Sunshine or rain, balmy breezes or stormy winds, scorching heat or icy cold — weather affects people in remarkable ways. From mood to activity levels and even to shopping choices, weather is a compelling force. With the technology of a weather application programming interface — a weather API — businesses can examine data and predict customer behavior.
Applied statistics is the key to unlocking secrets of weather-related consumer purchasing patterns. Businesses rely on statisticians and other data scientists to utilize advanced quantitative methods in developing and implementing weather APIs.
How Does Weather Data Affect Business?
There are obvious ways that weather affects business, like consumers stocking up on milk and bread before a storm. Everyone knows that customers increase their shopping for summer clothes as the spring skies signal warmer days just ahead. And undoubtedly, residents of Pennsylvania buy more snow gear than people in Alabama.
Companies that can mine beneath these surface-level weather conclusions, though, have tremendous opportunity for sales growth and operating efficiencies. These are some of the ways weather data can be used to affect business outcomes:
- Inventory planning
- Targeted marketing
- Production strategy
- Staffing allocations
- Supply chain development
Businesses of all types and sizes are taking note of the value of weather data. Huge collaborative partnerships have been formed, and companies are allocating significant budgets to the data analytics of understanding how weather affects consumer behavior.
Weather Data and Applied Statistics
Collecting and analyzing weather data depends on the art and science of learning from data. With the world’s rapidly increasing quantity of data, there is vast potential for companies who grasp the value of this data. Using applied statistics, companies can uncover powerful competitive secrets from weather data.
A weather API is the key tool for turning data into helpful information. And it is the work of statisticians that makes this possible.
Using applied statistics, software developers and inhouse information technology departments develop complex models to analyze weather data. Some of the aspects a weather API considers are:
- Storms and other severe weather outbreaks
- Seasonal changes
- Air quality levels
- Temperature changes
- Rainfall measurements
- Unusual weather patterns
How Does an API Work?
An API, or application programming interface, creates a digital system to facilitate communication between different computer systems and applications. As software is designed using a specific framework of codes and tools, programmers may use a range of APIs.
Different types of APIs achieve varying purposes. The major types of APIs and their functions include:
- Remote procedure calls (RPCs) — facilitate access between programs
- Standard query language (SQL) — set up uniform paths to database access
- File transfer APIs — create channels for uploading and downloading files
- Message delivery APIs — support the transfer of messages
In today’s market, APIs are increasingly built into commonly used applications as embedded analytics, creating readily accessible business intelligence. As organizations sharpen their use of these tools, development has extended beyond software manufacturers to customized solutions. With decision-makers progressively relying on data, APIs are a critical component of creating interfaces for reports, dashboards and predictive models (source).
What Does a Weather API Do?
A weather API coordinates the systematic communication of weather data between computer applications used by meteorologists and data scientists. These APIs connect users to large databases to determine the most accurate weather information, including current forecasts.
Weather API providers distinguish themselves by factors like data quality, speed and ease of implementation. Hyperlocality is also a critical feature that allows users to fine-tune geographic output based on sophisticated models and algorithms. Weather APIs include information such as:
- Current weather details
- Daily forecasts
- Hourly forecasts
- Severe weather alerts
- Historical data
- Air quality measures
Programmers utilize the weather API to integrate information in software applications used by organizations. Companies using programs with weather API capabilities then have access to robust weather data. Strategic planners and decision makers can apply this information in powerful ways.
What Statistical Methods Are Used in Weather API?
Modern technology allows for the collection of huge volumes of weather data points. Using various methods of applied statistics, statisticians can convert data to useful information in understanding how weather affects consumer behavior. This section will examine some of the statistical tools employed in weather API.
Evaluating historical trends is often a trusted method for predicting future patterns. Using applied statistics, organizations can capture real data sets to extrapolate useful information for decision-making.
Tracking weather patterns over time equips statisticians to predict the future. While weather events never repeat exactly according to a pattern, some general cadences and elements are fairly consistent.
Companies like AerisWeather maintain large databases with tens of thousands of historical data points. Utilized in weather API, historical trends are examined to help users prepare for future outcomes (source).
Statisticians are experts in probability and statistical inference that affect forecasting. The science of applied statistics considers factors like probability laws and theorems, discrete and continuous random variables and joint distributions.
Predicting the weather is a highly sophisticated application of data science. Researchers use instruments like radar, satellites and barometers to collect weather data. A complex mix of conditions affect a weather forecast, including (source):
- Humidity levels
- Wind velocity
- Air pressure
Statisticians use distribution databases for replication of complex data to develop current forecasts. The Weather Company, an IBM business, has developed a cloud computing system that delivers four gigabytes of data each second, responding to 26 billion daily requests (source).
In studying available data, statisticians apply predictive modeling methods to systematically anticipate outcomes. Models may include linear and nonlinear regression models and classification models. Applied statistics includes constructing and evaluating predictive models.
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Weather API uses a method of applied statistics known as Numerical Weather Prediction. This mathematical method establishes equations and converts them to formats that computer applications can process (source).
In the United States, Numerical Weather Prediction begins with the federal Environmental Modeling Center, a component of the National Weather Service. Through a complex network of public and private partnerships, information is disseminated globally (source).
Visual mapping aids users in interpreting, applying and communicating information. Large volumes of data can be overwhelming, so visualization techniques simplify information by showing the geographic correlation of variables (source).
For weather data, maps are a critical component of clear communication. Geospatial visualization in weather is especially effective for implementing responses based on geography.
Statisticians use a variety of mapping tools for visualizing geographic weather data. Applications for weather may include different types of visualizations, such as heat maps for rainfall measurements. Developing these visualizations requires advanced algorithms and other data analytics techniques (source).
Maps of geospatial data may include weather reports such as:
- Temperature forecasts
- Dynamic radar
- Storm paths
- Precipitation records
How Is Weather API Used?
Computer programmers access weather data in a selected format with identified parameters and geographic specifications. The data is available through private suppliers and via public resources such as the National Weather Service (source). Though software developers are still the primary users of weather API, companies often create their own custom applications.
Weather API has become big business for private companies. Leading providers like AccuWeather have created ready-to-use products that offer applied statistics solutions for predicting sales based on weather patterns. Their resources are judged to be so valuable that AccuWeather now serves almost half of Fortune 500 companies and thousands of other businesses around the world (source).
Global weather radar taps into worldwide weather conditions and forecasts, including interactive maps. This is increasingly important for companies engaged in global commerce.
How Weather Data is Boosting Business
As technology has rapidly evolved, businesses are making innovative use of weather API. By interpreting weather data and applying the information they learn, companies are increasing sales and improving operating efficiencies. This generates higher profits and builds stronger, competitively-advantaged organizations.
In Peter Drucker’s exemplar business text, Management, his guiding principle is clear — the purpose of a business is to create a customer. External forces do not create businesses — people do — and understanding consumer behavior is critical to creating customers. Business leaders use the information they have to respond to external forces through innovation, creating customers.
These are some case study examples of how weather data affects marketing initiatives to create customers:
- Amusement Park Visitors: A warm day without rain or clouds prompts more online searches for information about amusement parks. A built-in weather API can facilitate automatic marketing promotions based on weather data (source).
- Coffee Drinkers: According to weather data, coffee purchases are affected by changes in weather. Using weather API, a company can identify weather-related purchase patterns by store and product. One company actually honed targeted initiatives with a $44 million increase in identified marketing potential (source).
- Hair Product Users: Through an innovative marketing campaign, Proctor & Gamble’s Pantene brand partnered with the Weather Channel for a “Haircast” campaign. When consumers checked the weather, Pantene targeted individual users with promotions for hair styling products based on weather conditions. Effective execution depended on hyperlocal data to deliver the most accurate weather implications (source).
- Allergy Sufferers: An estimated 88% of allergy flare-ups are associated with weather. Forward-thinking pharmaceutical suppliers, drugstores and other retailers can use weather data to predict when people will experience allergy symptoms. By proactively alerting consumers about weather patterns which may cause allergic reactions, these businesses can promote allergy medications that prevent symptoms (source).
Marketing initiatives only work if sufficient product supply is available to meet demand. If businesses overstock inventory, though, they risk financial loss.
By anticipating consumer needs related to changes in weather, companies can adjust production or procurement to fulfill expected demands. This requires the use of applied statistics to correlate weather data and inventory patterns.
Here are some examples of how companies benefit from weather data to optimize inventory levels:
- Surprising high-demand storm purchases. A correlation of weather data and inventory by a large retailer revealed unexpected purchase patterns at the threat of severe weather. With pending power outages, Pop-Tarts became a bestselling item. Inventory responses to this type of information can generate significant profit (source).
- Inverse effects of high temperatures. One study discovered that grocery store ice cream sales decreased when the weather exceeded 77°. Research analysts realized this counterintuitive finding was an indication that consumers were concerned the product would melt in higher temperatures. Using this kind of information, grocers can avoid sinking costs into excess inventory (source).
- Mircro-targeting for seasonal products. There are general weather patterns that affect inventory, such as stocking snow shovels in the winter in northern regions. Retailers who can implement hyperlocality through weather API can fine-tune their seasonal inventory strategies. Using weather data, they can be prepared with an adequate supply with precise timing in exactly the right locations (source).
- Just-in-time inventory. For businesses that want to maximize profit by keeping supply levels low, monitoring the weather can give significant insight. Restaurants, for example, could suffer serious loss from perishable food that is not used in time. Since people tend to dine at restaurants less during inclement weather, restaurants could adjust supply purchases by following weather forecasts (source).
By predicting the weather and related consumer behavior, businesses can run more efficiently, maximizing profitability and improving public perception. This may include factors such as production innovation, human resources planning and supply chain considerations.
These are some ways that effective organizations use weather data to implement appropriate operating efficiencies:
- Airport staffing: Weather API alerts airport leaders with the most accurate weather forecasts. This allows for advance staffing preparation for needs such as deicing planes, minimizing delays to facilitate quality customer service (source).
- Agricultural production: Farm industries, and even individual farmers, depend on precipitation forecasts for growing productive crops. Using weather data, they can anticipate dry periods and plan for needed irrigation processes.
- Energy distribution: Visual weather data identifies weather-related geographic risks and provides alerts to energy suppliers such as ONEOK. Using the most accurate weather information, utilities companies and suppliers are equipped to respond to disruptions caused by weather (source).
- Insurance risk mitigation: With the increasing sophistication of weather data analytics, insurers can more accurately predict the outcomes of weather. By alerting and educating customers about weather events, insurance companies can reduce claims while also enhancing customer relationships.
What Is the Job Outlook for Careers in Applied Statistics?
Data-driven business strategy is creating high demand for professionals who can make use of applied statistics in technology. A career in weather and business analytics is just one notable example of opportunities in the thriving field of statistics.
The range of job possibilities in statistics, along with impressive salaries, makes statistics one of the best career opportunities today. Statistician is ranked #1 in “Best Business Jobs” and #6 in “100 Best Jobs” by U.S. News & World Report (source).
According to the Bureau of Labor Statistics, for statisticians engaged in technology development, the outlook is especially promising. Demand for statisticians in computer systems design and related services is expected to increase by 49% from 2018 to 2028 (source).
How Do I Pursue a Career in Applied Statistics?
For aspiring statisticians, a graduate degree is the typical path to most jobs, including computer design such as weather API. A master’s in applied statistics prepares you with the quantitative skills critical for statisticians and the credentials employers are seeking (source).
A degree in applied statistics is also one pathway to a job as an atmospheric scientist such as a meteorologist. Writing and innovating weather forecasting computer programs is often part of the job for atmospheric scientists (source).
A master’s in applied statistics from Michigan Technological University is an accelerated, 100% online program. You can still work full-time while in school, making the program ideal for professionals.
Coursework in the applied statistics program will prepare you to analyze and interpret data in your particular area of interest, including weather data. The program covers a wide range of industry-leading statistical methods and tools, so graduates are equipped to:
- Analyze data
- Translate data into usable information
- Make predictions
- Solve real business problems
Where Can I Learn More about a Master’s in Applied Statistics?
Talk to an admissions counselor to see if the applied statistics program at Michigan Technological University is right for you.
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