Our client, a global consumer technology company, was looking to streamline and expedite their sales forecasting process. By adopting a statistical modelling approach, utilising Python, the Ipsos team created a solution which reduced the sales forecasting process from 2 weeks to a matter of hours; increasing efficiency and aiding enhanced decision-making.
The client, a global consumer technology company, was faced with a significant challenge concerning the processing of EMEA sales data to aid forecasting. Their existing solution, involving consolidating vast amounts of disparate sales data, typically took up to 2 weeks to turnaround and exposed them to the risk of inaccuracy due to human error. They needed a solution which would help them to streamline their existing process whilst still providing insights which would guide forecasting.
Using Python, we created bespoke scripts which automated the creation of the forecasting template. This involved using various Python capabilities and libraries, including Pandas and OpenPyxl. We were also able to harness Python’s statistical modelling features, providing the client with access to improved predictive functionalities and statistical forecasting capabilities.
As a result, we were able to reduce the clients sales forecasting process from 2 weeks to a matter of hours. Alongside this, our solution enabled them to benefit from more granular insights. We increased the sales team’s efficiency, reduced the risk of human errors and provided a solution which could adapt to a dynamic and ever-changing market.
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