When building business forecasts and predictive models, there are factors that are within the control of the business itself, such as marketing, distribution, and promotions. There are also external factors that the business has no control over, like economic shifts and the weather. A simple question that is often asked is, how does the weather impact sales? The answer to this question is obviously dependent upon the industry, but often businesses learn that their sales are indeed impacted, at least to a degree, by changes in the weather, both positively and negatively. Therefore, it is important to understand the relationship of your business performance to changes in the weather so you can confidently and accurately take that into account in your forecasting efforts.
Weather data refers to weather-related data points like temperature, wind speed, and precipitation, including rainfall and snow cover.
Understanding and quantifying the effects of weather on sales can lead to several possible outcomes:
There is no shortage of publicly available sources that collect and gather historical weather data that can be used to monitor your business performance. One of the most common sources of weather data is The National Oceanic and Atmospheric Administration’s Climate Data Online tool, which provides free access to global historical weather and climate data, as well as station history information. Customers can order this data online.
Weather conditions are also consistently monitored and logged by weather stations at varying degrees of geographic and time granularity. Temperature, wind speed, precipitation and many other weather-related datapoints are available, as well as both historical and forecasted (short and long term) data depending on your specific needs.
The disadvantage of using these raw data sources is that they are typically more difficult to access and come in different formats. This can make it harder and more time-intensive to use for forecasting.
With a tool like Ready Signal, you can streamline this process by exporting or automating the most impactful weather control data with our on demand weather control data sets, then easily organizing and transforming that data within the tool. Ready Signal simplifies the data ingestion process with the ability to connect directly to Ready Signal via R, Python, Domo or the API client of your choice.
With a robust amount of numerical weather data available, there are endless possibilities of utilizing historical weather data to understand its relationship to your business performance.
It is important to keep in mind that the absolute measures of these data can be misleading without context, and it must be evaluated in relative terms based on what normal weather patterns are, and then identify variance from that norm. For example, if you are selling motorcycles, a temperature of 60 degrees in July in Texas may actually suppress sales; whereas that same 60 degree temperature in Michigan in April may cause a spike in sales. Context matters. Understanding these variances will help you identify and quantify the impact of extreme weather conditions.
It is also valuable to think about how to interpret the results of your analysis and utilize the data in a meaningful way. For example, if you learn that wind speed impacts your sales or service, It may be less important to quantify the impact of the change of any given increment or decrement in absolute speed, and more important to bucket it into groups of 1 mph, 2-5 mph, 5 – 10 mph, etc. This makes the predictions easier to interpret, and your business can more effectively respond when you expect the weather forecast to reach any of these given levels.
Once you have an understanding of how different kinds of weather systems and conditions may impact your business, you can make more informed business decisions. These decisions can enable better, smarter business forecasts, as well as allow you to proactively adjust to unexpected and changing weather information. For example, a large automotive aftermarket retailer wanted to predict their customer flow in order to better schedule their employees. Factoring in the weather as control data contributed significantly to the accuracy of their model and provided the ability to optimize their staffing to meet their expected customer demand.
Understanding how historical weather information has impacted your business improves your ability to develop informed business forecasts and proactively manage against upcoming and unexpected changes, as well as utilize marketing, pricing, and promotions to either take advantage of increased demand or minimize losses.