How to Model Ecommerce using the Standard Financial Model

Prebuilt for Ecommerce Businesses

The Standard Financial Model works for ecommerce out of the box. The prebuilt revenue engine on Revenues handles acquisition, repeat-purchase cohorts, order volume, AOV, and cash. The Forecast sheet covers cost of sales, inventory, shipping, and fulfillment.

The video above walks through the end-to-end structure. The rest of this page points at the specific cells you'll touch.

When this guide applies

Use the Standard Financial Model for ecommerce when you want one document covering revenues, hiring, expenses, statements, inventory, and cash. It fits DTC brands with a paid acquisition motion, wholesale + DTC hybrids, subscription box and repeat-purchase models, hardware + consumable models, and any ecommerce business you want investor-ready financials for.

The free Ecommerce Forecasting Tool is a better fit if you want a revenue-only cohort model with historicals (~10x less code). See below.

Configure it in the Standard Financial Model

The whole revenue model can run off Get Started. Cells to set:

Model structure

• D10: Base Timescale. Leave at "monthly" for ecommerce.
• D11: Periods. Default 72 (6 years).
• D12: Date of first period.

Revenue model type

• D22: Revenue model type. Set to ecommerce. This wires the Revenues sheet for transaction-style repeat purchase instead of subscription retention.
• D20: Growth metric label (e.g. Sessions, Visitors, Ad Impressions).
• D21: Revenue metric label (e.g. Orders, Customers, Transactions).

Growth (traffic or acquisition)

• D26: New Growth Units in first month.
• D27: Growth start date (usually =D12).
• D28: Initial growth rate.
• D29: Growth rate deceleration. Default -5%. Ecommerce acquisition channels mature quickly; tune this up (more negative) if you're forecasting a paid channel you expect to saturate.
• D30: Use seasonality. For ecommerce, set to yes. Seasonality is usually material.
• D34-D35: CPA per paid Growth Unit and % acquired through paid. Most ecommerce brands run paid as the majority channel; set D35 to 100% if paid is everything.

Conversion (Growth Units to Orders)

• D40: Conversion rate. For session-to-order, 1–3% is a common starting point to tune against your analytics. The default is illustrative, not a benchmark. If your Growth Unit is already "customers acquired," set conversion to 100%.
• D41: Conversion lag months. 0 for most ecommerce (session converts in-session).

Two segments (Segment 1 vs Segment 2)
D44-E44 are segment names. Common ecommerce uses:

• DTC vs Wholesale (different AOV, different billing, different channels)
• First-purchase vs Subscription (subscribe-and-save)
• Core product vs Accessories
• B2C vs B2B

Configure each segment:

• D45-E45: % split of conversions to each segment.
• D46-E46: Churn rate per period. For ecommerce, "churn" is the inverse of repeat-purchase rate: if 40% of customers reorder in a given cycle, churn is -60%.
• D47-E47: Churn period. 1 = monthly reorder cadence, 3 = quarterly, 12 = annual. This is your repeat-purchase cycle.
• D54-E54: Avg Revenue per Revenue Unit (AOV).
• D55-E55: Billing period. 1 for ecommerce (billed at order).
• D56-E56: % billed upfront. 100%; orders are paid at checkout.

Why segments matter for ecommerce: DTC and wholesale have different AOV, different repeat-purchase cycles, and different cash timing. Splitting them keeps margin math honest on the Breakdown sheet.

Seasonality: required for ecommerce

• D131-D142: monthly % adjustments, Jan through Dec. D143 sums (should stay at zero; monthly adjustments shift the shape, they don't change the annual total).
• Ecommerce usually has Q4 lift (November, December), summer softness, and a post-holiday dip. Populate this early. The shape matters for inventory, hiring, and cash.
• More at Seasonality.

Overrides on Revenues
When you need month-specific overrides (promos, launches, stock-outs), the input rows on Revenues (Segment 1 in R20-R691, Segment 2 in R693-R1172):

• R114: Manual growth adjustments (columns AB-CU) for one-time traffic events.
• R202: Manual conversion adjustments for promos or site changes.
• R378+: Per-month AOV overrides for launches or price changes.

Common modifications

• Additional growth channels. Replicate the growth block on Revenues for each channel, or add channels on Forecast with custom logic and sum them before conversion.
• Multiple SKUs or bundles. Build a SUMPRODUCT table for weighted-average AOV, feed into D54. For AOV differences that matter for margins, split across the two segments instead.
• Cost of goods sold, shipping, fulfillment. Default on Forecast. Add expense rows in R72-R95 using drivers: % of Revenue Cat 1 for COGS (R70), $ per Order for shipping and fulfillment (drive off Revenue Units), % of Revenue for payment processing.
• Inventory. Prebuilt on Forecast R613-R639. Inputs on Get Started cover lead times (months from PO to stock), minimum order quantities (dollar value), safety stock (minimum on-hand), and payment terms (upfront vs N days arrears). The section calculates dollar inventory needed to support the forecast. It does not unit-count SKUs, but can be extended. More at Inventory.
• Returns and refunds. Add as a % of Revenue driver row on Forecast, or net against AOV on D54-E54.
• Hardware + consumables. Use Segment 1 for the one-time hardware purchase (set D47 churn period high so cohorts don't repeat) and Segment 2 for the consumable subscription (recurring churn cycle). Or build the hardware as a driver off Segment 2 new customers.
• Custom revenue logic. Build it outside and link in. See Integrating models.

When to reach for the Ecommerce Forecasting Tool

The Ecommerce Forecasting Tool is ~10x less code and purpose-built for cohort-based repeat-purchase modeling. Use it when you only need a revenue forecast, not full statements; when you want to load real historicals on the Historicals sheet and calibrate the retention curve against actual per-cohort reorder data; or when you want separate AOV for new vs repeat customers (D17 vs D18) and separate CAC for acquisition vs reactivation (D31 vs D32), built in without editing segments.

Both models use cohorts. The Ecommerce Forecasting Tool exposes the per-cohort retention curve more directly; the Standard Model wraps it inside a full P&L.

Edit with AI

Start with the universal context primer and Standard Model template primer. Paste them into Claude for Excel before anything specific.

Then three ecommerce-specific prompts:

Add wholesale as Segment 2 alongside DTC

I have DTC and wholesale revenue. Set up Segment 1 as DTC and Segment 2 as wholesale. DTC: AOV ~$80, reorder cycle ~3 months, 40% repeat rate. Wholesale: AOV ~$2,500, reorder cycle ~6 months, 80% repeat rate. Walk me through which cells to set on Get Started (D44-E44, D45-E45, D46-E46, D47-E47, D54-E54), confirm the inputs before editing, then show me the resulting revenue split on Breakdown.

Tune monthly seasonality for Q4 retail lift

I'm in Q4-heavy retail. Typical split: Nov and Dec combined are ~35% of annual revenue, Jan-Feb are the weakest months, summer is mid. Propose D131-D142 values on Get Started that produce this shape while summing to zero at D143. Before applying, show me what each month's adjustment would be and confirm the annual total doesn't change.

Model inventory carry cost on physical goods

I carry physical inventory with ~60-day lead times and pay suppliers 50% upfront, 50% at receipt. Walk me through setting lead-time, MOQ, safety-stock, and payment-terms inputs on Get Started, and confirm the cash impact shows on Forecast R613-R639 and flows into Statements. Then help me add a % of Inventory carry-cost expense row on Forecast using drivers so storage and financing show up on the P&L.

Common questions

See the Standard Financial Model page for FAQ. I run free and paid onboarding sessions and custom model work; contact me any time.

Related: Revenues · Cohorts · Drivers · Seasonality · Inventory · SaaS guide · Ecommerce Forecasting Tool · Edit with AI