Shopify Benchmarking for Data Analysis

4 attribution sources. 4 different answers. Your MTA tool, GA4, Meta, and Shopify can't agree on attributed $$$. And you can't audit why. Standalone MTA tools add proprietary pixels and enforce vendor-locked query params. Meanwhile, your GA4 + CAPI + Shopify + survey data already contains SUPERIOR insights. Your existing 1st party data captures DEEP attribution insights: → Server-side CAPI (Elevar, Blotout, Littledata) + GA4: event-level data of purchase journeys with identity resolution → Shopify: Order-level attribution (1st & last touch) → Zero-party surveys (Fairing, KnoCommerce): Awareness channel credit The goal: Join these sources to create verifiable attribution, complete funnel visibility, and methodology you control. 1. Start Simple 🚀 Sync GA4 to BigQuery (free). This unlocks event-level data and real-time reporting GA4's UI can't provide. Elevar users: their Pub/Sub feature gives you a real-time firehose of server-side events. 2. Unify Sources 🔗 Join touch points from GA4, CAPI, Shopify, survey responses by order IDs. Now you can do journey analysis. 4. Build Logic 🧮 Start with rule-based models (first/last/linear) to establish baselines. Then test more advanced models customized to your typical purchase journeys. 5. Visualize 📊 GSheets or LookerStudio will often suffice (free). Experiment with different views to fit your decision-making process. 6. AI Validation 🤖 Export sample data with human-verified calculations. Feed to Claude/ChatGPT to validate logic, catch edge cases, and generate SQL for advanced models like Markov chains or Shapley values. 7. Scale Up 🐍 Move complex analysis to Python notebooks. Libraries like pandas for data manipulation, lifetimes for CLV modeling, and scikit-learn for ML-based attribution are battle-tested and FREE. Reality check: More work than SaaS, but you get complete confidence, custom logic, and zero vendor lock-in. This makes sense if you're: • Running $1M+ monthly digital spend • Already investing in data infrastructure • Competing on marketing efficiency • Fed up with conflicting attribution sources Choose your data battles wisely. If attribution accuracy drives your growth, owning this capability changes everything. Wanna nerd out? Comment or DM. 😎

Here’s the exact framework that $50M+ brands are using to evaluate their storefront performance. (You’ll want to Save this for later) Most merchants obsess over total revenue and conversion rate, then wonder why their optimizations don't move the needle. After analyzing 1,000+ Shopify stores, here's the framework that actually drives profitable growth: 1. North Star Metrics Yes, there should be one. But these two are inseparable (they are like twins): → Gross Profit per Visitor (GPV): the hotter sibling. Your true profitability indicator. (Total Revenue - COGS) ÷ Total Visitors. Shows how much profit each visitor generates → Revenue per Visitor (RPV): Total Revenue ÷ Total Visitors. Combines conversion rate and AOV into one optimizable number 2. Context Metrics Your business health indicators: snapshots and trends plotted over time: → Total Revenue: top-line growth indicator → Number of Orders: volume and capacity indicator → New Customer Rate: % of first-time buyers → Return Customer Rate: % from existing customers → LTV: total revenue per customer relationship 3. Revenue & Conversion Drivers The profitability building blocks: → Conversion Rate: % of sessions that purchase → Average Order Value: mean purchase amount → Median Order Value: middle purchase amount (better for typical behavior) → Subscription Take Rate: % opting into subscriptions → Post-Purchase Take Rate: % accepting upsells/cross-sell offers post-purchase → Units per Transaction: items per order, which is a good measure of your cross-selling activities 4. Funnel Metrics Where visitors actually drop: → Sessions: total storefront visits → View Product Rate: % viewing at least one product → Add-to-Cart Rate: % of viewers who add to cart → Checkout Rate: % of carts that initiate checkout Don't forget abandonment rates at each stage, because they show where to focus optimization. → Product Abandonment Rate → Cart Abandonment Rate → Checkout Abandonment Rate → Post-purchase Abandonment Rate You can't optimize what you're not measuring correctly. Start with your North Stars. Layer in context. Then drill into the drivers + funnel. That's how you build a storefront that converts visitors into profit, not just orders. What metric surprised you most on this list?

Improve your store's conversion rate, retention rate, and overall profitability within 30 minutes. No clickbait, I promise. Product performance analysis is one of the least used strategies in DTC marketing. Here are my steps to do product performance data without any additional tools - Using Shopify, Meta & Google Analytics 1. Go into Shopify Analytics and pull "Sales by product" for revenue 2. Download the "Product orders and returns" report for the return percentage 3. Go into GA4 and pull the Ecommerce purchases report for items views, items added to the cart, and items purchased. 4. Go into Meta and pull the Catalog ads product report by ad spent and purchases to find product level CAC. 5. Find the margin on the products, or use the Shopify "Profit by-product" report if the margin was available in the reporting. Merge all the above reports either on SKU or product name to a single report. It should have -  Product name Revenue Gross Margin % Return Rate % Meta Catalog Ads CAC for product GA4 Item View-To-Purchase Conversion Rate GA4 Item Cart-to-View Conversion Rate Remove or fix -  - Least profitable or low-margin products - High CAC products - High return products - Low view to conversion products Promote aggressively -  - High-margin products - Low CAC products - Low return products - High conversion rate products It's best not to look at a single metric but to get to the contribution margin at the product level. Single Product Contribution Margin = (Unit Revenue * (1- Return %)) - (Meta CAC) - (Unit Shipping + Payment Costs) It's a simple formula, you can increase complexity with more variables. The goal is to do a comparative analysis. Using Pareto principles, 80% of sales will be coming from 20% of the products. Use this information to align your inventory and sales projections. Most DTC brands have a higher count of SKUs than needed. Trim your SKU count. You can use ROI Hunter & Reveal by Omniconvert for in-depth product analysis. Both tools have fantastic reports for doing product analysis. In Reveal, you can get the product level NPS, segment performance analysis, and retention rate per product. Again, it's a simple analysis that needs to be done once a quarter. It will help you remove the dead weight. #dtc #ecommerce I am Rahi Jain I use my PCC (Product, Customer, Communication) framework to maximize growth and profitability for ecommerce brands. Have helped generate $34M+ for 50+ ecommerce brands. Built a $20M ecommerce store.