Getting the Pricing Right in Food & Beverage

Situation:

A snack brand was under pressure to raise prices due to rising ingredient costs. They needed a clear path forward that would protect profit and avoid triggering internal second-guessing.

Complication:

The team brought Red Analytics in from the start – aware that standard pricing models often fail in food & beverage by treating price and pack size as independent. We deployed PPA EngineX up front to model how revenue is actually earned in the category.

For validation, we ran a standard model in parallel. As expected, it produced inverted demand curves and misleading signals – the kind that would’ve stalled the decision and fractured team alignment.

The Outcome:

PPA EngineX delivered a confident price point that protected profit and avoided downstream confusion. The strategy team had a clear, defensible answer before the conflict started – and the pricing move went forward without delay.

The promise of Revenue Crafting: to see the trap before it’s sprung – and apply a model built from the failures that laid it.

Situation :

A syndicated study aimed to uncover how players value core game modes, like “Capture the Flag”, when delivered through different monetization formats: base game, DLC, or subscription. The goal was to shape strategy by understanding which combinations actually drive value.

Complication:

Standard conjoint analysis failed. Because the same game modes appeared across formats, the model fractured each preference across multiple parameters – creating multicollinearity and unstable results. What looked like noise in the output was actually a deeper problem: a model that didn’t reflect how players think, and that risked distorting what to charge for.

Outcome:

Red Analytics engineered a custom model that linked game modes across formats while introducing a separate parameter for the value of format itself. This stabilized the output and aligned the insights with how players experience content – and how strategy teams make monetization decisions.

Situation:

A software company needed to optimize pricing across a multi-product portfolio. They turned to conjoint analysis to reveal customer preferences and simulate pricing moves using standard off-the-shelf tools.

Complication:

Midway through, the plan collapsed. The platform couldn’t handle the study structure – and when the team reached out, the vendor confirmed: no code changes, no flexibility, no help. That left them with unusable output, stranded in the gap between what the software supported and what the decision required.

The study hadn’t just stalled – it had stopped answering the original question.

Outcome:

Red Analytics built a custom model and simulator tailored to the decision space. The result wasn’t just recovery – it was a strategic upgrade:

• Revenue-based scenario testing
• Product mix simulation
• Clear tradeoffs the team could explain and defend

What started as handcuffed analysis became a portfolio pricing strategy the team could run, use, and sell up the chain. The team avoided what could have been an expensive embarrassment – salvaging strategy and credibility in the process.