AI & Data Analytics for E-commerce & D2C

Beyond ROAS: measure real profit

ROAS ignores product cost, shipping, fees and returns. We instrument contribution margin (CM2), LTV by cohort and blended CAC, so you scale only where the unit economics work: the core idea in Beyond ROAS and What’s a good ROAS.

Blended attribution across channels

Platform-reported numbers over-claim and double-count. Blended attribution gives one honest view across Meta, Google, quick commerce and owned channels, so budget follows what truly drives profitable orders, not what each platform says it drove.

Cohorts, forecasting and dashboards

Cohort analysis shows whether each month’s customers are getting more or less valuable, and forecasting makes the next move obvious. Clear dashboards on one source of truth are the AI & Data Analytics foundation, powered by our analytics stack.

Cohort LTV, the view that stops you scaling a loss

A blended monthly ROAS can look healthy while the business quietly loses money on every new order, because it hides product cost, shipping, fees, returns and the fact that different customers are worth wildly different amounts. Cohort analysis is the antidote. By grouping customers by when they first bought and tracking their contribution margin and repeat behaviour over time, you see the truth: which acquisition sources bring one-and-done buyers versus loyal repeat customers, how long CAC takes to pay back, and whether your LTV actually justifies what you’re spending to acquire. That’s the difference between scaling profit and scaling a loss you won’t notice for months. We build the dashboards that make it visible, CM2, LTV by cohort, blended CAC, retention curves. On one trustworthy source of truth, so budget decisions are made on economics rather than a flattering top-line number.

Fix the tracking before you trust the dashboard

Most D2C analytics problems are really data-quality problems. If your GA4 is double-counting, conversions aren’t firing reliably, or privacy and consent changes have blown holes in the data, every dashboard built on top inherits the error, and you end up scaling or cutting spend on numbers that quietly lie. So the first job is always a measurement audit: verifying events, deduplicating conversions, reconciling platform figures against actual Shopify sales, and setting up server-side and first-party tracking that survives cookie and consent shifts under the DPDP era. Only once the foundation is trustworthy do the cohort, CM2 and LTV models mean anything. It’s unglamorous work, but it’s the difference between analytics that guides budget and a dashboard that merely looks confident, and for a brand spending real money on acquisition, trustworthy numbers are what stop you scaling a loss you can’t yet see. This is the same measurement discipline our analytics practice brings to every store before a single dashboard is built.