Free Webinar

Leveraging Virtual Shopper Insights for Optimizing Price Pack Architecture (PPA)

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When inflation limits price moves, pack architecture becomes the real growth decision.

Designing Packs for Real Choice, Not Assumptions

For many CPG organisations, price pack architecture has moved from a periodic hygiene exercise to a core commercial lever. Inflation has increased price sensitivity, reduced headroom for list price increases, and exposed the limits of treating PPA as a static structure rather than a dynamic decision system.

This session addresses why PPA decisions fail when they’re evaluated in isolation—by pack, by brand, or by elasticity—and why understanding shopper switching, cannibalisation, and portfolio-wide effects is now essential. The focus is not on tools, but on how organisations make PPA decisions that hold together commercially under pressure.

What You'll Learn

Why PPA has become more critical as inflation reshapes price sensitivity.
As further price increases become harder to execute, pack size changes and portfolio restructuring emerge as alternative levers—raising new questions about shopper trade-offs and value perception. [03:55]
Why PPA cannot be evaluated independently from pricing, promotions, and channel mix.
Changes to pack size affect every other lever, from promotional mechanics to trade terms and cross-channel consistency, making siloed decisions structurally flawed. [08:00]
How traditional PPA logic breaks down once shopper switching is considered.
Rules-based structures and price ladders create internal consistency, but fail to explain how shoppers actually choose between promoted and unpromoted packs, brands, and sizes. [16:49]
Why elasticity signals often contradict PPA logic—and what that tension reveals.
A pack can look mispriced architecturally while still appearing underpriced through elasticity, exposing the limits of single-product analysis. [19:41]
How virtual shopper modelling makes cannibalisation and substitution visible before execution.
By simulating individual shopper choices across the full portfolio, teams can quantify where volume moves internally, what leaks to competitors, and what disappears altogether. [21:49]

Meet the Speakers

Ingo Reinhardt

Co-founder and Managing Director at Buynomics

Before Buynomics, Ingo was a Senior Director with Simon-Kucher & Partners, a global leader in pricing. He holds a Ph.D. in Management from the University of Cologne and Master's degrees in Management and Mathematics. Ingo was a PostDoc at the University of Oxford and published in the Strategic Management Journal.

Mario Koenigsfeld

VP Revenue at Buynomics

Mario is VP of Revenue at Buynomics, where he leads and scales global commercial teams. With a background at Simon-Kucher and over 9 years of experience in pricing, sales strategy, and revenue optimization, he combines analytical rigor with hands-on leadership to build high-performing teams and drive sustainable growth across industries.

Session Highlights

Inflation has pushed PPA into the C-suite conversation.
Mentions of price pack architecture are rising in earnings calls as leaders confront the limits of price increases and look for alternative margin levers. [03:55]

PPA is one lever—but never an isolated one.
Pack size decisions interact directly with pricing, promotions, portfolio mix, channels, and trade terms, making partial optimisation ineffective. [08:00]

Occasion-based pack logic creates structure—but also hidden trade-offs.
Smaller and larger packs address real usage occasions, yet narrow price gaps can unintentionally push shoppers into unintended sizes. [14:19]

Architectural consistency can conflict with profit signals.
A pack may violate expected price-per-unit logic and still show low elasticity, creating tension between structural coherence and short-term profit optimisation. [19:41]

Elasticity alone can’t explain where volume goes.
Single-product elasticity ignores substitution within the portfolio and towards competitors, obscuring the true impact of pack and price changes. [21:14]

Virtual shoppers turn PPA into a portfolio-level decision.
By modelling millions of individual choices, teams can see switching patterns, cannibalisation, and total profit impact before committing to execution. [21:49]

Q&A

How do you account for purchase frequency or use-up rates when testing new pack sizes?
The model can incorporate time-based effects, where higher purchases in one period reduce demand in subsequent periods, reflecting stockpiling or accelerated usage. This allows pack changes to be evaluated over time rather than as one-off volume shifts. [43:24]

Does the behavioural model consider shopper price knowledge and price awareness?
Yes. Shopper sensitivity to price levels and thresholds is learned from observed behaviour, though the strength of price knowledge varies widely by category and market. Ingo notes that actual price awareness is often lower than teams expect. [44:06]

Are PPA scenarios based only on item-level price elasticities?
No. Virtual shopper behaviour allows elasticities to be derived not just for price, but also for pack size, brand, and other attributes—based on observed switching rather than assumed curves. [45:59]

How do you assess cannibalisation when introducing a new pack size?
By simulating how shoppers reallocate choices across the entire portfolio, the model shows how much volume is incremental versus taken from existing products or competitors. This allows teams to judge whether added occasions outweigh internal cannibalisation. [36:04]

How reliable are the profit and revenue forecasts from these simulations?
Accuracy depends on data quality, but the approach is designed to reflect real decision dynamics by modelling individual choice behaviour rather than relying on aggregate assumptions. The focus is on directional confidence and trade-off clarity, not point estimates in isolation. [21:49]