E-commerce Analytics Checklist

Set up GA4 with ecommerce event tracking to capture product views, add-to-cart, purchase, and refund events. Enable product-level attribution.

Use Shopify's official GA4 app to auto-track events—manual implementation is error-prone and delays data insights by days.

Install Shopify pixel to unify behavior from your store, landing pages, and ads. Enable tracking across all owned domains.

Test pixel firing with browser DevTools before assuming data is flowing. Misconfigured pixels are the #1 cause of attribution gaps.

Tag all products with consistent attributes (category, price tier, margin). Enable filtering by product performance in analytics.

Use product IDs that persist across inventory updates. Dynamic IDs cause analytics fragmentation and mask underperforming items.

Define and fire custom events for wishlist adds, product comparisons, video watches, or review interactions. Measure engagement beyond purchases.

Create dashboards in GA4 or Triple Whale showing conversion rate, AOV, traffic source, and cart abandonment. Review weekly.

Include week-over-week and year-over-year comparisons to spot seasonal trends and marketing impact quickly.

Record and review sessions of users who add items but don't buy. Look for friction points like unexpected fees, confusing copy, or payment errors.

Focus on users who reached checkout—they're closest to purchase. Fixing checkout friction yields faster ROI than homepage redesigns.

Test 1-page checkout vs. multi-step. Use clear, benefit-driven CTA copy. Allow guest checkout to remove signup barriers.

Even 1-step reductions can lift conversion by 3–5%. A/B test changes in GA4 to measure impact statistically.

Test product image layouts, price positioning, social proof placement, and CTA button placement. Measure impact on conversion and AOV.

Run tests for at least 2 weeks to account for day-of-week variance. Analyze by traffic source—what works for paid may not work for organic.

Use Klaviyo or Shopify Email to send automated recovery emails at 1hr, 24hr, and 72hr after abandonment. Include product images and time-limited discounts.

Segment by product type—abandoned luxury items need different messaging than abandoned basics. Personalization lifts recovery rate by 10–20%.

Deploy exit-intent popups offering discounts or free shipping when users show abandonment signals. Use countdown timers or low-stock badges.

Create cohorts: first-time buyers, repeat customers (2–5 purchases), VIPs (10+ purchases). Track metrics separately for each.

Use Shopify's built-in customer segments or sync to Klaviyo. VIPs often represent 20% of customers but 80% of revenue—treat them differently.

Create automated flows: order confirmation, shipping notification, post-delivery follow-up, product recommendations, win-back campaigns. Segment by purchase value.

Test discount incentives vs. free shipping in post-purchase offers. Free shipping often outperforms percentage discounts for repeat rate.

Define CLV as (average order value × repeat purchase rate × customer lifespan). Track by traffic source, product category, and customer cohort.

Use CLV to set rational paid acquisition budgets. If CLV is $500, you can afford to spend up to $100–150 per customer acquisition.

Offer points, tiered discounts, or exclusive access based on repeat purchases or spending. Use Shopify apps like Smile or LoyaltyLion.

Data shows loyalty members have 25–30% higher repeat purchase rates. Start simple—even a basic point system drives engagement.

Use Klaviyo or Attentive to send SMS about new products, flash sales, and VIP offers. SMS has 98% open rate vs. 20% for email.

Limit SMS to 2–4 per month—oversending causes opt-outs. Segment by purchase history so VIPs see different offers than casual browsers.

Use GA4 attribution models (linear, last-click, data-driven) to see which channels contribute to conversions. Review multi-touch journeys.

Most e-commerce uses last-click, which overvalues paid and undervalues brand awareness. Test data-driven attribution to see true channel value.

Define a UTM naming convention (source, medium, campaign, content). Apply across Google Ads, Meta, email, affiliates, and organic posts.

Use lowercase, hyphens (not underscores), and no spaces. Inconsistent UTMs create data pollution that makes attribution impossible.

Track ROAS (revenue ÷ ad spend) separately for paid search, display, social, and video. Set target ROAS goals per channel.

ROAS targets vary by channel: search often achieves 3:1–5:1, social 1:1–2:1. Compare to benchmarks in your industry, not globally.

Track CPA (ad spend ÷ new customers) by channel, campaign, and product. Identify channels with high CAC that are unprofitable.

Compare CPA to CLV for each channel. If CPA is 30%+ of CLV, that channel is unsustainable. Cut or optimize immediately.

Cohort users by source. Calculate repeat purchase rate and CLV for each. Paid social users might have lower LTV than organic search.

A channel with high CAC but high CLV (loyal customers) is often more valuable long-term than cheap traffic that never returns.