Performance analytics for a direct-to-consumer brand

The business

A growing direct-to-consumer brand selling supplements and performance nutrition products online. Revenue was climbing past the point where gut-feel decisions still work, but the data infrastructure hadn't kept up.

The team was small — a founder, a marketing contractor, and an outsourced fulfilment partner. Shopify was the storefront. Stripe handled payments. Google Ads and Meta were driving acquisition. The systems worked individually, but nobody had connected the dots between them.

The challenge

Three specific blind spots were holding the business back.

No unit economics. The founder knew top-line revenue but couldn't tell you the contribution margin on any individual product. Shipping costs, returns, and promotional discounts were all lumped together. A product that looked like a bestseller might have been losing money after fulfilment.

No customer intelligence. Repeat purchase rate, average order value over time, and customer lifetime value were all unknown. The Shopify dashboard showed orders, but not whether those customers came back, how often, or what drove them to return.

No channel attribution. Marketing spend was split across Google, Meta, and email, but there was no way to see which channel was actually driving profitable customers versus just traffic. ROAS was calculated at platform level with no connection back to actual margin.

What we built

A performance engine that pulls data from Shopify, Stripe, and marketing platforms into a single view, updated in real time.

Revenue and margin visibility. Total revenue, order count, AOV, and conversion rate at a glance. Every SKU drills into a waterfall from gross revenue through discounts, returns, COGS, and shipping to contribution margin.

Customer cohort tracking. Repurchase rates by monthly cohort show exactly when customers come back — or don't. Customer health segmentation splits the base into VIP, repeat, one-time, at-risk, and churning segments, with revenue attributed to each.

Product-level intelligence. Every SKU is ranked by revenue, margin percentage, repurchase rate, and customer rating. Trending indicators flag products gaining or losing momentum before it shows up in the top line.

Channel attribution. Sessions, revenue, conversion rate, ROAS, and CAC by channel. Campaign-level drill-down connects marketing spend to actual orders, not just clicks.

Customer journey mapping. The acquisition funnel runs from first visit through to VIP status. Segment migration tracking shows how customers move between cohorts over time, making retention patterns visible.

What the platform does

Unit economics become visible at product level. Every SKU carries a full margin waterfall from gross revenue through discounts, returns, COGS, and shipping — so margin-negative products surface before they quietly eat into the bottom line.

Marketing spend becomes comparable. Channel-level CAC sits next to channel-level LTV in the same view, so the conversation about where to put the next dollar moves from gut feel to evidence.

Repeat purchase behaviour becomes measurable. Cohort analysis shows exactly when customers drop off, and customer health segments flag at-risk groups early enough for retention flows to matter.

Concept build. Fictional brand. All data illustrative.