Multi-Channel Revenue Forecasting

This use case describes how to forecast revenue across multiple channels for eCommerce and direct to consumer brands using Model Reef.

You will:

• Represent sales channels such as direct web, marketplaces and wholesale.

• Forecast traffic, conversion and average order value where appropriate.

• Model units and revenue per SKU or product family per channel.

• Roll channel level revenue into consolidated P&L, cash and valuation.

The aim is to make channel performance explicit rather than hiding everything in a single aggregate revenue line.

When to use this pattern

Use this pattern when:

• You sell across multiple channels with different economics, for example direct web, Amazon, retail partners and wholesale.

• Channel mix is strategic for margin and brand control.

• You want to see channel contributions to revenue and profit separately.

It is usually combined with:

• SKU Margin and Contribution Modelling

• Paid Ads CAC Forecasting

• Inventory Purchases and Reorders

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Architecture overview

Multi channel forecasting includes:

• Branch structure

  • Branches per channel (Direct, Marketplace, Wholesale, Retail, etc.).

  • Optional child branches for region or major partners.

• Volume and funnel drivers

  • Traffic and conversion drivers for direct channels.

  • Order volumes and sell-in volumes for wholesale and retail.

• Price and discount drivers

  • Selling price and discount per SKU or family per channel.

  • Different economics at retail versus wholesale versus marketplace.

• Revenue variables and outputs

  • Revenue per SKU per channel.

  • Channel level revenue and margin.

  • Consolidated brand or group revenue.

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1

Define channel branches

In the branch tree, create a structure such as:

• Brand

  • Channel - Direct Web

  • Channel - Marketplaces

  • Channel - Wholesale

  • Channel - Retail Stores

If you operate multiple brands, have a parent branch per brand under a group level root branch.

Each channel branch will hold its own revenue, discount and sometimes cost variables.

2

Set up volume and funnel drivers per channel

For direct web and app channels, use funnel drivers:

• Sessions or Visits.

• Conversion Rate.

• Average Basket Size in Units.

• Average Order Value.

Compute orders and units sold as:

• Orders = Visits × Conversion Rate.

• Units Sold = Orders × Units per Order.

For marketplaces, wholesale and retail, you may use:

• Order volumes in units per customer or partner.

• Sell-in volume to retailers and distributors.

• Sell-out volume if you receive reports on downstream sales.

Store these as drivers or variables in the relevant channel branches.

3

Define pricing and discount per channel

For each SKU or family and channel, define drivers for:

• List Price per Unit - Channel.

• Average Discount per Channel.

• Net Price per Unit - Channel.

Examples:

• Net Price - Hoodie Classic - Direct Web.

• Net Price - Hoodie Classic - Marketplace.

• Net Price - Hoodie Classic - Wholesale.

Note: Marketplaces may also have platform commission percentages which you can model either as a reduction in effective net price or as a separate cost line.

4

Build revenue variables per SKU and channel

Combine volume and price to create Revenue variables per SKU and channel, for example:

• Revenue - Hoodie Classic - Direct Web.

• Revenue - Hoodie Classic - Marketplace.

• Revenue - Hoodie Classic - Wholesale.

Typical formula:

• Revenue = Units Sold per Channel × Net Price per Unit per Channel.

Ensure all revenue variables are typed as Revenue so they flow into P&L, Cashflow and Cash Waterfall.

Create channel level roll up variables or use reports and dashboards to aggregate across SKUs within each channel branch.

5

Include channel specific variable costs where relevant

Channel economics often differ in variable costs, for example:

• Marketplace commissions and fulfilment fees.

• Wholesale discounts and marketing contributions.

• Retail rent, staffing and store operating costs.

Create COGS or Opex variables such as:

• COGS - Marketplace Fees.

• Opex - Retail Store Rent.

• Opex - Wholesale Slotting Fees.

Attach these to the relevant channel branches. Combined with SKU level contribution modelling, they give a comprehensive view of channel profitability.

6

Build channel and consolidated dashboards

Create dashboards that show:

• Revenue per channel and per SKU over time.

• Channel mix as a share of total revenue.

• Gross margin and contribution per channel.

• Key KPIs such as revenue per visit for Direct, revenue per customer, or revenue per store.

At the root branch, view:

• Total brand or group revenue across channels.

• Margin and cash metrics that aggregate all channels.

This supports discussion about growth priorities, channel investment and channel mix management.

7

Use scenarios for channel strategy and mix

Clone the model into scenario models to reflect different channel strategies, for example:

• Aggressive marketplace expansion.

• Direct first strategy with reduced marketplace reliance.

• Wholesale expansion with new retail partners.

• Store roll out and new retail geographies.

For each scenario, adjust:

• Volume, funnel and pricing drivers per channel.

• Channel specific cost assumptions.

• Marketing and CAC assumptions in connected paid media models.

Compare scenarios on:

• Revenue and margin per channel.

• Channel mix and dependency.

• Cash requirements and risk.

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Check your work

• Channels and branches reflect how the business actually operates and reports.

• Volume, price and discount assumptions per channel are grounded in data or well informed estimates.

• Channel revenue and margin patterns look plausible when compared to historical results.

• Consolidated revenue aligns with company level planning when calibrated.

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Troubleshooting

• Channel results do not reconcile to total sales Check for double counting between channels and ensure that volumes are partitioned correctly.

• Mix shifts create unexpected swings in margin Verify that unit cost and channel specific costs are correctly allocated and that price differences are correctly applied.

• Model becomes too complex with many channels and partners Group smaller channels or partners into aggregate buckets and only model the largest or most strategically important ones in detail.

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Related guides

• Build a Multi Entity Group Model

• Build a Multi Period Comparison Dashboard

• Revenue Variables: what is, rules, what it affects

• Modifier Driver Fields