How do weather and external signals improve fashion demand forecasting?
Unseasonal weather regularly moves fashion sales, and retailers often cite it in results. What weather, events and search data can add to AI demand planning, how much, and where the evidence is thin.
KEY TAKEAWAYS Summary by the editors
- Weather affects fashion demand mainly through timing: unseasonal temperatures delay or bring forward purchases of seasonal categories such as coats, boots and summer dresses.
- H&M said in September 2023 that unusually warm autumn weather delayed selling of heavier autumn products, and projected a 10% drop in September sales, according to Modern Retail.
- Weather is most useful in short-term decisions such as allocation, replenishment and markdown timing, where reliable forecasts exist, and least useful for buy decisions made months ahead.
- Research on French apparel sales measured weather effects as deviations from a 30-year climate normal, an approach that separates weather effects from underlying sales performance.
- External signals such as search trends improve forecasts modestly; one study of new fast-fashion products found Google Trends improved weighted error by 1.5%.
External signals improve fashion demand planning by explaining why sales deviate from the plan, and by adjusting short-term decisions such as allocation, replenishment and markdown timing. Weather is the most important of these signals for seasonal categories, because unseasonal temperatures shift when customers buy. The measured gains in forecast accuracy are usually modest, and the value depends on whether stock can still move when the signal arrives.
How does weather affect fashion sales?
Fashion is sold in seasons, but weather does not follow the retail calendar. A warm autumn delays coat and knitwear purchases; a cold, wet spring holds back sandals and dresses. Modern Retail reported in March 2024 that retailers increasingly cite weather in their results. Its examples include H&M, which in September 2023 blamed unseasonably warm autumn weather in the US and Europe for delayed selling and projected a 10% drop in September sales; Dr Martens, which pointed to warm US temperatures for weak boot sales; and JD Sports, which partly blamed mild conditions in a January 2024 profit warning.
H&M's chief executive at the time, Helena Helmersson, said that heavier autumn product types were where the delay in selling was clearest. Just Style reported that Next described variable sales growth in the third quarter of 2023, which it attributed to changing weather.
Is weather a real driver or an easy excuse?
Both. The same Modern Retail article notes that Inditex reported a 14% sales increase over the period in which H&M cited weather, and quotes a GlobalData analyst calling weather a very easy excuse to roll out and arguing that weather explanations must be specific. For planners the point is practical: weather explains part of the variance in sales, and quantifying that part separates genuine weather effects from assortment or pricing problems.
Academic work supports a measured approach. Bertrand and Brusset, in a 2014 paper on weather effects on apparel sales in France, used monthly sales data from the Institut Français de la Mode and a population-weighted national temperature index. They modelled sales anomalies against temperature anomalies, measured as deviations from a 30-year normal, to estimate the weather sensitivity of each product category, channel and gender and the periods of the year most exposed.
Which external signals are used in fashion demand planning?
| Signal | Typical use | Useful horizon | Main caveat |
|---|---|---|---|
| Weather forecasts | Allocation, replenishment, promotions | Days to a few weeks | Forecast skill falls quickly with lead time |
| Weather anomalies (historic) | Cleaning sales history, post-season analysis | Retrospective | Needs local, store-level weather data |
| Public holidays and events | Store traffic, occasion wear | Weeks to months | Event impact varies by location |
| Search and social trends | Trend direction for new products | Weeks to months | Noisy; measured gains are modest |
| Marketing calendar | Campaign uplift | Planned in advance | Internal data, often poorly recorded |
| Economic indicators | Category-level planning | Months | Weak link to individual products |
How much do external signals improve forecast accuracy?
Less than vendor marketing sometimes suggests, but enough to matter at scale. In research on new fast-fashion products using the VISUELLE dataset of 5,577 items, Skenderi and colleagues found that adding Google Trends series to a model built on images and product metadata improved weighted absolute percentage error by 1.5%. Weather typically adds most at short horizons and in weather-sensitive categories, and very little for products whose demand is driven by trend or marketing.
A further benefit is indirect. Removing weather effects from historical sales gives a cleaner picture of underlying demand, so next season's forecast is not distorted by last year's heatwave or wet summer.
Weather effects also vary by geography. A chain with stores across several climate zones may see a mild autumn in one region and an early cold snap in another, which is why national averages hide much of the signal. Planners who match weather to store locations, and who group stores into climate clusters, are better placed to see which regions are running ahead of or behind the seasonal plan and to move stock accordingly.
Where in the planning cycle does weather data help most?
- Allocation and replenishment: shifting seasonal stock between regions according to the short-term weather forecast.
- Markdown timing: delaying a planned discount on outerwear if a cold spell is forecast, or bringing it forward in a mild season.
- Store and online merchandising: featuring weather-appropriate products in windows, homepages and campaigns.
- History cleaning: adjusting past sales for weather anomalies before training forecasting models.
- Buy planning: limited direct use, since reliable weather forecasts do not reach months ahead; flexibility in the buy matters more.
What data and capabilities are needed?
- Store-level or postcode-level weather history matched to daily or weekly sales.
- A definition of normal weather per location, such as a long-term average, so anomalies can be calculated.
- Product tagging that identifies weather-sensitive categories.
- A forecasting model that can include external variables and report their measured effect.
- Operational processes that can act within the forecast horizon, such as weekly reallocation.
What are the risks and limits?
Weather data can create false confidence. Signals that correlated in one season may not hold in the next, and complex models with many external variables can overfit history. Each signal should therefore be tested on held-out seasons and kept only if it improves accuracy at the relevant decision level. Climate change adds another layer: as seasons shift, historical norms become a weaker guide, which argues for reviewing seasonal launch timing and flexible buying rather than relying on forecasts alone.
There is also a cost question. Detailed weather and trend data, and the modelling work to use them, are not free. A brand should estimate how much forecast error the signal removes in its weather-sensitive categories, what that error is worth in markdowns and lost sales, and compare that value with the cost of data and analysis. For some ranges the answer will be clearly positive; for others, simpler seasonal rules may be enough.
Frequently asked questions
Does weather affect clothing sales?
Yes, especially for seasonal categories such as outerwear, knitwear, boots and summer clothing. Unseasonal weather shifts when customers buy, which can delay or advance demand. Retailers including H&M and Dr Martens have cited weather in explaining weaker sales periods.
How is weather used in retail demand forecasting?
Weather forecasts are used for short-term decisions such as allocation, replenishment and promotion timing, where forecasts are most reliable, typically days rather than months ahead. Historical weather data is also used to remove weather effects from past sales so that underlying demand is clearer.
Can AI predict fashion trends from Google searches?
Search data can help indicate whether a style is rising or fading, but measured gains are modest. In one study of 5,577 new fast-fashion products, adding Google Trends improved weighted forecast error by 1.5%. It works best as a supplement to product and sales data.
Should fashion brands buy weather data?
Weather data is most valuable for brands with weather-sensitive categories and the operational flexibility to reallocate stock, adjust markdowns or change marketing quickly. Brands should test its effect on their own forecasts before committing to ongoing costs.
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SOURCES
- Modern Retail: Retailers are increasingly blaming bad weather for weak sales
- Just Style: Signal: unseasonable weather impacts European apparel brand results
- SciTePress: Weather Effect on Apparel Sales in France (Bertrand and Brusset, ICORES 2014)
- arXiv: Well Googled is Half Done, multimodal forecasting of new fashion product sales with image-based Google Trends (Skenderi et al.)
