CPG | Sales Planning
Market Estimation and Budget Planning
Objective
To optimize sales planning and budget allocation at the product level within the CPG industry by leveraging AI/ML, BI, and engineering solutions to forecast demand accurately and align resources effectively.
Challenges
- High variability in demand across different regions and products.
- Complexity in integrating multiple data sources such as macroeconomic data, inventory data, and partner data.
- Inefficiencies due to manual and heuristic-driven forecasting approaches.
Solution Proposed
- To develop a robust forecasting framework, utilize advanced algorithms such as Support Vector Regression, linear regression, Lasso, Ridge Regression, and GLM/GAM.
- Implement an automated process for generating reports and dashboards using tools like Shiny by RStudio and Microsoft SQL Server.
- Develop an interactive what-if analyzer to simulate various scenarios and assess the impact of changing key drivers.
- Leverage external data sources, such as weather trends, social media sentiments, and marketing data to enhance prediction accuracy.
Outcome
- Model accuracy of 99% enabled confident business decisions.
- Reduction in man hours spent on manual forecasting.
- Improved and optimal forecasts provided by sophisticated ML algorithms.
- Optimal quota setting for each partner/product, leading to better sales rep incentivization.
- Better sales result from the alignment of national goals with territory goals.
Design & Thinking Wins
- Identified key drivers of sales across numerous metrics.
- Developed performance reports for tracking model health.
- Improved workforce motivation resulting from optimal workload distribution.
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Back to CPGDisclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
