Free Webinar
Mindset Shift in RGM: From Independent Tools to Holistic Solutions
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When pricing, promotions, and pack decisions live in separate tools, the portfolio can’t be optimized as a system.
The Integration Gap Inside RGM
Most RGM organisations have strong methods in isolation: elasticities, price-pack architecture, promo uplift analysis, occasional conjoint work. The failure mode is what happens when those outputs collide: different teams defend different “truths,” and decisions get averaged, delayed, or made on instinct.
This session addresses the mindset shift required to move from independent tools to integrated decisioning. Not as a technology story, but as an operating reality: if pricing, promotions, and PPA interact in market, then the analytics must model them together, across the portfolio and against competitive response—at the level where decisions are actually taken.
What You'll Learn
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Stop treating RGM levers as separate workstreams when the market treats them as one system. Why pricing, promotions, and PPA create second-order effects that invalidate “single-lever” answers once you manage a real portfolio. [05:41]
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Recognise when “the workhorse” becomes the constraint. How price elasticities remain useful, but also become misleading when they vary by price point, depend on demand shape, and change meaningfully once multiple products move together. [14:48]
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Avoid the most common integration workaround: averaging conflicting analyses. Why taking the midpoint between elasticity-based outputs and price-pack logic produces a number that is rarely defensible or optimal. [19:20]
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Model portfolio interaction explicitly, not as an afterthought. How changes to one SKU shift volume across your own range and competitors—so “winning” on a single product can still be suboptimal for total portfolio performance. [30:18]
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Anchor integration at the level decisions are made. What it means to run modelling by customer or channel (and when outlet-level detail is unnecessary), so insights translate into executable commercial decisions. [40:11]
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
Elasticities are powerful—until you assume they’re stable. [14:48]
The session shows why a single elasticity value can be a dangerous simplification when demand is closer to linear, elasticity changes by price point, and the same move behaves differently depending on whether one SKU shifts or the full portfolio shifts.
Price-pack logic and elasticity logic can point in opposite directions. [17:23]
In a real portfolio, one tool may argue a SKU is priced “too high” versus a curve, while elasticity suggests price should rise. Without modelling cross-effects, teams end up choosing whichever narrative fits—or averaging both.
Averaging contradictory analyses is a decision failure, not a compromise. [19:20]
Ingo calls out a common organisational behaviour: when tools disagree, teams take the midpoint. The session positions this as a symptom of fragmented analytics, not a credible pricing method.
Integration is not only about levers—it’s about effects across the portfolio and competition. [05:01]
The discussion makes the point that RGM decisions must account for substitution, cannibalisation, and competitor response. If you can’t quantify those movements, “optimisation” is often just local improvement.
Holistic modelling changes the answer because the levers interact. [34:12]
The session highlights that you can’t sum the impact of isolated lever tests. When price, promo, and pack changes coexist, the market response is not additive—and that’s exactly where many planning cycles go wrong.
Decision quality improves when the model follows real shopper switching behaviour. [36:38]
Ingo ties integration back to a behavioural foundation: shoppers move between SKUs, brands, and sometimes out of category. Modelling that switching is what makes integrated recommendations materially different from tool-by-tool outputs. [36:38]
Q&A
Can the modelling work without weekly data—e.g., monthly—and what’s the minimum history needed?
Yes, monthly data can be used. The guidance shared is to use as many data points as possible, with three or more years being helpful to capture seasonality and trend effects more reliably. [39:11]
Is modelling done at customer level or outlet level?
The modelling is intended to run at the level decisions are made—typically customer or channel level, depending on data granularity. Outlet-level modelling is usually unnecessary unless decisions (like promotions) are truly executed and governed at that level. [40:11]
Does the model assume a closed market, or can it reflect category expansion and incremental sales?
It does not assume a fixed total market volume. The response explains that some categories are more “expansive” under promotions (driving incremental volume) while others mainly shift volume within the category, and the solution learns those dynamics from historical patterns. [41:04]
Do you factor in category share, especially when volume is under pressure?
Yes—market share metrics can be included and analysed alongside units, revenue, profit, and other KPIs. The session notes that both volume share and value share views can be added to the KPI set depending on what the team needs to manage. [42:11]
Can profitability be viewed in percentage terms, such as margin bps changes?
Yes, margin can be included as a KPI and analysed directly, including whether it increases, decreases, or remains stable under scenarios. This can be used to express impact in percentage terms rather than only absolute profit. [42:50]
Can the output be used to build a customer-facing sell-in story?
Yes—the model can be viewed from both sides, including retailer outcomes and manufacturer outcomes, which supports win-win reasoning. The answer explicitly frames this as a common client need: quantifying customer impact to support customer conversations. [43:15]