> For the complete documentation index, see [llms.txt](https://help.modelreef.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.modelreef.io/use-cases/hospitality-groups-multi-venue/promotions-and-margin-sensitivity.md).

# 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.

{% stepper %}
{% step %}

### 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.
{% endstep %}

{% step %}

### 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.
{% endstep %}

{% step %}

### 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.
{% endstep %}

{% step %}

### 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.
{% endstep %}

{% step %}

### 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.
  {% endstep %}
  {% endstepper %}

## 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

<details>

<summary>Promotions appear to destroy margin in the model</summary>

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

</details>

<details>

<summary>Results are too sensitive to small price changes</summary>

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

</details>

<details>

<summary>Too much detail for decision makers</summary>

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

</details>

## Related guides

* [Build a Sensitivity Analysis Pack](/how-tos/scenarios-and-planning/build-a-sensitivity-analysis-pack.md)
* [Build a Valuation Sensitivity Model](/how-tos/valuation/build-a-valuation-sensitivity-model.md)
* [Operating Cashflow](/help/financial-outputs-and-valuation/operating-cashflow.md)
* [Entering Seasonality](/syntax/how-input-fields-work/entering-seasonality.md)