Free Webinar
From Descriptive to Prescriptive Analytics in RGM
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Descriptive analytics explains the past. Prescriptive analytics changes how RGM decisions get made.
Why RGM Analytics Is Hitting Its Ceiling
Most RGM teams are rich in analysis but poor in conviction. They can explain what happened last quarter, estimate average uplifts, and debate elasticities, yet still struggle to agree on what to do next when pricing, promotions, and pack decisions collide.
This session focuses on the shift from describing outcomes to prescribing actions. Not as a technology upgrade, but as a decision upgrade. The difference lies in whether analytics can predict portfolio-wide effects, explain trade-offs clearly, and support concrete recommendations that leadership is willing to execute.
What You'll Learn
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Why descriptive analytics breaks down when decisions become interdependent. How averages, historic uplifts, and static elasticities lose reliability once multiple products, competitors, and levers move at the same time. [11:55]
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What prescriptive analytics requires beyond better models. Why prediction, optimization, and explainability must work together if analytics is going to guide real pricing, promotion, and PPA decisions. [15:18]
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How portfolio interaction changes the “right” answer. Why a price move that looks optimal for one SKU can be wrong for the portfolio once substitution, cannibalization, and competitive response are considered. [16:28]
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How to move from insight to recommendation without black boxes. What it takes for systems to explain why certain prices, pack sizes, or scenarios are recommended so teams trust and adopt them. [17:38]
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How prescriptive tools still leave room for human judgment. Why constraints, business rules, and local knowledge must sit on top of optimization rather than be replaced by it. [32:38]
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
Descriptive analytics tells you what happened, not what to do next. [12:11]
Average promotion uplifts and historic elasticities describe past outcomes, but they hide variance, context, and interaction effects that matter for forward-looking decisions.
Static elasticities become unreliable as soon as prices move. [15:10]
The session shows why elasticity changes by price point and demand shape, making table-based approaches risky when used over time or across multiple SKUs.
Prediction is a prerequisite for prescription. [15:40]
You cannot recommend optimal actions without being able to predict how shoppers, competitors, and portfolios will respond under different scenarios.
Portfolio effects matter more than single-SKU optimization. [16:45]
Prescriptive decisions require understanding how changes to one product shift demand across the rest of the range and toward or away from competitors.
Explainability determines adoption. [17:51]
Teams will not trust recommendations unless they can see why a system suggests certain moves, such as higher prices where elasticities are lower or adjustments driven by cost changes.
Prescriptive analytics is constrained optimization, not free-form automation. [33:02]
Business rules, price corridors, and strategic constraints are essential inputs that ensure recommendations are realistic and executable.
Q&A
Where does the data for the virtual shoppers come from?
The virtual shoppers are parameterized primarily using customer sales and RGM-relevant data provided by the organization. Sales data forms the foundation, supplemented by other standard pricing and revenue management data sources depending on context. [35:32]
How does this approach link descriptive analytics to value-based pricing?
Descriptive analytics can infer how attributes are priced in the market, but prescriptive methods simulate how shoppers value those attributes in decision contexts. This allows teams to move from average signals to a full distribution of willingness to pay. [36:37]
Does the model account for category expansion or changing shopper behavior?
Yes. Seasonality, trends, and evolving demand patterns are captured in the modeling, allowing elasticities and responses to adjust as categories grow or shift. [39:00]
What is the best next step if a team cannot adopt a new solution yet?
Advanced Excel and regression-based approaches can move teams part of the way by estimating average effects, but they lack precision once interactions matter. The limitation becomes material as volumes and complexity increase. [39:47]
Can this approach be applied outside retail and CPG?
The virtual shopper concept is already applied in multiple industries, including telecom and technology. While not every industry dynamic can be modeled today, the approach is broadly transferable. [40:55]
How do you prevent prescriptive tools from ignoring real-world constraints?
Constraints such as price corridors, minimum margins, or strategic rules can be built directly into the optimization. Teams can also manually adjust recommendations for exceptional cases. [32:38]