baskets generate, so when customers split their spend the programme's economics deteriorate before the member count does. Retailers that measure programme health only by enrolment and active members will therefore see a stable picture while the underlying contribution per member falls. Adding contribution per active member, and the share of that member's category spend captured by the retailer, gives a far more sensitive read on whether the programme is still earning its keep during an affordability squeeze.
Best Practices
The first practice is to rebuild the demand forecast around basket composition rather than around revenue targets. Forecasts should be produced at the level of category, channel and price band, with explicit assumptions about trade-down rates and promotional depth. That level of granularity lets merchandising and operations plan against the same view of the season, instead of merchandising planning for full-price volume while operations staffs for a promotional surge. It also makes it possible to test trade-down assumptions against actual weekly performance and adjust before the peak weeks arrive.
Forecasting Trade-Down Explicitly
The second practice is to treat labour planning as a function of picking complexity rather than order count. When average baskets shrink, the number of picks per hour falls, so the same order volume needs more labour hours than the previous season's model assumed. Retailers can capture this by adding a units-per-order variable to their labour standards and re-running the model weekly. Doing so avoids the common failure mode of a peak week in which the labour plan looks fully staffed on paper while pick lines are visibly behind, because the plan was built on last year's basket size.
Making Promotional Depth Auditable
The third practice is to make promotional depth auditable in advance. Each promotion should carry a stated expectation for incremental units, margin and the cannibalisation it will cause on adjacent full-price lines. When those expectations are recorded before the event rather than reconstructed afterwards, teams can compare promotions against each other and retire the ones that only shift volume from one week to the next. Levi Strauss & Co. reported net revenues up 4% to USD 1.6 billion in its most recent quarter, with wholesale up 6% while direct-to-consumer rose just 2%, a reminder that channel mix and margin, not headline growth, determine whether a season is actually profitable.
Common Mistakes
The most common mistake is to read resilient headline sales as proof that customers are comfortable. Growth of 4.3% to 4.9% in a season where half of shoppers say debt will constrain their budgets is a price-and-mix outcome, not a confidence signal. Teams that plan inventory depth, promotional calendar and service levels on the assumption of healthy discretionary demand tend to discover the mismatch in the final two weeks, when there is no time left to change assortment or reallocate labour between channels.
A second mistake is to treat online and store performance as separate scorecards. When customers split baskets across formats, a channel-level view can show both channels performing acceptably while the combined household relationship deteriorates. New York Fed research published on Oct. 12 estimated that tariffs pushed consumer goods prices up by 2.9 percentage points, and price pressure of that scale accelerates format switching. Retailers that reconcile performance at the customer level, rather than only at the channel level, will detect the shift earlier and can respond with targeted offers instead of broad discounting.
What Store Teams Should Instrument First
Store teams should start with four measures that are cheap to capture and highly diagnostic. The first is units per transaction alongside revenue per transaction, reviewed weekly by store and category. The second is the share of transactions that include at least one promotional line. The third is substitution behaviour, captured when a customer accepts a cheaper alternative at the shelf or at the point of order. The fourth is the abandonment rate on digital orders, which rises when delivery fees start to outweigh the value of a small basket.
These four measures share a useful property: they change before financial results do. A store that reports stable revenue but rising promotional penetration and falling units per transaction is sending an early warning that its customer base is under pressure. Acting on that warning may mean adjusting pack sizes, introducing entry price points, or shifting delivery incentives toward basket thresholds. Retailers that instrument the behaviour rather than the outcome gain roughly a month of lead time, which in a promotional-heavy season is often the difference between protecting margin and absorbing it.
Summary
The ICSC survey describes a holiday season in which traffic and total spend hold up while household budgets tighten. For omnichannel retailers the operating question is therefore not whether demand exists but how it is being redistributed across formats, price points and promotional windows. Basket composition, promotional penetration and units per transaction are the measures that reveal that redistribution first, and they are also the measures that most forecasting and labour models currently ignore.
Retailers that rebuild forecasts around basket composition, plan labour against picking complexity, and reconcile performance at the customer level rather than the channel level will be better positioned to protect margin through the season. Those that continue to plan against headline growth risk discovering the mismatch too late to change anything, which is the same pattern that has played out in previous periods of price pressure when demand stayed visible but became far less profitable.
Data Sources
- Retail Dive: Retail Dive
- Retail Dive: Retail Dive
- Retail Dive: Retail Dive
- Retail Dive: Retail Dive
- Retail Dive: Retail Dive
- Retail Dive: Retail Dive
FAQ
Why does basket composition matter more than traffic this season?
A: Because the ICSC survey shows 49% of shoppers worried about affording gifts while total retail sales are still forecast to grow 4.3% to 4.9%. Demand is being redistributed across price points and formats rather than disappearing, so composition reveals stress before headline figures do.
Which metric gives the earliest warning of trade-down?
A: A widening gap between units per transaction and revenue per transaction. It indicates customers are buying similar volumes at lower price points, and it typically moves weeks before margin compression becomes visible in reported results.
How should labour planning change when baskets shrink?
A: Labour standards should be rebuilt around units per order rather than order count. Smaller baskets reduce picks per hour, so the same order volume requires more labour hours than the previous season's model assumed.
Why reconcile performance at the customer level?
A: Because customers split baskets across formats, and channel-level scorecards can show both channels performing acceptably while the combined household relationship deteriorates. Identity resolution across channels is required to see that pattern early.
What does the tariff research add to this picture?
A: New York Fed research published on Oct. 12 estimated tariffs pushed consumer goods prices up by 2.9 percentage points, which accelerates format switching and makes affordability pressure more visible in basket behaviour.
How can promotional depth be kept under control?
A: Each promotion should carry a stated expectation for incremental units, margin and cannibalisation before it runs, so teams can compare promotions against each other and retire those that merely shift volume between weeks.
References
- Retail Dive：Retail Dive
- Retail Dive：Retail Dive
- Retail Dive：Retail Dive
- Retail Dive：Retail Dive
- Retail Dive：Retail Dive
- Retail Dive：Retail Dive
