New - How A Dairy Company Leveled Up Their RGM Strategy With Up To 90% Forecasting Accuracy

Background

Pricing PPA | Food & Beverage | Global

This company is a global leader in food and beverages, offering dairy and plant-based products.

With over 90% of its sales in health-focused categories and products available in 120+ countries, it continues to expand its mission of promoting nutrition.

The company previously used simple price elasticities for their RGM forecasting, but this method lacked accuracy, as it didn’t account for all RGM levers, competitor actions, or market constraints.

With the rising cost of goods (COGS), they needed a faster, more accurate tool to model price and distribution changes, optimizing profitability while considering multiple variables and uncertainties.

Challenges

The company aimed to gain a holistic understanding of how they can mitigate increases in COGS with price and distribution changes.

Increase in COGS

Manufacturers faced an inflationary increase in the cost of goods sold (COGS). Leading to margin pressure and the need to increase prices across the portfolio.

Static price elasticities

Static price elasticity modeling with traditional, time-consuming
tools lead to uncertainty of impacts of planned price changes on unit sales, revenues, and profits.

Channel-level sales data impacting accuracy

When setting up the Buynomics tool, the manufacturer was concerned about the results’ accuracy because they only had channel-level sales data instead of SKU-level sales data.

Solution

When setting up the Buynomics tool, the manufacturer was concerned about the results’ accuracy because they only had channel-level sales data instead of SKU-level sales data.

Approach

After completing the 12-week onboarding period, the Buynomics platform was actively used to model and analyze price and distribution changes across different levels, including SKU, portfolio, and category.

Price elasticity accuracy served as a key
performance indicator, providing valuable insights into market responsiveness and the impact of pricing decisions.

Results

Price increases were modeled on several key products, and implementation impact in the market was monitored by comparing forecasted price elasticities:

Buynomics price elasticity: -0.5

Actual resulting price elasticity: -0.4

Exemplary for one product, same accuracy for all tested products.

Results

With the Buynomics tool, the team can now model price, price pack architecture, promotions, and distribution changes with up to 90% accuracy in minutes. This efficiency enables them to focus their time on making strategic RGM decisions.

>90%

Accuracy in forecasting
sales impact based on
price elasticity.

80%

Time saved on RGM
analytics in a dynamic
market environment.

How A Dairy Company Leveled Up Their RGM Strategy With Up To 90% Forecasting Accuracy

>90%

80%

Background

Challenges

Solution

Approach

Results

Results

>90%

80%

Make better RGM decisions, faster!

2-4%

95%

80%