GA4, Shopify, and Ads never agree.
Revenue and conversions are out of sync across tools. Finance trusts Shopify, marketing stares at Ads Manager, and GA4 is somewhere in the middle.
Industry · Ecommerce & DTC Analytics
Make every dollar you spend on traffic show up in your numbers.
We help ecommerce and DTC brands fix the tracking, modeling, and attribution chaos between Shopify, GA4, Ads, Meta, and your warehouse—so you can optimize for profit, not just ROAS screenshots.
- Revenue, orders, and conversions aligned across GA4, Shopify, Stripe, and ad platforms within an agreed tolerance.
- Clean event schemas and server-side tagging powering Meta CAPI, Google Ads, and other performance channels.
- Cohort, LTV, and CAC payback views that match how you buy traffic and how your customers actually repeat.
Built for DTC brands, marketplaces, and multi-store retailers where ad spend, merchandising, and inventory need one shared source of truth.
Example · Ecommerce Performance Snapshot
Key metrics
- ✦ AOV, conversion rate, and cart-to-checkout rate
- ✦ New vs returning revenue
- ✦ First-order vs repeat-order mix
- ✦ CAC payback and contribution margin
Signals we wire in
- ▸ Product + variant performance by cohort
- ▸ Channel, campaign, and creative efficiencies
- ▸ Subscription + one-off order behavior
- ▸ Fulfilment, refunds, and margin impact
Result: a view where buyers, products, and channels line up—so scaling spend doesn't feel like gambling.
Ecommerce Reality
You're spending serious money on traffic—but your data still doesn't match how you actually make money.
We rebuild ecommerce analytics around how your business operates: product assortment, margins, offers, and acquisition strategy—not just what your theme and a few apps happen to track out-of-the-box.
Channel ROAS is a political argument.
Meta, Google, influencers, and email all claim the win. Nobody can see blended performance, incrementality, or what actually drives profitable growth.
Subscriptions, bundles, and one-off orders are a mess.
Rebill revenue, intro offers, and multi-item carts aren’t modeled cleanly. LTV, churn, and contribution margin are guesses, not metrics.
Tracking breaks every time the site or CMP changes.
Theme edits, app churn, and consent changes quietly break events and pixels—months go by before anyone realizes what’s missing.
Foundations First
Fix the measurement foundation before you chase another "ROAS hack."
Chasing attribution hacks on top of broken fundamentals is how brands burn cash. We focus on event design, revenue logic, and signal quality first—so every experiment and optimization has a chance to work.
Revenue & order truth alignment
- Define a single source of truth for orders, refunds, discounts, and tax.
- Align GA4 purchase and conversion events with Shopify/Stripe/warehouse logic.
- Set realistic tolerance thresholds so teams stop re-litigating every delta.
Event schema for the full shopper journey
- Design events for browse, add-to-cart, checkout, purchase, and post-purchase flows.
- Handle subscriptions, pre-orders, bundles, and custom upsell paths cleanly.
- Track key UX states: low stock, backorder, out-of-stock, back-in-stock alerts.
Identity, cohorts, and LTV reality
- Model customers across devices, sessions, and email identities.
- Define first-order vs repeat, one-time vs subscriber, and VIP segments.
- Wire LTV and cohort metrics into reports leadership actually uses.
Signal resilience for performance media
- Server-side tagging for Meta CAPI, Google Ads, and other key platforms.
- Consent-aware signal design that still gives your algorithms fuel.
- Monitoring for dropped events and degraded conversions over time.
Ecommerce Data Architecture
A stack where products, customers, and channels finally line up.
The goal isn't just pretty dashboards. It's a measurement system where the merch team, the performance team, and finance all agree on what "good" looks like—and can see the same levers at work.
Storefront & UX Layer
- Theme and UX events across homepage, collections, PDP, cart, and checkout.
- Tracking for search, filters, merchandising modules, and recommendations.
- Split between new vs returning, logged-in vs guest, and mobile vs desktop.
Typical tech
Shopify, headless storefronts, WooCommerce, custom carts; GA4, GTM, server-side tagging, experimentation tools.
Order, Subscription & Fulfilment Layer
- Orders, line items, discounts, and shipping data from Shopify/ERP/OMS.
- Subscription objects and rebill events from Recharge, Skio, etc.
- Refunds, cancels, partial returns, and chargebacks wired into analytics.
Typical tech
Billing/ERP APIs, webhooks to warehouse, server-side events to GA4/Ads, standardized order tables keyed by customer/account.
Marketing & Lifecycle Layer
- Acquisition campaigns across Google, Meta, TikTok, and affiliates.
- Email, SMS, and retention touchpoints modeled alongside orders.
- Influencer, creator, and referral attribution handled without duct tape.
Typical tech
GA4, server-side GTM, Meta CAPI, Google Ads enhanced conversions, email/SMS platforms, affiliate platforms, UTMs + campaign taxonomy.
Warehouse, Models & Profitability
- Customer and order models joined across store, marketing, and finance.
- Product, collection, and category profitability with real COGS and fees.
- Cohorts by first-touch channel, intro offer, and key merchandising strategies.
Typical tech
BigQuery/Snowflake/Redshift, dbt or SQL models, Looker Studio / Power BI, margin and payback models owned by analytics, not ad platforms.
Optimization & Testing
- Always-on tracking of landing pages, offers, and creative performance.
- Systematic testing of PDPs, bundles, subscriptions, and onsite flows.
- Budget allocation frameworks grounded in LTV and contribution margin.
Typical tech
Experimentation platforms, GA4 experiments, in-house frameworks, channel and creative scorecards fed from the warehouse.
Selected Ecommerce Engagements
From "which numbers are right?" to "what do we scale next?"
The through-line: align tracking and revenue, wire in real margins and cohorts, and give your team a clear view of what actually drives profitable growth—not just top-line revenue.
DTC brand scaling paid social
Problem: GA4 under-reported purchases vs Shopify, Meta over-claimed credit, and leadership didn’t trust any ROAS number.
What we did: Aligned revenue logic across GA4, Shopify, and warehouse; implemented server-side tagging and Meta CAPI; standardized UTMs and offer tracking.
Impact: Clear blended ROAS and CAC payback, confident budget shifts into winning audiences and creatives, fewer "are these numbers real?" meetings.
Multi-region, multi-store retailer
Problem: Each region had its own tracking hacks and reporting logic. HQ couldn’t see global view of performance or product profitability.
What we did: Standardized tracking schemas, centralized order and product models in the warehouse, and rolled up region-level reporting with consistent metrics.
Impact: Leadership could compare regions apples-to-apples and roll out best-performing campaigns and merchandising strategies globally.
Subscription-first ecommerce
Problem: Intro offers and first orders looked great—but nobody could see true subscription retention, churn, or payback by channel and offer.
What we did: Modeled subscription lifecycle events, wired them into GA4 and BI, and created cohort dashboards by acquisition source, offer, and pricing plan.
Impact: The team shifted spend and creative towards offers with better LTV and unit economics instead of just the lowest CAC.
For performance marketing
Clean conversion, revenue, and LTV feedback loops into Google, Meta, and other platforms—so bid strategies and budgets are driven by reality, not noisy signals.
For ecommerce & merchandising
Product, collection, and onsite performance tied back to cohorts, offers, and channels—so you can double down on the right assortment and experiences.
For founders & finance
Unit economics, CAC payback, and LTV by cohort and channel, grounded in actual margins and refunds—not just ad platform revenue exports.