Promotions & Margin Sensitivity

This use case explains how to model promotions and margin sensitivity across multiple venues for hospitality groups in Model Reef.

You will:

• Represent price, discount and promotion levers in venue revenue models.

• Link promotions to volume, mix and COGS impacts.

• Analyse margin sensitivity to price, discount, mix and cost changes.

• Use scenarios to test promotion and pricing strategies across the group.

Model Reef is not a POS or discount engine. It uses planning level assumptions to approximate the impact of promotions and price changes on financial results.

When to use this pattern

Use this pattern when:

• Price and discount decisions have material impact on margin.

• You run recurring promotions across venues or brands.

• You need to understand trade offs between volume and margin.

• You want to test promotion strategies before committing to them.

It adds to:

• Venue Level Forecasting Pack

• Group Level Consolidated Reporting

• Staff Rostering and Labour Planning

• Build a Pricing Model

Architecture overview

Promotions and margin sensitivity modelling uses:

1. Price and discount drivers

   • Base price per item or category.

   • Discount percentages or promotional price points.

   • Promotion calendars.

2. Volume and mix drivers

   • Volume uplift or cannibalisation effects.

   • Mix shifts between items or categories.

   • Day part, day of week and event effects.

3. COGS and margin

   • Recipe or category level cost per item.

   • Gross margin impact of price and mix changes.

4. Scenario analysis

   • Alternative promotion calendars and price strategies.

   • Sensitivity plots and reporting.

1

Step 1: Extend venue revenue drivers with price and discount inputs

In the Venue Level Forecasting Pack, you already have revenue drivers. Extend these by adding:

• Base Price per item or category.

• Promotion Price or Discount Percentage.

• Promotion Flags per period or event.

For example, you might have drivers for:

• Food Base Price and Food Discount during weekdays or specific campaigns.

• Beverage Base Price and price changes during happy hour.

Revenue formulas become, for example:

• Revenue = Volume × (Base Price × (1 minus Discount Percentage)).

Keep price and discount drivers in the Data Library so you can apply them consistently across venues where promotions are common.

2

Step 2: Model promotion triggered volume and mix changes

Define drivers for how promotions affect volume and mix, such as:

• Uplift Factor for promoted items, for example 1.1, 1.2 etc.

• Cannibalisation Factor for non promoted items.

• Mix Shift Percentages where promotions encourage particular menu items.

Apply these drivers to your volume assumptions so that during promotion periods:

• Promoted items see increased volume.

• Other items may see flat or reduced volume.

• Overall covers may increase for group level promotions.

If you run event based promotions, you can create one off or recurring uplift factors aligned with your event calendar.

3

Step 3: Connect to COGS and margin

Make sure COGS modelling is detailed enough to capture margin effects. For each category or item, you should have:

• Cost per item or cost percentage of sales.

• Ability to differentiate cost structures for promoted items if promotions change portion sizes or specifications.

Margin sensitivity will come from:

• Price reductions from discounts or promotions.

• Volume changes.

• Mix shifts towards higher or lower margin products.

• Any supplier rebate or cost changes associated with promotions.

The output is a clear view of gross profit before and during promotions at venue, brand and group level.

4

Step 4: Build margin sensitivity views

Create charts and reports that show:

• Gross margin percentage by venue, brand and group over time.

• Margin versus price and discount percentage for selected items.

• Impact of specific promotions on revenue and margin for the periods they run.

• Cumulative impact of promotion calendars on annual results.

You can build simple sensitivity tables or charts by varying key drivers, such as:

• Price increase or decrease percentages.

• Discount levels.

• Uplift factors.

This helps stakeholders see the impact of potential promotion strategies without building separate spreadsheet tools.

5

Step 5: Use scenarios for promotion calendars and price strategies

Clone the base model into scenario models to compare:

• No promotions versus current practice.

• Different promotion intensity and frequency.

• Targeted promotions by venue, region or brand.

• Higher versus lower price levels outside promotion periods.

• Supplier funded campaigns versus self funded.

In each scenario, adjust:

• Promotion calendars and flags.

• Price and discount drivers.

• Volume uplift and mix shift assumptions.

• Supplier cost and rebate assumptions if relevant.

Compare scenarios using:

• Revenue and gross profit impact.

• Margin percentages and volatility.

• Cashflow and working capital implications where promotions affect inventory.

• Performance by venue and brand.

Check your work

• Promotion period assumptions match realistic calendars and durations.

• Uplift and mix assumptions are consistent with past data or informed estimates.

• Margin changes are directionally consistent with expectations.

• Scenario results are understandable for both commercial and finance teams.

Troubleshooting

Promotions appear to destroy margin in the model

Review whether uplift or mix improvements have been understated and ensure that you are not double counting discounts across multiple drivers.

Results are too sensitive to small price changes

Check that volume drivers are not overreacting to price changes, and consider applying more conservative elasticity assumptions unless strong evidence supports higher sensitivity.

Too much detail for decision makers

Summarise results at category, venue or brand level and keep detailed item level numbers behind drill downs rather than in primary dashboards.

Related guides

• Build a Sensitivity Analysis Pack

• Build a Valuation Sensitivity Model

• Operating Cashflow

• Entering Seasonality