Free Webinar
Next-Generation Pricing Methods for Sustainable Profit Growth
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Watch the session to explore how next-generation pricing methods are evolving and how a more holistic approach can support sustainable profit growth.
Rethinking Pricing in a More Complex Market Environment
In this webinar, we explore how pricing teams can move beyond isolated methods and adopt next-generation pricing approaches that support sustainable profit growth. As markets become more complex and inflation, competition, and customer behaviour continue to shift, relying on a single pricing lens is no longer enough.
The session breaks down the strengths and limitations of traditional pricing methods, such as cost-plus, competitor-based, elasticity-driven, value-based, and behavioural pricing and shows why they often lead to conflicting recommendations. From there, it introduces a more holistic way of thinking about pricing within Revenue Growth Management, connecting pricing with portfolio structure, promotions, and mix decisions to enable more consistent and resilient outcomes.
What You'll Learn
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How “next-gen pricing” actually maps to real methods teams use today.
A clear breakdown of cost-plus, competitor pricing, elasticity, value-based pricing, behavioural pricing, and where AI fits (and doesn’t) in practice. -
Why pricing methods often conflict and what the “elephant” metaphor reveals.
How different approaches each capture only part of the truth, and why that leads to wildly different recommended prices. -
What price elasticity is good for and where it quietly breaks.
Why elasticity changes by price point, how demand function assumptions matter, and why “one elasticity per SKU” is often misleading. -
How behavioural pricing changes outcomes without changing the product.
Practical examples like decoy effects, good-better-best structures, and price thresholds (e.g., 9.99 vs 10.00), and why they matter commercially. -
What a “holistic” pricing approach looks like in RGM.
How pricing connects with PPA, promotions, mix and trade spend and why treating them separately creates inconsistent decisions. -
Where virtual shoppers fit into next-generation pricing.
The logic of simulating real purchase choices across the full shelf (not just one product), and using that to test scenarios and steer decisions.
Meet the Speakers
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Ingo ReinhardtCo-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.
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Anton von LampeChief of Staff |
Anton is the Chief of Staff and Head of Product at Buynomics. Previously, he headed the Customer Success department and worked closely with international clients in the CPG, Telco, and Retail industries. He holds a MSc in International Management from the Rotterdam School of Management.
Session Highlights
Why traditional pricing methods point to different “optimal” prices [04:19]
An introduction to the core problem in pricing: cost-based, competitor-based, elasticity-driven, value-based, and behavioural approaches each describe only part of reality—often producing contradictory recommendations when used in isolation.
The “blind men and the elephant” problem in pricing [04:32]
A powerful metaphor explaining why pricing teams struggle to align internally when different functions rely on different pricing logics without a shared framework.
Cost-plus and competitor pricing: why they persist and when they still make sense [13:01]
A balanced view on why these methods remain widely used, particularly in mature industries and resource-constrained teams, and what their hidden strengths and limitations are.
Price elasticity explained and where it breaks down [16:59]
A clear walkthrough of price elasticity, profit-optimal pricing, and revenue optima, including why elasticities change by price level and fail to scale to portfolio decisions.
Behavioural pricing: how real shoppers deviate from rational models [21:14]
Examples of price thresholds, decoy effects, and middle-option bias and why behavioural effects are real, measurable, and difficult to integrate into classical pricing models.
Why pricing tools don’t naturally fit together [23:10]
An explanation of why combining multiple pricing methods often leads to wide price ranges and why averaging outputs is not a valid strategy.
From pricing to Revenue Growth Management (RGM) [27:09]
Why pricing decisions cannot be separated from pack architecture, promotions, channel mix, and trade spend and how pricing becomes more powerful when embedded within RGM.
The limits of price elasticity lists [28:44]
Why static elasticity numbers are fundamentally flawed, and why next-generation pricing cannot be built on elasticities alone.
Introducing virtual shoppers as a unifying pricing framework [29:25]
How virtual shoppers model real decision-making across prices, products, competitors, and behaviours, creating a single, integrated view of demand.
Live example: managing cost inflation with integrated pricing decisions [32:13]
A practical walkthrough showing how pricing, competitive context, and shopper behaviour interact when costs increase and how different strategies perform in simulation.
Why next-generation pricing matters even in disinflationary markets [40:47]
A forward-looking discussion on why advanced pricing capabilities remain critical as markets stabilize and pricing distortions from inflation are corrected.
Q&A
How can pricing teams reconcile conflicting outputs from different pricing methods?
Many traditional pricing approaches such as cost-plus, competitor benchmarking, elasticity analysis, and value-based pricing each capture only part of the pricing problem. Because they are built on different assumptions, they often point to very different “optimal” prices. The key is not to average these outputs, but to integrate them within a single decision framework that reflects how customers actually choose between products in a competitive environment.
Why are price elasticities often misleading when used in isolation?
Price elasticities are not fixed product attributes; they depend on the current price level, the competitive set, and the broader portfolio context. A single elasticity value cannot capture cross-effects between products or changes in shopper behaviour when multiple levers move at once. This makes elasticity lists a weak foundation for portfolio-level or strategic pricing decisions.
What role does behavioural pricing play in modern pricing strategies?
Behavioural pricing highlights that customers do not always behave rationally. Factors such as price thresholds, preference for the middle option, and framing effects materially influence purchase decisions. These effects are real and measurable, but difficult to integrate into traditional analytical models unless customer behaviour is explicitly modelled rather than assumed.
How does a holistic RGM approach improve pricing decisions?
Pricing is only one lever within Revenue Growth Management. Decisions on price interact directly with pack size, promotions, channel mix, and trade spend. A holistic approach evaluates these levers together, ensuring that changes in one area do not unintentionally undermine performance elsewhere in the portfolio.
How do virtual shopper models reflect real customer behaviour across markets?
Virtual shoppers are calibrated using context-specific data, including historical sales, market structure, and available consumer research. Each virtual shopper represents a distinct set of preferences and decision rules, allowing the model to reflect differences in geography, economic conditions, and competitive dynamics. This enables scenario testing that mirrors how real markets respond to pricing and portfolio changes.
Are next-generation pricing methods still relevant in a disinflationary environment?
Yes. Periods of stabilising or declining inflation often reveal pricing distortions created during high-inflation phases. This is when advanced pricing methods become especially valuable—helping teams identify where prices have overshot, where margins can be protected through mix or pack changes, and how to rebalance portfolios without relying solely on price reductions.
