Everyone knows that as a consumer, weather affects buying decisions. How likely am I to buy new sunglasses while it's raining outside? What are the odds that I invest in a new winter jacket on the hottest day of the season?
On a macro level, many advertisers understand this, too. That's why you see more advertisements for ice cream during the warmer months and why you see commercials for ski resorts in the dead of winter.
Of course, all of this makes plenty of sense: these kinds of advertising strategies have been in place ever since humans first started selling things to each other. But they are also "low-tech" in the truest sense of the word, often tied more to the calendar than they are to actual weather conditions.
We saw an example of the failure of this "low-tech" calendar-driven implementation this past holiday season. In 2015, we were met with the warmest December on record in the U.S., which didn't bode well for retailers looking to capitalize on strong Q4 cold-weather apparel sales. Winter items like sweaters or coats sat on store shelves and racks, despite the sales and promotions pushed to consumers. This past holiday season, it was estimated that the above-average temperatures resulted in a $343 million loss for mall-based clothing stores in the U.S., the largest such loss in nearly two decades.
While it may have been difficult to anticipate and avoid these inventory problems, retailers could have mitigated their losses if they used a more weather-driven advertising strategy. Indeed, smart marketers are already taking stock of the tools at their disposal, and programmatic technology provides media buyers with the capacity to make bid decisions based on an overwhelming buffet of data. Go on any DSP and you'll see multiple vendors offering targeting segments for specific demographics, niche interest areas, page-level context, selected keywords, and even predicted income level. But weather -- the one thing that's been persistently behind human decision-making for millennia -- is missing.
That's all changing. Marketers no longer have to time campaigns and creative messaging to align with the lowest common denominator of seasonal change. Rather, today's technology gives advertisers the ability to use real-time weather data to laser-target media buys towards users experiencing specific conditions. By leveraging solutions that map IP addresses to ZIP codes and connecting that data with information about temperature, precipitation, sun exposure, alerts, and more, advertisers can tap into an extremely influential factor behind what consumers choose to engage with in that particular moment.
Let's go back to the occurrence this past holiday season. While retailers couldn't have controlled the stock they already had in-store, they could have diverted spend from warm areas to cold areas, or even controlled the messaging conveyed to consumers based on the actual (not predicted) weather they were experiencing. For example, if snow was hitting a smaller geographical area than usual, media spend could have been concentrated in that region. In order to sell those snow boots in warmer areas, marketers could have implemented dynamic creative optimization (DCO) to swap out images showing their benefit in the rain instead.
For some brands, identifying the optimal conditions for promoting certain products is a no-brainer. For example, pharmaceuticals can deliver cold medicine ads to areas experiencing high flu activity. Or automotive companies can deliver snow tire ads to locations experiencing snow. For other brands, finding the right strategy might take a bit of research and trial and error. Beverage companies or beauty products companies might experiment with different temperature ranges and humidity levels and ultimately target the best performing segment. The travel industry might discover how different conditions like rain, freezing temperatures, or thunderstorms contribute to a person's likelihood to book a getaway. Either way, there is plenty of untapped opportunity to capture.
Despite the obvious potential, some advertisers are uneasy about buying this kind of data. For something as unpredictable as weather, incorporating this new facet to their strategy might make it more difficult to manage spend. But those fears are short-sighted. Not only can programmatic buyers employ multiple targeting techniques to minimize variability, but simply anti-targeting a set of outlier conditions can also be just as an effective strategy to avoid waste. (Regardless of the optimal weather to sell a set of lawn chairs, I'll bet you it's not while it's icy outside.) Furthermore, if weather is used to inform dynamic creative decisions rather than media targeting decisions, underspending is never an issue.
Whatever the strategy, one thing is clear: with weather affecting our behavior as much as it does, marketers who don't use it to their advantage are simply working harder, not smarter, and leaving money on the table.