Weather data plays an important role in our everyday lives. Understanding this data is particularly important for businesses because weather conditions and predictions affect consumers’ behaviors and needs, impacting consumer foot traffic to commercial places of interest. From weather reports on the morning news to push notifications from weather apps, weather data serves an important purpose for consumers, allowing them to accommodate their plans depending on weather conditions. Many consumers’ travel patterns and general foot traffic trends are affected by the weather every day, so how can weather data and location intelligence be combined to provide beneficial consumer insights for businesses?

Weather data insights already play an important role for businesses, providing an opportunity to anticipate consumer behavior depending on precipitation levels, temperature, and more. However, weather data can become even more valuable for businesses when combined with location intelligence. By pairing weather data with location analytics and analyzing these datasets, companies can better understand how local weather patterns impact consumer visits.

In-person consumer visits to a commercial place of interest can be examined for a variety of insights, such as frequency of visits, distance traveled to make the visit, and dwell times. Analyzing dwell times using weather data and location analytics can lead to powerful insights. Inclement weather can delay consumers’ plans, causing longer dwell times, or wait times, at a location. Understanding these dwell times can help businesses, governments, and other organizations better understand consumers’ challenges and plan for improved efficiency.

The travel and hospitality industry is especially vulnerable to fluctuations in the weather, with consumer dwell times playing an important role for analysts. Travel and hospitality is a $1.9 trillion industry, so it is imperative for analysts and researchers in the industry to understand the delays and challenges that inclement weather may bring and continuously improve business efficiency during inclement weather.

To learn more about how weather data and location intelligence can work together to help solve problems within the travel and hospitality industry, we analyzed consumer dwell times at Denver International Airport during a major weather event. The weather data we looked at helped us understand the details of this weather event, and our high quality location analytics helped us understand the effect of this weather on consumers. We captured dwell times at the airport from the time mobile location data signals were observed at the airport to the time consumers left to fly to their next destinations. Here’s what we found when examining these datasets.

Let’s look at an example of how this kind of analysis can provide insight into consumer behaviors in the travel industry. We at Gravy Analytics used our Visitations data and data from the National Oceanic and Atmospheric Association (NOAA) to determine how weather changes impacted the number and length of visits at different airports in 2021. Visitations are generated when a mobile location signal is observed within the perimeter of a commercial place of interest, such as an airport’s area perimeter.

The NOAA data we examined showed that a significant amount of precipitation was recorded at Denver International Airport on March 14, 2021. We found that the local news later reported that Denver had received over 27 inches of snow on this day. Now, how did this substantial snowstorm affect visitor patterns to Denver’s major travel hub?

By looking at Gravy Visitations data for Denver International Airport on this date, we saw a large spike in the amount of time that travelers spent at the airport. During the major snowstorm, consumer dwell times jumped from an average of 60-90 minutes to almost 6 hours. In this instance, there was a clear correlation between the major weather event and its impact on consumer travel.

A more in-depth analysis using location intelligence and weather data could further reveal recurring data patterns. For example, if we were to analyze datasets for the Denver International Airport for 2023, we might see a continued pattern of longer dwell times during extreme weather events and shorter dwell times during mild weather. This type of analysis can be utilized to benefit businesses of all types. These datasets can be fed into impact models and used with artificial intelligence to predict how the weather will affect business and operations in many industries.

By looking at Gravy Visitations data for Denver International Airport on this date, we can see a large spike in the amount of time that travelers spent at the airport. Dwell times jumped from an average of 60-90 minutes to almost 6 hours on the same day as the snowstorm. In this instance, there is a clear correlation between this major weather event and its impact on consumer travel.

A more in-depth analysis using location intelligence and weather data could reveal recurring data patterns. For example, if we analyze the rest of 2021 foot traffic data for Denver International Airport, we could observe a pattern of longer dwell times during extreme weather events and shorter dwell times when those events do not occur. Many technology companies today are feeding these data sets into impact models and using artificial intelligence (AI) to predict how weather will affect future business levels and operations in many industries.  

By combining these two datasets, analysts in the travel industry can gain insight into how weather events impact human behavior to inform operations and increase efficiency. But there are plenty of other use cases for this type of data analysis for businesses. Here are some additional use cases of how this type of analysis can help businesses in different industries:

• Retail brands can combine weather data, location analytics data, and other consumer data like purchase data to predict in-store foot traffic levels and inform business strategy based on weather forecasts (e.g., stocking umbrellas ahead of rainy days).
• Restaurants can use this data to adjust staffing levels and hours of operation before a weather event hits, ensuring efficiency and reducing wasted resources like labor costs.
• Emergency managers can effectively guide or evacuate local populations in extreme weather situations. Read our case study to learn more about how researchers from the University of Florida Transportation Institute used location intelligence to inform emergency planning and management.
• Sports and entertainment companies can pivot marketing efforts and fan outreach ahead of impactful weather events to improve communication and plan for any potential challenges with outdoor in-person events.

So, while dwell times in the travel and hospitality industry is a great example of enriching weather data insights with location intelligence, other businesses in various industries can benefit from using combination datasets like this.

For more insight on Gravy’s DaaS offerings and how they can be used by businesses in various industries, click here. To receive more consumer foot traffic analyses like this in your inbox,  subscribe to our email newsletter today.