The following was edited by Dylan Lowe from a presentation by the author
Weather may not be the topic you typically think of when you are trying to sell goods to retailers, but think again because more than 90% of annual weather-driven sales volatility originates from typical, everyday weather changes. This fact affects goods across a variety of product categories including:
- Safety (i.e. sunscreen, flooring, tire chains, etc.)
- Comfort (i.e. heaters, A/Cs, warm or light clothing, F&B, etc.)
- Activities (i.e. gardening, construction, outdoor cooking, etc.)
- Maintenance needs (i.e. vehicles, plants, home repairs)
- Basic necessities (water, batteries, shelter, emergency equipment, etc.)
You can also consider all the secondary categories that are affected by the above, which could be nearly any kind of home goods, clothing, F&B, or really any soft and hard goods. And this brings us to two kinds of weather information:
Raw Weather Data vs. Business Weather Intelligence.
Data and analytics are two very different tools and in trying to predict buying and demand patterns you need to analyze raw weather data under the microscope of the industry and regions it can and will affect. Helpful weather analytics is objective and puts weather's impact in a business context for effective analysis and planning. Good weather analytics combined with sales data will result in weather-driven demand modeling.
Here are some very recent regional examples of how different products have been affected by weather and the analytical results used in estimating weather-driven demand.
Integrating weather-driven demand into demand forecasts also enables companies to lower inventory carrying costs and increase working capital. Businesses can improve efficiencies in their demand planning function while ensuring a consistent and scalable approach when addressing weather-driven sales volatility. For instance, if you expect colder than usual weather for a sustained period, you know that you won't need to carry that line of swim shorts for some time, freeing up your inventory expenses.
Note of caution: This type of modeling needs to be done by forecasting future weather, not simply relying on last year's weather. We live in an ever changing global climate that brings us weather often not measured before, so planning your future inventory and sales based on last year's events could be a gigantic mistake.
Suppliers can see a revenue increase by showing their buyers what products their customers will demand based on the future weather outlook, utilizing weather metrics and analytics provided by companies like Planalytics. We saw this with a recent client who was a manufacturer of household goods and was planning their Spring business for a large national account. The client leveraged the Planalytics outlook to identify early season opportunities for their products in March. The client communicated with their retail partner to determine appropriate inventory quantities for the initial allocation to meet the expected increase in demand due to weather. The result of this action is the client maintained a 98.5% in-stock rate the entire month, received an incremental order from their retail partner, and achieved a double-digit increase in YOY sales during March.
For more information visit Planalytics.com or email the author directly at email@example.com.