Where is volume heading?
Forecast by brand, region, customer or time level early enough to change commercial or supply plans.
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GN CPG & Beverages Decision Pack · CPG Decision Intelligence
Turn demand signals into commercial decisions.
For revenue growth and trade marketing teams deciding which prices, promotions and distribution moves to make. Use sell-in, sell-out, distribution, pricing, promotions and market context to decide what is driving volume, which interventions create incremental impact and where commercial investment should move.
Decisions this pack supports
Move from volume drivers to commercial levers.
Which promotions create incremental uplift?
Separate attributed sales from credible incremental effect and identify which promotional conditions matter. See how promotion effectiveness is measured.
How sensitive is demand to price?
Estimate price response by SKU, channel, customer or market and compare the volume, revenue and margin trade-off. See price elasticity modelling.
Which levers actually move volume?
Distinguish actionable commercial drivers from signals that merely move with the outcome.
Where should commercial effort move?
Compare feasible interventions and rank the actions expected to create the strongest business outcome. See MMM vs incrementality for budget decisions.
From today's process to the next decision
Decide which promotions deserve the next investment.
How teams make the decision
Teams compare sales before, during and after a promotion, then use reports and spreadsheets to plan the next cycle.
What this leaves unresolved
Higher sales do not establish that the promotion created incremental profit. Seasonality, cannibalisation and discount costs can change the result.
What the GN Pack adds
Estimate incremental impact where the data supports it, then compare promotion choices against margins, budgets and commercial constraints.
What your team receives
A prioritised promotion, pricing or distribution move by brand, customer or geography, with estimated impact, supporting evidence and the constraints considered.
See the pack in action
From your business data to a decision you can act on.
Explore what a director or manager could receive. Choose a model, see its output, then compare practical actions with costs and constraints.
Illustrative interactive demoAll figures and businesses are fictional. These are examples of potential deliverables, not actual customer results, guarantees or live predictions.
CPG / EXAMPLE DECISION WORKSPACE
Where should the commercial budget go?
Forecast demand, isolate credible commercial levers and compare investments after cost.
01 / Explore a model output
EXAMPLE OUTPUT 01 / 05
128,000 HL
Forecast monthly volume
What we found
Volume is forecast 6% below the comparable period; three regions account for most of the gap.
Recommended next move →
Review regional supply and investment plans.
Decision-relevant output6% modelled shortfall
02 / Compare possible decisions
Where should we invest a €50,000 budget?
Switch between the options and compare expected contribution after intervention costs. Numbers use fixed fictional assumptions.
Gross incremental contribution+€98,000
Intervention cost€50,000
Net incremental contribution+€48,000
Selected action
Distribution recovery
Restore availability in selected outlets and regions.
Estimated gross incremental contribution €98,000 less €50,000 intervention spend.
Scope: 3,900 HLFeasibility: Within stated scenario rules
Operating constraints considered
See what this pack could recommend for your business →- Maximum €50,000 total intervention spend
- Retailer and distribution feasibility
- Counterfactual effect estimates require validation
Read every example output and decision alternative
Demand Forecasting: 128,000 HL — Forecast monthly volume
Volume is forecast 6% below the comparable period; three regions account for most of the gap. Review regional supply and investment plans. 6% modelled shortfall.
Demand Driver Model: 3 regions — Priority demand signals
Distribution and category demand explain much of the projected decline; association is not itself a causal effect. Investigate addressable distribution gaps. 3 regions to investigate.
Promotion Incrementality: 12 markets — Where promotions may pay back
Observed promotional sales exceed the estimated counterfactual, but national discount cost absorbs much of the gain. Repeat only interventions with credible incremental contribution. Targeted promotion candidates.
Price / Distribution Response: 120 outlets — Potential distribution recovery
Availability recovery in selected outlets is modelled to outperform a broad price reduction. Prioritise outlets where feasible distribution gains have credible economic value. 120 opportunity outlets.
Intervention Optimisation: €48,000 — Expected net contribution
A targeted distribution plan delivers the highest estimated contribution after the intervention cost under the scenario assumptions. Assign investment to selected outlets, then measure actual outcomes. +€48,000 modelled net contribution.
Decision options
Run a broad price promotion. Estimated gross incremental contribution €62,000 less €50,000 spend. Scope: 4,800 HL.
Restore availability in selected outlets and regions. Estimated gross incremental contribution €98,000 less €50,000 intervention spend. Scope: 3,900 HL.
Run a media intervention in priority regions. Estimated gross incremental contribution €59,000 less €50,000 spend. Scope: 2,600 HL.
Models inside this pack
One product bundle around a recurring business decision.
GN CPG & Beverages combines the models, business logic and constraints needed to move from your operating data to a decision-ready recommendation.
DECISION PACKGN CPG & Beverages
Built aroundSell-in · sell-out · price · distribution · promotions
Demand ForecastingForecasts volume by brand, SKU, customer, region or time period.
Demand Driver ModelQuantifies how price, distribution, macro, media, weather and other drivers relate to volume.
Promotion IncrementalityEstimates what would likely have happened without the promotion and separates attributed sales from incremental effect.
Price / Distribution ResponseModels response to price or distribution changes at the level the data can credibly support.
Intervention OptimisationRanks feasible commercial moves using expected effect, budgets, timing and operating constraints.
OUTPUTDecision support your team can use
Models are combined only where the decision needs them. The pack returns a business-ready action with supporting evidence and expected impact.
- Brand
- customer
- geography
- investment move
- Expected impact and supporting evidence
- Configured around your real operating constraints
Demand Forecasting
Forecasts volume by brand, SKU, customer, region or time period.
Where supply and commercial plans need to move before the result is visible.
Demand Driver Model
Quantifies how price, distribution, macro, media, weather and other drivers relate to volume.
Which commercial levers deserve attention and which are only signals.
Promotion Incrementality
Estimates what would likely have happened without the promotion and separates attributed sales from incremental effect.
Which promotions to repeat, redesign, target differently or stop.
Price / Distribution Response
Models response to price or distribution changes at the level the data can credibly support.
Where price, availability or route-to-market changes are likely to create value.
Intervention Optimisation
Ranks feasible commercial moves using expected effect, budgets, timing and operating constraints.
Where commercial investment should move next.
Pack anatomy
One repeatable path from market data to commercial action.
Reusable decision workflows, configured around the portfolio, market and levers you can actually change.
Your commercial data
Volume, price, distribution, promotions, customers, brands, media, weather, macro signals and other relevant first-party or external drivers.
Predictive + causal models
Forecast outcomes, estimate intervention effects and use causal methods where the question is about changing the outcome.
Available levers and constraints
Define the actions the commercial team can take and the budgets, timing, availability or policy constraints around them.
A prioritised intervention
Return the lever, geography, brand, customer or investment move with its expected effect and supporting evidence.
Prebuilt + configured
The decision architecture is reusable. The market context is yours.
Graphite Note reuses the workflow for forecasting, driver analysis, causal validation and intervention ranking while configuring the data, levers, hierarchy and constraints for your portfolio.
Where is volume heading?
Build demand expectations early enough to change commercial or supply plans.
What can actually change it?
Use causal evidence to distinguish intervention candidates from correlation.
Which move is strongest?
Rank feasible actions against expected commercial impact.
Direct answers
Questions about the GN CPG & Beverages Decision Pack.
What is the GN CPG & Beverages Decision Pack?
It is a packaged Decision Intelligence solution for recurring decisions around demand, distribution, promotions, commercial drivers and intervention planning.
Can it measure promotion effectiveness?
Yes, where the available data supports a credible design. Graphite Note can use causal methods to estimate incremental impact rather than relying only on attributed sales or simple before-and-after comparisons.
Can it support assortment decisions?
Yes. Where transaction or sell-out data, product economics and sufficient variation are available, Graphite Note can model substitution and constraints to support listing, delisting and customer or channel assortment decisions. See assortment optimisation.
What data can it use?
Typical inputs include sales or volume, price, distribution, promotions, brand and customer dimensions, media, weather and relevant market or macro variables.
What comes out?
The output can be a forecast, ranked driver set, estimated intervention effect or prioritised commercial action tied to the decision the team needs to make.
Decision IntelligenceFrom signal and prediction to a governed business action.AutoMLAutomate repetitive model-building work without losing business context.Causal Machine LearningSeparate drivers from correlations and estimate intervention effects.Predictive AnalyticsForecast demand, score customers and anticipate business outcomes.Decision OptimisationCompare feasible actions and rank them by expected business impact.Forecasting vs OptimisationSee where prediction ends and the business decision begins.Decision APIDeliver recommendations, expected impact and evidence into workflows.Embedded Decision IntelligenceAdd a decision layer to ERP, CRM, BI, workflow and vertical software.Build vs BuyA practical framework for deciding whether to build, buy or embed.
Bring the volume problem, drivers and available levers.
We will structure the pack around what the data can credibly support and what the business can actually change.