How a Beverage Manufacturer Identified New Portfolio Optimization Opportunities

Pricing PPA  |      Food & Beverage   |    Global

This global food and beverage manufacturer has a diversified portfolio spanning dairy, plant-based, and waters. The business operates across multiple regions and channels, including modern trade, convenience, e-commerce, and out-of-home.

Within its waters division, the team set out to optimize its large-format still-water lineup to better meet emerging consumer needs.

Historically, the manufacturer sold an 8L jug that was discontinued for operational reasons, while a 6.25L SKU remained. The team wanted to evaluate whether reintroducing the 8L and/or introducing a 5L alternative would drive incremental volume and revenue while keeping complexity and cost manageable.

Before adopting Buynomics software, the manufacturer lacked a reliable way to evaluate pack and price interactions across the portfolio or the impact of introducing new products.

With Buynomics’ Virtual Shoppers AI, an agent- based simulation model, the manufacturer was able to model shopper behavior with up to 95% accuracy. This allowed them to uncover white- space opportunities, test new product introductions, and maintain price ladder integrity.

The Virtual Shoppers AI estimated willingness to pay based on product attributes (value drivers), providing a more precise foundation for portfolio and pricing decisions.

To address the challenges, the manufacturer used Buynomics’ software to get answers to their key business questions.

• Should we replace the 6.25L with a 5L?
• Should we introduce the 8L jug?
  
  

Step 01

Price changes: Modeled different portfolio prices to keep a price ladder structure.

Step 02

New product introduction: Reintroduced the 8L jug based on historical distribution patterns and relevant value drivers.

Step 03

Product architecture adjustments: Replaced the 6.25L bottle with a 5L bottle, while keeping the 8L bottle in the portfolio

Step 04

Price changes: Modeled different portfolio prices to keep a price ladder structure.

01. Should we introduce the 8L jug?

*Average prices are shown because pricing varied slightly by channel.

** Versus the original case.

02. New product introduction with architecture swap

*Average prices are shown because pricing varied slightly by channel.

** Versus the original case.

03. New product introduction with architecture swap and adjusted pricing

*Average prices are shown because pricing varied slightly by channel.

** Versus the original case.

*When back tested against 2021 sales of the same product planned for reintroduction.

** Versus the original case.

By leveraging Buynomics’ Virtual Shoppers AI, the RGM team successfully evaluated multiple product and pricing scenarios with a high level of accuracy and confidence. The analysis identified the 8L jug introduction, paired with no changes to the existing range, as the optimal strategy, offering the lowest innovation cost and a projected annual revenue uplift of approximately 4%. With 94% prediction accuracy based on historical performance, this data-driven approach enabled the team to move forward with a scenario that balances profitability, risk, and operational simplicity.

This project demonstrates the value of advanced simulation tools in guiding strategic pricing and portfolio decisions, ensuring that innovation investments translate into measurable commercial impact.