> 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/consumer-goods-fmcg-and-cpg-manufacturers/promotional-lift-discount-impact.md).

# Promotional Lift/Discount Impact

This guide explains how to model promotional uplift and discount impact for consumer goods, FMCG and CPG manufacturers in Model Reef.

You will:

* Represent base demand and promotional uplift separately.
* Model discounts, trade spend and promotional mechanics by retailer and channel.
* Quantify the impact of promotions on revenue, volume, margin and cash.
* Use scenarios to test promotional calendars, depth and mix.

{% hint style="info" %}
Model Reef is not a trade promotion management system. It models the financial consequences of promotional strategies using structured drivers, not in-store execution data.
{% endhint %}

## When to use this pattern

Use this pattern when:

* Promotions and discounts are a significant part of your commercial model.
* You want to understand lift versus cannibalisation and net margin impact.
* You run multiple promotional mechanics across retailers and channels.
* You need scenario level visibility for promotional strategy decisions.

It combines well with:

* SKU Manufacturing Cost Model
* Retailer and Channel Margin Modelling
* Multi Channel Revenue Forecasting style patterns
* New Product Launch Forecast

## Architecture overview

Promotional modelling uses:

* Baseline and uplift separation
  * Baseline volume and price per SKU and channel.
  * Uplift volume driven by promotional activity.
* Promotional mechanics
  * Price discounts, temporary price reductions.
  * Multi buy or quantity based mechanics.
  * Feature and display, retailer funded mechanics.
  * Trade spend, rebates and co op funds.
* Margin and cash analysis
  * Gross to net waterfall.
  * Contribution impact per promotion, per SKU, per channel.
  * Promotional calendars by retailer and region.

{% stepper %}
{% step %}

### Define baseline volume and price per SKU and channel

Using demand or sales modelling patterns, create drivers for each SKU and channel such as:

* Baseline Volume per period (units or cases) without promotion.
* Baseline Price per Unit or per case.
* Baseline Mix for SKUs within a category.

Model these as if there were no promotions. These series will underpin uplift analysis and margin attribution.
{% endstep %}

{% step %}

### Build promotional calendars and uplift drivers

In the Data Library, define promotional calendars by SKU and channel, for example:

* Promotional Weeks per SKU per retailer.
* Depth of Discount as a percentage of list price.
* Expected Uplift Factor on volume during promotional periods, for example 1.5, 2.0, 3.0.
* Cannibalisation or pull forward factors where promotions move volume across periods or SKUs.

Represent this as time series and flags so that you can identify:

* Periods that are on promotion.
* Uplift and discount applied in those periods.
* Any offset in other periods due to pull forward.

You can use separate drivers for retailer funded and manufacturer funded mechanics where needed.
{% endstep %}

{% step %}

### Model promotional volume, price and trade spend

Extend the revenue model by splitting volume into:

* Baseline Volume.
* Incremental Volume from promotions.

Use formulas such as:

* On Promotion Volume = Baseline Volume × Uplift Factor.
* Incremental Volume = On Promotion Volume minus Baseline Volume.

Attach pricing and trade spend drivers, for example:

* Promotional Price per Unit.
* Trade Discount or Off Invoice per unit.
* Fixed Fees for retailer features or displays.
* Post event rebates or scan data based accruals.

Then compute:

* Gross Revenue = Volume × List Price.
* Net Revenue after Discounts = Volume × Promotional Price or Net Price.
* Trade Spend = Trade Discount plus Fixed Fees plus Rebates.
* Net Net Revenue = Gross Revenue minus Trade Spend.

You can calculate these at SKU, channel and retailer level by using appropriate branches and categories.
{% endstep %}

{% step %}

### Link promotions to cost and margin

Combine promotional revenue with cost per unit from the SKU Manufacturing Cost Model. Compute:

* Gross Margin on Baseline Volume.
* Gross Margin on Promotional Volume at reduced price.
* Net Margin after Trade Spend.

Use custom reports or dashboards to show:

* Margin per unit and per case for promoted versus non promoted periods.
* Incremental profit or loss from promotional activity.
* Contribution per promotion, per retailer and per channel.

This helps distinguish between volume driving and value destroying promotions.
{% endstep %}

{% step %}

### Use scenarios for promotional strategy, mix and depth

Clone the base model into scenario models to explore:

* Different promotional frequencies and calendars.
* Shallower versus deeper discounts.
* Shifts in promotional spend between retailers or channels.
* Alternative mechanics such as multi buy instead of price cut.
* Changes in funding mix between manufacturer and retailer.

In each scenario, adjust:

* Promotional calendar and flags.
* Uplift factors, discount depths and trade spend drivers.
* Cannibalisation and pull forward assumptions.
* Pricing and cost assumptions where strategy changes are broader.

Compare scenarios using:

* Net revenue and margin over the year.
* Incremental profit contribution by promotion.
* Retailer and channel level performance.
* Cashflow implications of trade spend timing and accruals.
  {% endstep %}
  {% endstepper %}

## Check your work

* Baseline and uplift volumes are grounded in historical promotional performance.
* Promotional calendars reflect realistic retailer windows and constraints.
* Trade spend assumptions reflect actual commercial terms.
* Scenario outputs are helpful for joint business planning and internal reviews.

## Troubleshooting

<details>

<summary>Promotions appear to destroy too much value</summary>

Check that cannibalised or pulled forward volume is not counted as incremental and ensure that trade spend is applied correctly to incremental versus total volume where relevant.

</details>

<details>

<summary>Results differ from promotional post analysis</summary>

Align uplift, cannibalisation and funding assumptions with your existing post event analytics or adjust them to match measured outcomes.

</details>

<details>

<summary>Model is too granular to maintain</summary>

Group similar SKUs and promotions into promotional archetypes and apply those patterns rather than modelling each event individually.

</details>

## Related guides

* [Build a Pricing Model](/how-tos/operations-and-unit-economics/build-a-pricing-model.md)
* [Build a Relative Valuation Using Fundamentals](/how-tos/valuation/build-a-relative-valuation-using-fundamentals.md)
* [Units & Bounds](/help/drivers-variables-and-timing/units-and-bounds.md)
* [Overriding Drivers](/syntax/scenario-syntax/overriding-drivers.md)