Situation
A U.S.-based food manufacturer faced challenges with pricing analysis, a critical lever for managing demand during promotions and regular sales periods. Previously reliant on third-party vendors, the client sought to build an in-house capability to enhance accuracy, scalability, and cost efficiency.
Challenge
The transition to an internal pricing analysis approach posed several hurdles:
- High external costs: Vendor dependency drained resources without delivering tailored insights.
- Limited demand forecasting: Lack of precise models to predict customer behavior under varying price scenarios.
- Segment-specific challenges: Diverse consumer responses to pricing changes were not well understood, hindering targeted strategies.
Approach
Mu Sigma partnered with the client to establish a robust in-house pricing analysis framework:
- Conducted a comprehensive study of factors influencing pricing and demand, including socio-economic drivers.
- Developed price elasticity models to quantify customer responses to price fluctuations across products and segments.
- Identified critical correlations between socio-economic status and price sensitivity, enabling segmentation-based insights.
- Enhanced demand forecasting accuracy by analyzing product-category level price sensitivity and crafting predictive models.
The insights enabled the client to design and execute strategic trade promotions and establish a sustainable, scalable pricing analysis capability.
Impact
- 8% increase in sales, driven by more effective trade promotions.
- Improved demand forecasting, enabling data-driven decision-making.
- Reduced costs by eliminating reliance on third-party vendors and fostering self-sufficiency.
Business Impact
-
8%
increase in revenue
-
Increase forecasting accuracy
Let’s move from data to decisions together. Talk to us.
The firm's name is derived from the statistical terms "Mu" and "Sigma," which symbolize a
probability distribution's mean and standard deviation, respectively.
