Situation: A national snack brand was forced to raise prices due to rising ingredient costs and couldn’t afford to get it wrong. They knew volume would fall – but the real question was:

• How much decline is acceptable, and where is profit optimized across all flavors and pack sizes?

There was no appetite for guesswork. The strategy team needed clarity – not another deck of scenarios.

Complication: Standard pricing models treat price and pack size as independent — but in food & beverage, pack size drives price. That’s where traditional approaches break.

We were brought in because of this exact flaw.

We ran both models side by side – and just as expected, the “best practice” approach produced inverted demand curves and contradictory recommendations. That wasn’t a forecast. That was Insight Compliance -a model built to fit the method, not the decision.

The Revenue Crafting Approach: Using PPA EngineX, we rebuilt the pricing architecture from the ground up:

• Captured pack-price interdependence
• Modeled substitution behavior between pack sizes
• Revealed profit-maximizing price points under real-world constraints

This wasn’t about picking the right method. It was about designing a strategy that reflected how revenue is really earned.

The Outcome: We projected a small but manageable volume decline, Improved profit from realigning price points, and a clear pricing strategy the team could confidently execute – not debate.

In a category where most pricing research ends in ambiguity, this one ended in action.

Situation A syndicated study set out to understand how players value popular game modes – like “Capture the Flag” – when delivered as part of the base game, DLC, or a subscription. The goal was to reveal which combinations drive value – and how they might shape future monetization strategies.

Complication Standard conjoint analysis failed. Because game modes appeared across multiple formats, the model assigned multiple parameters to the same underlying preference – creating multicollinearity and outputting inconsistent, unstable results. The risk wasn’t just bad statistics – it was producing a view of player preferences that could misinform the logic of what to charge for.

Result We engineered a custom model that identified each game mode across formats and introduced a separate parameter to isolate the value of format itself. This resolved the confusion, stabilized the outputs, and delivered insights that matched how players think – and how strategy teams make monetization calls.

Situation: A software company needed to optimize pricing across a portfolio of products. They chose conjoint analysis to surface customer preferences and guide key product decisions. The plan: use standard tools to get clean outputs and simulate price moves.

Complication: Midway through, it became clear the conjoint software couldn’t handle the study design. The format was incompatible – and when they reached out, the vendor confirmed: no code changes, no support. That left the team stranded – with only generic “importance scores” and no way to run a real portfolio optimization. They weren’t just losing outputs. They were losing the ability to answer the real decision the study was supposed to support.

Result: We built a custom model and simulator tailored to the structure of their data and decision space. It unlocked the strategic outputs:

• Revenue-based scenario testing
• Product mix simulation
• Confidence in the actual tradeoffs

The team moved from handcuffed analysis to a pricing strategy they could stand behind – and sell up the chain.