Financial Analysis · E-commerce
The real economics under your revenue growth
We model your DTC business from actual order data, contribution margin, CAC, cohort repeat, and the inventory cash cycle, so you see whether growth is building value or just buying revenue. Every number ties to a driver you can change, and we are clear about what a young store cannot yet know.
The Outcome
What a real financial model gives you
Not a spreadsheet of wishful numbers — a model built from your real drivers that a lender or investor can actually trust.
Contribution margin you can act on
What is left after COGS, shipping, fees, and returns on a real order, so you know which products and channels actually fund the business.
CAC and LTV that reconcile
Acquisition cost and customer value built from real cohort behavior, so payback and LTV:CAC reflect how customers actually repeat, not a blended guess.
A handle on the cash cycle
How much cash inventory ties up and for how long, so you can see why a growing store can still run tight on cash.
How we work
Here’s how we’d model it — from your drivers, not our wishes
We won’t hand you a hockey-stick built on invented assumptions. What follows is exactly how we’d model your business — from your real drivers, with transparent, stress-tested assumptions, and honesty about what can’t be known before you have the data. Where a number genuinely can’t be forecast yet, we build a framework to fill, not a fiction to sell.
Who it’s for: DTC and e-commerce founders scaling spend, wrestling with inventory cash, or raising capital, who need to know whether the unit economics support the growth they are paying for.
What We Model
What we model for e-commerce & DTC
Four workstreams that turn order and ad data into a model of whether growth creates value. Built from your real numbers, with every assumption made visible.
Contribution
Does an order actually make money?
The question: Revenue looks healthy, but after COGS, shipping, payment fees, discounts, and returns it is unclear what an order really leaves behind.
What we’d build: A contribution-margin model per order and per product, netting COGS, fulfillment, fees, discounts, and returns, so you see true contribution by SKU and channel, not just top-line gross margin.
CAC / LTV
Is acquisition paying back?
The question: You spend to acquire customers but cannot say cleanly whether they pay back, or how the LTV:CAC ratio really looks on your numbers.
What we’d build: A CAC and LTV model from real cohort repeat behavior: fully-loaded acquisition cost by channel, contribution-based LTV, payback period, and an LTV:CAC ratio grounded in how customers actually reorder.
Cohorts
How do customers repeat over time?
The question: Blended averages hide the truth. A single retention number cannot tell you whether repeat behavior is improving or decaying by cohort.
What we’d build: A cohort model tracking repeat rate, orders, and cumulative contribution by acquisition month, so retention and expansion are grounded in behavior rather than one smoothed assumption.
Cash cycle
Where does the cash go?
The question: A profitable-looking store keeps running low on cash because inventory and payment timing lock it up, and nothing shows the gap.
What we’d build: A cash-conversion-cycle model tying inventory purchases, days-on-hand, and payment timing to a cash flow, so you see how much working capital growth consumes and when.
The Fuller Scope
The fuller scope
Beyond the core four, the engagement can extend into the specifics your channels, your inventory, and your raise bring up.
Channel & marketing P&L
Contribution by acquisition channel after ad spend, so you see which channels build the business and which quietly drain it.
Inventory & reorder planning
Inventory purchases, lead times, and reorder points modeled against demand, so the cash and stock plan are the same plan.
Subscription & repeat revenue
Recurring or subscription revenue modeled by cohort with churn, so predictable revenue is separated from one-time orders.
Blended vs. new-customer economics
New-customer CAC and margin separated from repeat, so growth spend is judged on the customers it actually buys.
Three-statement & fundraising model
A linked P&L, balance sheet, and cash flow for a raise, so investors see how inventory and growth spend move real cash.
Scenario & sensitivity analysis
Base, upside, and downside on CAC, repeat rate, and margin, so you see which lever most decides whether the plan works.
CEO / founder dashboard
A live dashboard of contribution, CAC, payback, and cash on hand, so the metrics that run the business sit in one view.
The Questions the Model Answers
The questions the model answers
A model earns its keep by answering the decisions that ride on the numbers. Here’s what ours is built to answer.
What is the contribution margin?
True margin after COGS, shipping, fees, discounts, and returns, by SKU and channel, so you know what an order really leaves behind.
What is the LTV:CAC?
Contribution-based LTV against fully-loaded CAC, built from real cohort repeat, so the ratio reflects behavior rather than a hopeful multiple.
How fast does CAC pay back?
The months for a customer's cumulative contribution to cover acquisition cost, so you know how long spend stays underwater.
How much cash does growth eat?
The working capital locked in inventory and payment timing, so you see why scaling revenue can still tighten cash.
Which channel actually pays?
Contribution by channel after ad spend, so budget follows the channels that build value, not just the ones that drive orders.
What breaks the model?
Sensitivity on CAC, repeat rate, and margin that isolates the driver most likely to decide whether the plan holds.
Your model is built from your real order data, COGS, fulfillment and fee structure, ad spend, and observed cohort behavior, with every assumption exposed so any number traces back to why it is there. Future repeat rates and CAC on channels you have not scaled yet cannot be known precisely, so we anchor them to your own early cohorts and comparable data, label them as assumptions, and stress-test them rather than projecting a smooth curve as fact. What you get is a model designed to show whether growth is creating value, and honest about the parts of the future the data cannot yet settle.
The Engagement
Investor-grade, driver-based, and yours to run
A scoped modeling engagement built from the drivers you actually control — not a top-down guess. We map the assumptions, build the model, stress-test it with scenarios, and hand you a tool your team can run, present, and defend.
Transparent and honest. Every assumption is visible and sourced, downside cases are shown rather than hidden, and where something can’t be known yet we say so — a model you can stand behind, not one that flatters a deck.
Every business is different. Discovery is where we map the drivers your model runs on.
How It Works
From drivers to a model you can defend
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Discovery & drivers
We map the real operating drivers — pricing, volume, cost structure, cash timing — and the decision the model has to support.
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Model build
A clean, driver-based, three-statement or purpose-built model with a transparent assumptions tab everything flexes from.
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Scenarios & sensitivity
Base, upside, and downside cases plus the sensitivities that show which assumptions actually move the outcome.
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Review & pressure-test
We stress the model against the questions a lender or investor will ask — coverage, runway, returns, breakeven — and fix what doesn’t hold.
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Deliver & support
You get a documented model your team can run, plus support taking it into the raise, the loan, or the board meeting it was built for.
FAQ
E-commerce & DTC — financial-modeling questions
What tools do you build in?
Excel or Google Sheets, unlocked and fully formula-driven, so you and your investors can open every cell. We can pull from your Shopify, ad platform, and accounting exports, but the model itself is yours to keep and maintain.
Is this investor-grade?
Yes. The model is built the way DTC investors expect to see it: real contribution margin, cohort-based LTV and CAC, a working-capital view, and a documented assumptions layer they can interrogate directly rather than take on faith.
How long does it take?
A focused unit-economics and cohort model is often one to two weeks; a full three-statement fundraising model with inventory and scenarios takes longer. The pace depends on how clean and accessible your order and ad data are.
What does it cost?
Scoped to the work. A contribution-and-cohort model costs less than a full three-statement build with inventory modeling and a fundraising package. We quote a flat fee against a defined scope before we begin.
Do you use our real drivers?
Yes. We build from your actual order economics, COGS, fees, returns, ad spend, and cohort behavior. Where a forward driver is uncertain, repeat rate on a new channel, we set it as an explicit assumption rather than importing a generic benchmark.
We are early with thin data, can you still model us?
Yes, with honesty about the limits. We build from whatever real cohorts you have, anchor the rest to comparable data as clearly labeled assumptions, and design the model so it sharpens automatically as more order history accumulates.
Three-statement or a simple model, which do we need?
If the question is whether the unit economics work, a contribution and cohort model answers it. For a raise or for managing inventory-heavy cash, you need the linked three-statement build. We recommend the model that fits your decision, not the largest one.
Do you help with the raise itself?
We build the fundraising model and the financial slides, and we prep you for the questions investors ask about CAC, margin, and cash. We are not your fundraising agent, but you will be able to defend every number in the room yourself.
The Full Practice