The start of July in North America marks an annual boost in demand for advertisers selling summer apparel, consumables, and outdoor living products. The combination of warm weather, Independence Day, and Canada Day all coincide to make the first week of July a busy one for businesses and advertisers in this market segment.
Of course, the demand for these products is heavily influenced by regional weather patterns across the continent, as shown by data produced by Planalytics. This year, for example, we’re seeing unprecedented heatwaves in the North West, with comparatively cooler temperatures in the central and eastern parts of the country. This, undoubtedly, has advertisers scratching their heads about how to allocate their spend, and which products to push on a regional basis.
This exact situation is where combining weather analytics with historical purchase data and contextual categories can give advertisers a competitive advantage.
What is weather analytics?
As any advertiser knows, weather can influence consumer purchasing on a day-to-day basis. But, activating ads based on daily weather patterns alone is often a futile cause that can produce inconsistent and sub-par results.
That’s because weather triggers do not consider market-specific purchasing characteristics or regional variances. Instead, they trigger blanket responses to weather, neglecting the nuanced responses that consumers will have to that weather in each targeted region.
Instead of blanket weather triggers, advertisers should be leveraging historical weather analytics as part of their targeting strategy.
Weather analytics, like that offered through our partner Planalytics, take historical weather metrics AND associated consumer behaviors into account. This helps to create a clear, data-driven connection between weather and purchasing behaviors, taking into account all unique interactions that occur across regions and time periods for a specific product.
Layering weather analytics and historical purchase data
Weather analytics is made even more powerful for advertisers when you layer on historical purchase data and contextual categories like those offered in Peer39’s Contextual Data Marketplace.
Layering weather analytics with historical purchase data allows you to take that historical weather data and leverage insights into the optimal in-product categories to target. In other words,you are now able to leverage weather data AND past product purchase data by region to predict how specific market segments will purchase during existing weather events.
This, of course offers advertisers a much deeper knowledge about how weather impacts purchasing behavior in their target regions, and how they can adapt their advertising strategy to account for those nuances.
For example, advertisers can identify the optimal times to advertise to regional audiences, and gain insights into what type of messaging and ad placements will resonate with them the most.
So, if you’re struggling to adapt your advertising strategy this July due to the unprecedented weather patterns we’re seeing across North America, combining weather analytics with contextual categories will help steer you in the right direction.
