How Brands Are Using Weather Data To Unleash The Power Of AI
Marketers get excited about data, artificial intelligence and the internet of things because of their combined power to potentially impact consumers’ everyday lives. Across the commerce landscape, the potential applications may be limitless: Farmers are now using satellite data to help increase crop yields and improve the quality of the food we eat. Shippers are deploying blockchain technology to modernize the supply chain and get products into stores more safely and quickly. Banks are relying on encrypted mainframe computers to help protect consumers’ personal data and prevent cybercrime.
One of the areas in which marketers have only just begun to tap the exponentially increasing unstructured data of the internet is the weather. Corporate America’s growing interest in weather data makes sense, given the near universal influence of environmental factors like weather on consumer purchase behavior. Fluctuations in weather can determine the frequency and timing of everything from doctor visits to shopping trips to attendance at entertainment and sporting events.
New and emerging technology platforms allow marketers to leverage weather data and connect with consumers in more targeted and relevant ways. For example, IBM Watson Advertising has informed its WEATHERfx platform with the Truven MarketScan database, which includes more than 250 million unique patients from across the health care spectrum, through a strategic partnership with Watson Health called WEATHERfx Health with Watson. A wide array of the platform’s triggers can help predict when certain weather patterns may exacerbate particular health conditions. By analyzing the two sets of data, WEATHERfx Health with Watson empowers brands to connect with consumers during these critical moments in order to drive both awareness and action.
Contextual targeting based on weather conditions can prompt an immediate response to messaging from a variety of health and wellness brands. Migraine sufferers, for instance, may experience the onset of symptoms in extremely dry or humid climates, or following the rapid drops in atmospheric pressure that come with stormy weather. By creating a look-alike model from the aggregate anonymized data, marketers have a better understanding of the circumstances that may affect the typical migraine sufferer and can serve up an ad when weather conditions may seem more likely to trigger an episode.
Retailers and manufacturers can employ preventative messaging in a host of different scenarios to drive traffic to stores—for example, recommending purchasing over-the-counter allergy medications when pollen counts are high. A national cough drop brand recently used WEATHERfx Health with Watson to target cold and flu sufferers in a digital ad campaign designed to raise awareness and consideration through highly relevant placements triggered on conditions that may aggravate coughing symptoms. The campaign outperformed the broader performance benchmarks for desktop and mobile by 520 percent and 160 percent, respectively, per Watson Advertising campaign results.
Make It Relevant and Personal
Increasingly, marketers seek to create relevance through personalization. The recent surge in adoption of ad blockers is clear evidence that consumers prefer a world with fewer ads, yet surveys have also shown that consumers appreciate ads that are tailored to their interests and shopping habits. In addition, personalized ads have the potential to lift sales and increase campaign ROI.
As with any personalized campaign, the challenge is to create highly targeted, relevant ads without becoming intrusive or annoying. This is particularly true in campaigns that leverage patient-level data. Targeting a group of consumers with a specific health condition adversely affected by weather is both an opportunity and a responsibility to conduct personalized marketing in a deliberate manner. A few guidelines:
Comply with patient privacy laws
In the case of WEATHERfx Health with Watson, ads are served based on the when and where that the weather signal provides, and not the who, which eliminates privacy concerns. The patient database is generalized but is able to give a better aggregate signal because of the larger sample size.