wetter.com
Case Study
From Weather Analysis to Media Optimization.
Are we just as happy to shop on sunny days as on rainy days? Which weather variables are the correct ones to differentiate between good and bad weather?
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Motivation
Does weather have an impact on retail sales?
Can the placement of advertisement be optimized weather-based?
Method / Model
Automated download of temporal and spatial resolution of weather information throughout Germany.
Methodical selection of relevant weather variables with an impact on audience ratings.
Consideration of geographical, socio-economic, cyclic effects, and seasonal shifts in order to explain ratings and sales as precisely as possible.
Clustering and quantification of weather impact.
Result
Not the absolute weather but the relative changes and/or deviations from the expected condition are driving factors.
Up to 30% sales-uplift can be achieved when advertisement budgets are geographically redistributed using weather optimization.
Are we just as happy to shop on sunny days as on rainy days? Which weather variables are the correct ones to differentiate between good and bad weather? How can we use the knowledge of the current weather and the weather forecast to optimize the placement of advertisement?
Typically – just as in our weather project – the first challenges for analysts are the availability of data, the elimination of data errors, the merging of data of different origin, and the correct aggregation of relevant information. Our first goal was to reduce the variety of quantitative information about the historic, current, and forecast weather that was provided to us by Wetter.com to the most essential parameters. Thereafter we included geographical, socio-economical, and periodical data in our analysis in order to be able to explain the sales of our client’s product – which can be purchased online exclusively – in the best way possible. The result is a spatiotemporal, explicit model with high resolution which is easily scalable and can be transferred to many other products at a low effort.
The resulting regression model has very high explanatory power (with a coefficient of determination of 0.98) and therefore is suitable to explain the various influences on the purchase decision of certain products. Furthermore we discovered that the spurring factors aren’t the absolute weather, but the relative changes, or rather the deviation from the expected values.
The essential advantage of our model is a precise basis for decision-making for a weather-based targeting of advertisement. Sales can be increased by weather-dependent placement of advertisement and at the same time spendings for advertisement can be used more efficiently, resulting in increased gains.
