Efficiency / US / Retail & Consumer Goods
Optimising selling cycles in the CPG industry using predictive analytics
"The enriched datasets helped the client to optimise selling cycles for certain types of products as a result of identifying patterns based on third-party weather data."
Business issue
A client in the Consumer Packaged Goods industry wanted to understand what additional insights, beyond those captured in survey, might impact product performance perception. The client commissioned Ipsos to integrate environmental data with the survey data for further their predictive analytics capabilities.
Our solution
As part of our solution, Ipsos integrated numerous datasets at scale for the U.S on weather attributes, such as precipitation, humidity, temperature, and cloud cover. We provided daily values for each at a granular level, and then connected this data with the product testing survey results.
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Impact
The enriched datasets helped the client to optimise selling cycles for certain types of products based on weather patterns, and enabled them tm answer questions, such as:
- For hair care: might high humidity impact attitudes on whether they counteract frizz?
- For fertilizer: might less-than-normal precipitation or high cloud cover (impacting agricultural growth potential) lessen customer satisfaction?
- For bug killer: might higher levels of precipitation fostering mosquitos result in perceived poor performance?
Weather data allows for calibration of survey – (dismissing product performance during certain types of weather) – and can help client optimise selling cycles for certain types of products based on weather patterns.
