Does weather move the high street? What rain really does to footfall

The wet-weekend excuse is as old as retail itself. Sales were down because it rained. Footfall was off because of the storm. Nobody came because of the cold. These explanations are used so often, and so conveniently, that managers who hear them twice a month tend to stop believing them. The problem is that they are sometimes entirely true, and without measurement there is no way to tell.

What rain actually does to the high street

The effect of precipitation on open high streets is real and documented. Shoppers who would otherwise browse a town centre make a straightforward calculation: staying dry is more comfortable than going out. The more exposed the retail environment (open streets, open-air markets, uncovered car parks) the stronger the effect.

Footfall data from severe weather events shows just how sharp this can be.

According to Ipsos Retail Performance, Storm Ciara caused a 15.3% drop in footfall across UK retail during its peak weekend in February 2020.

Storm Dennis, the following weekend, pushed that further to a 16.3% decline. The month as a whole, one of the wettest February months on record, saw overall retail footfall fall 7.8% year-on-year according to data reported by Retail Gazette, with storms and sustained rainfall as the primary weather-related driver.

Those are not marginal effects. A 15% footfall drop over a weekend is the equivalent of losing most of Saturday’s trading, and for businesses with high fixed costs the revenue impact is severe.

The covered-destination switch

The footfall that disappears from the high street during rain does not all disappear from retail altogether. A proportion of it relocates. Covered shopping centres and retail parks, destinations where visitors can park undercover or move between stores without going outside, see a well-documented compensating effect during wet periods.

The RMS retail consulting blog, drawing on footfall and consumer behaviour data, notes that high street shops tend to suffer during extended wet weather while shopping centres benefit as shoppers seek shelter.

The implication for city centre operators and planners is that rain does not destroy aggregate retail demand, it redistributes it. That redistribution tends to favour investment in covered or enclosed environments, and it has long been an argument for canopied high streets, covered markets, and the kind of physical environment that keeps a destination usable in poor weather.

For outdoor DOOH advertisers, the same redistribution affects audience: the people outside in the rain are fewer, but they are often moving quickly and purposefully. Weather-indexed audience data matters here, reaching the right number of people at the right moment depends on knowing how many people are actually out there.

Temperature: the slow, consistent signal

Rain creates dramatic but relatively brief disruptions. Temperature is quieter but operates continuously and at scale.

Data from The Weather Channel, cited by retail consultant RMS, indicates that a seasonal temperature deviation of 1°C above or below average corresponds to approximately a 1% change in retail sales.

In a sector the size of UK retail, worth hundreds of billions of pounds annually, this equates to significant aggregate shifts in revenue.

The temperature effect plays out differently by category. Cold snaps slow footfall for fashion and home stores while boosting grocery and certain convenience categories. Warm periods pull people outdoors and into leisure, sometimes away from enclosed retail environments. An unseasonal warm October is bad news for anyone who bought heavily into autumn knitwear. A colder-than-average May strands summer clothing ranges before they have moved.

Unlike rain, temperature shifts are often predictable enough to plan around. Seasonal forecasting, factoring expected temperature deviations into buying and staffing decisions, is a well-established practice among larger retailers. The difficulty for most operators is that they lack the baseline footfall data to know how sensitive their specific location is to a temperature deviation of a given magnitude.

Why “it rained” is not a complete explanation

The weather effect is real. It is also the most convenient available excuse for almost any trading shortfall, and the two facts live in uncomfortable proximity. A manager who blames every quiet Saturday on the weather is not lying, there was probably some weather, but they may be missing a genuine performance problem hidden underneath the meteorological noise.

The only way to resolve this is a measured baseline that captures what footfall normally looks like for a given date in the retail year’s calendar, a given day in the weekly rhythm, and a given weather condition. Cities and municipalities running footfall programmes to understand the health of their town centres face exactly this problem: if footfall is down in March, is that the wet spring or is it structural decline?

Without a weather-adjusted baseline, the answer is a guess. With one, the data can begin to separate the two: a footfall shortfall on a wet day that matches the expected weather penalty is a weather event; a shortfall that persists on comparable fair-weather days is something that warrants a different response.

Building the adjusted baseline

The data output that makes weather normalisation possible is straightforward in principle: consistent, timestamped footfall counts logged over enough months to capture seasonal and weekly variation, combined with recorded weather conditions for each period. Over time, a model of expected footfall for any given condition emerges, and actual performance can be compared against it.

This is not a complex statistical exercise. It is what measurement makes possible that guesswork does not: a genuine answer to the question of whether the trading environment is changing beneath the weather noise.

For outdoor advertising and city-level decision-making, the practical applications are similar. Audience or footfall targets set without weather context will be systematically missed in poor months and easily beaten in good ones, neither outcome is very useful. Weather-adjusted baselines give planners and budget holders a consistent metric to assess against.

Separating signal from weather

Rain moves the high street. Cold temperatures move retail sales. Named storms move both, visibly and fast. “It rained” is the beginning of an analysis, not the end of one.

The useful question is: given that it rained, was the footfall hit larger or smaller than you would expect from a comparable event? Did the fair-weather days in the same week compensate? Was the performance of covered destinations up in proportion to the high-street loss?

None of those questions can be answered without a measured baseline, footfall data continuous enough and historic enough to tell you what normal looks like for this location, this time of year, this weather. That is what separates a weather report from genuine retail intelligence. And because it is the only footfall method in Europe approved by a data protection authority, that intelligence is collected without recording a single named person’s journey.