Employs historical weather data to verify claims at the time of insurance event. Management of existing risks via forecasts and current weather data.
In 2016 alone, windstorms cost the UK insurance industry approximately £1bn, mainly through large numbers of small insurance claims, with the average cost of around £700 per claim.
This is exactly the area where OpenWeather “History API 40 years by timestamp” can be particularly nesessary. First of all, it provides a cost-effective way for the insurance industry to gain instant access to accurate weather information. This information can be used to streamline the claims procedure giving increased customer satisfaction, reducing research costs and fraud, and eliminating potential mistakes. It also gives both a general overview of the prevailing weather conditions, along with full and specific details about temperature, pressure, wind speed and direction, rain and snow volumes etc.
From temperature-driven customer desires to delays in supply chains, weather impacts the retail industry in a significant way. That's why innovative retailers are moving to integrate weather analytics in all areas of their businesses, with the ultimate goals of maximising sales, predicting customer’s demand and refining operational efficiency.
Without a doubt, the most valuable use case for weather data in the retail industry is the ability to far more accurately forecast customer demands by taking into account expected weather conditions. Whether hot or cold, rainy or dry, sunny or overcast — customers may purchase lesser or greater quantities of certain products, on a seasonal or even day-to-day timeframe. The first step in generating these insights is to create a statistical model that shows how sales have been affected by meteorological conditions in the past. To this end, OpenWeather's Historical Weather API provides hourly weather readings for up to 40 years in the past for any location, which can be cross-referenced against sales records.
Weather-based sales models are particularly valuable across longer timeframes, especially seasonal ones. For example, as summer approaches, demand for warm clothing drops while demand for outdoor sporting equipment rises. By feeding data from OpenWeather's 30 Day Climate Forecast into sales models, retailers can better predict and prepare for changing demands, such as by placing orders with suppliers sooner or later.
Analytical sales models can also be used to forecast short-term demand, which may inform managerial decisions on a local level. For example, in anticipation of winter's first freeze, store managers may instruct employees to stock shelves with additional antifreeze and ice scrapers. Here, OpenWeather's Daily Weather Forecast can provide the weather data to accurately forecast demand based on sales models.
Sales prediction models in weather sensitive industries (food, beverage, entertainment). Complex analysis of weather impact on a particular business/project.
Short term forecasts to set up severe weather alerts for efficient route planning and minimizing risks of loss (delay/cargo damage)
The transportation industry, both in itself and as a part of logistics, is highly dependent on short-term weather events. Timely and accurate weather information for the coming period is important both from the point of view of driver safety and for calculating optimal routes, and even helps to save petrol.
Information about when rain will begin or end – and in which part of the city, or in which village – will help to build the most optimal route, increase safety and make the functioning of the whole system more efficient. Obviously, such information should be timely, accurate and easily accessible.
Enhance your short-term planning with Road Risk API by exploiting an accurate minute-by-minute forecast for 2 hours, hourly weather forecast and national alerts for five-days. The weather data is provided for any moment of time for 5 days ahead and 5 days back, covering any destination and any point along the route.
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