Last year, one national plan met four different summers across the United States. What consumers were “supposed” to buy didn’t happen, leaving some retailers overstocked and others short as demand changed dramatically region by region. When a miss like that happens, what follows is expedited freight into the markets that ran hot, transfers out of the ones that stalled, and markdowns on what remained on the shelf.
As weather conditions diverged, so did household care categories. Pest control sales rose 11.0% in the Northeast, where warm overnight temperatures kept insects active, and fell 4.1% in the West, where dry heat had the opposite effect. Mold and mildew prevention climbed 47% in the Midwest from patchy precipitation and late-season warmth, and 31% in the Northeast following flooding. Across the West, persistent heat and drought pulled nearly every household care category down.
When averaged nationally, the losses in one region offset the gains in another, making overall performance look unremarkable, which is the problem. A product plan can be accurate in aggregate and wrong in the locations where inventory sits. What separated those four regions was weather. Because seasonal outlooks are usually available before buying decisions are made, they should be treated as an early demand signal while inventory plans can still be adjusted
Why this problem persists
None of those four regional outcomes were random. Each traces a specific condition with a recognized impact on the category. Knowing which region will get which weather pattern is the harder part, and a region's own history is a weaker guide than it used to be.
Most demand plans forecast regional demand from three inputs, each with their own blind spots.
The first is historical sales data, which reflect last year’s weather. Building on that baseline assumes a repeat of the least repeatable variable in the year, and 2025 was the perfect example. Four regions, four different weather events, four different category outcomes, and no single plan would have worked in any of them.
The second is the national number, and cold and flu categories demonstrate the impact on the bottom line. Peak season sales came in at $6.36 billion, down 3.6% against the prior year, which reads as a category in decline. Underneath it, upper respiratory grew in the Mountain West, Texas, the Gulf Coast, the Carolinas and Florida. A plan built on the national figure cuts inventory across the board, shorting the regions that really need it.
The third is broad seasonal assumptions based on an unreliable calendar. NOAA's Climate at a Glance shows the South's October through March average temperature on a 1.0°F per decade climb since 2000. Warmer winters push the onset of illness later and soften the peak, making a traditional winter demand curve a less reliable planning input each year. Population is heading in the same direction. The South is growing fastest, so a timing error becomes quite costly when inventory arrives too early, too late, or in the wrong markets.
Every decision has its own forecast
Climate trends look ahead by decades, months or weeks, and each range suits a different decision. A two-week forecast can't change what a plant already produced, and a decades-long warming trend won't tell a distributor what to ship next Tuesday. It’s important to match the signal to a decision that can still be changed.
Long-range weather patterns, those analyzed over decades, should be used for decisions revisited every few years, like where facilities are located and which regions hold seasonal inventory. Most networks tier their distribution centers by volume, which can put Phoenix and Boston in the same group, using the same seasonal plan: a costly mistake for two areas with distinctly different weather.
Seasonal outlooks running from 30 to 90 days can inform regional allocation, initial buy quantities, pre-positioning, and safety stock before a season opens. These signals are important for manufacturers, since production and inbound transportation are committed early, and changing them once set is costly.
Short range forecasts, two to six weeks out, are for deployment. Cold and flu season is a good example. Last December, sales across the eight highest-density markets climbed 15.8% over three weeks, compared with 10.5% in rural markets. Those metros serve as an early indicator for the rest of the country while there’s still time to reposition product.
What to Change Now
Build regional forecasts rather than disaggregating a national one. Summer 2025 showed the cost of allocating a national total by historical share; it reproduces last year's weather in this year's plan.
Apply the seasonal outlook to categories where weather sensitivity is proven. Most won't require an adjustment, but the handful of categories that do will move hard enough to justify a different planning approach.
Set thresholds that trigger a deployment change when predefined conditions are met. A category that peaks over three weeks can lose a third of its season waiting for a weekly review to catch up.
Evaluate the post-season variance against weather, and the miss becomes something a planner can act on next year.
Retailers and manufacturers don't need to hire a meteorologist, but they do need to treat weather as a measurable demand input with a defined horizon, and add it to their plans while there's still time to act. The information is public, costs nothing, and is published before the purchase order.
Darren Seifer is Vice President of Thought Leadership for Home, Family & Baby at NielsenIQ.
