I'm Michael Haddad. I pull your raw data myself, find where reality has drifted away from what you believe, and turn that into strategy you can defend in any room.
Maybe your model is a forecast in a spreadsheet. Maybe it's just what you believe: CAC is around $45, shipping runs about 12%, subscribers stay 8 months. Written down or not, that's your model. The question is whether it still matches reality. Usually it doesn't, in at least one place that matters.
One brand I work with believed shipping to Canada cost under $40 per order. Their 3PL's invoices buried the real figure: closer to $70, and Canada wasn't the only country affected. Their international unit economics were fiction. And they were gearing up to launch discounted international subscriptions on those numbers, which would have meant losing cash on every single order, locked in at subscription prices. The audit caught it first.
Another brand, $12M on Shopify, had weekly forecasts that kept drifting from actuals. Their own CFO couldn't find the cause. In the raw cohort data I found that customers acquired years earlier had quietly grown to about 25% of revenue, and the model couldn't see them. Retention targets sat too low, the marketing team could coast all year and still beat plan, and the board would congratulate everyone. The model wasn't just inaccurate. It was hiding how the business actually performed.
Dashboards don't catch this. Benchmarks don't catch this. Your accountant isn't looking for it. Someone has to open the raw data and check, and that's the work I do.
One brand, two years, through a board crisis and a broken-attribution year. Read the full story →
One blended LTV-to-CAC number replaced the weekly guesswork in media buying debates. Arguments that used to go "CAC is down but site traffic leaked to Amazon, are we better off?" became "LTV to CAC went from 1.41 to 1.56, this is a win."
Models I've built or torn apart across SaaS, consumer, logistics, and healthcare. That range is why I could look at a struggling DTC brand and see what specialists missed: a subscription brand is a SaaS business wearing an e-commerce costume.
Fixed price. Two weeks. Findings in writing.
I audit your forecast, your CAC math, and your unit-economics assumptions. No formal model? Then I reconstruct the one implied by your dashboards and decisions, and test that instead.
Shopify exports, ad platforms, 3PL invoices, cohort tables. I find the root causes of where your numbers have diverged from reality.
A written report: the two to four divergences that matter, what each is costing you, and the corrected numbers to run the business on. Delivered in a walkthrough call, written so you can repeat it to a partner, board, or investor without me in the room.
Most clients do ask me to stay on afterward. That conversation happens because you've seen the findings, not because I pitched you.
Before ecommerce, I led dozens of due diligence engagements at a UK investment agency that took equity in the startups it backed. The firm's money rode on my recommendations, in both directions: deals I argued for, deals I killed. The companies are named, the outcomes are public, and my calls have aged well.
It's the same scrutiny I now point at ecommerce P&Ls: what are the numbers actually saying, underneath the story everyone wants to believe?
Cohort revenue, retention curves, LTV payback. The metrics that actually govern your future are the ones a standard e-com dashboard treats as afterthoughts. Getting one brand to take that reframe seriously is what turned –$2.9M EBIT into cash positive.
A partner, a board, an investor. Or reality itself, when the cash stops matching the forecast. Better to know the answer before the question comes.