Free Webinar
Mastering Pricing Strategy: The Ultimate Guide for Profitable Growth
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Pricing strategy breaks when “the right price” is treated as a number, rather than a set of trade-offs the organization can execute.
From price mechanics to pricing strategy: diagnosis, choices, and coherent action
Most pricing work gets stuck in the microeconomics comfort zone: elasticity, margins, and a theoretical profit optimum. That logic is clean, until you re-enter the real world of portfolios, competitor moves, inflation shocks, and internal constraints that make “optimal” pricing a moving target rather than a single answer.
Ingo’s argument is that pricing strategy only matters because reality is messy. The job is to diagnose what’s changing in the environment, choose a guiding policy on which levers to use (and which to avoid), and then make the actions coherent enough that the organization can execute them, especially when revenue and profit objectives pull in different directions.
What You'll Learn
- Why pricing strategy exists at all, and what a “simple world” gets wrong.
If pricing were just costs plus elasticity, the profit-optimal price would be mechanical. Strategy becomes necessary because real categories involve portfolios, competitors, inflation, and shifting constraints that break the single-product logic. [04:35] - How to use the diagnosis → guiding policy → coherent action structure in pricing.
A practical application of Richard Rumelt’s strategy definition: diagnose the challenge, set a guiding policy on which levers to use (price, promotions, portfolio, pack changes), and then make actions coherent and executable. [08:17] - Why “silver bullet” pricing moves rarely exist in mature categories.
Ingo contrasts strategy-as-trickery with strategy-as-planning and makes the case that most profitable growth comes from disciplined lever choices, not one heroic price move or pack change. [06:42] - How inflation can invalidate your historical pricing logic overnight.
A clear example of how a pre-inflation elasticity relationship can become misleading when prices rise sharply but demand doesn’t respond as expected, making diagnostics and model choice non-negotiable. [19:45] - How to manage the profit vs. revenue trade-off without pretending it’s a single objective.
Most companies are not pure profit maximisers or pure revenue maximisers. Strategy is choosing where to land on that trade-off and selecting pricing actions that move you along an efficient boundary rather than chasing dominated options. [22:24]
Meet the Speaker
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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.
Session Highlights
Pricing is simple only in theory, strategy exists because reality adds portfolios, competitors, and shocks.
Ingo opens with the microeconomic “single product” world to show why it’s not an operating model for modern pricing teams. [04:35]
Most pricing “strategy” failures are actually definition failures.
He frames strategy as either trickery (finding a Trojan horse) or planning discipline, and argues mature categories rarely offer one-move solutions. [06:42]
Good strategy starts with diagnosis, not action.
Using Rumelt’s model, Ingo pushes teams to define the nature of the challenge before debating levers and execution. [08:17]
The David vs. Goliath story is a lesson in guiding policy, not bravery.
He uses the example to show how hidden strengths, constraints, and a single coherent policy (agility, one shot, refuse armour) create a winnable strategy. [09:51]
Inflation can break your historical elasticity assumptions.
A simple scatter example shows a period where a 35% price increase does not reduce sales, highlighting why legacy relationships can fail when regimes change. [19:45]
Profit-growth decisions live on a frontier, your job is choosing the trade-off, not chasing a perfect number.
Ingo shows how different price and offer combinations create different revenue/profit outcomes, and why efficient boundaries matter for selecting “best” options. [26:11]
GenAI helps with selection and alignment, if it sits on top of prediction and optimisation.
He positions GenAI as a layer that can steer recommendations toward company strategy and constraints (price-ending rules, no price decreases, strategic shifts), not as a replacement for market modelling. [27:31]
Q&A
How do you define “pricing strategy” beyond elasticity and profit-optimal price formulas?
Ingo’s view is that strategy only matters because pricing is not a single-product optimisation problem in reality. Once portfolios, competitors, and external regime shifts enter, the job becomes diagnosing the challenge, choosing which levers to use, and executing coherent actions aligned to company objectives. [04:35]
Why do “silver bullet” pricing moves fail so often in established categories?
He argues that in mature industries it’s hard to find one decisive move that changes outcomes on its own. Most results come from disciplined planning: differentiating prices and coordinating multiple RGM levers rather than betting on one pack size or price move to “save the day.” [06:47]
How should teams respond when inflation changes demand behaviour in ways historical models don’t capture?
Ingo highlights that relationships estimated in a pre-inflation period can give the wrong guidance once the regime changes. The implication is to revisit diagnostics and model assumptions rather than applying old elasticities mechanically into a new environment. [20:42]
How should leaders handle the inevitable profit vs. revenue tension in pricing decisions?
He notes that most companies are neither pure profit maximisers nor pure revenue maximisers, so pricing decisions naturally sit on a trade-off curve. The strategic question is where to land, and how to choose actions on an efficient boundary rather than adopting options that sacrifice both. [22:24]
Where does GenAI actually help in pricing strategy without falling into the “halo effect”?
Ingo is explicit that GenAI is not the engine that computes the right prices just because it writes good text. Its value is in selecting and explaining options that align with company strategy and rules, once prediction and optimisation have produced credible alternatives. [29:16]
How accurate are the forecasts behind a revenue vs. profit curve, and how confident can you be in a chosen point?
Ingo answers that accuracy depends on data quality, but cites that they often reach around 95% accuracy in practice. He frames this as a discussion worth unpacking further based on the specific data and context. [32:19]