Growth Analytics for Ecommerce: The Metrics and Framework That Drive Revenue
Ecommerce growth analytics is fundamentally a repeat purchase problem. Most ecommerce brands can acquire customers profitably in the short run — the growth question is whether those customers come back, how often, and whether the acquisition cost is justified by lifetime value. This guide covers the analytics framework, metrics, and tools that drive ecommerce growth beyond the first order.
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Open tool →Why ecommerce growth analytics is different from SaaS
In SaaS, retention is contractual — customers pay monthly and churn by cancelling. In ecommerce, retention is behavioral — customers return (or don't) based on experience and habit. This makes ecommerce cohort analysis structurally different: you're measuring purchase frequency distributions rather than subscription renewal rates, and the signal is weaker (the fact that someone didn't buy in month 2 doesn't mean they've churned — they might buy in month 5). Good ecommerce analytics requires understanding cohort purchase patterns, RFM segmentation, and the channels that produce the highest-LTV customers.
The three ecommerce metrics that predict long-term growth
30/60/90-day repeat purchase rate: what % of first-time buyers make a second purchase within 30, 60, and 90 days. This is the leading indicator of LTV — brands with a 30-day repeat rate above 25% have fundamentally different economics than those below 15%. LTV by acquisition channel: the 12-month or 24-month revenue per customer, segmented by where they came from. This is the only correct basis for CAC decisions. Average order value (AOV) trend: whether customers are buying more per order over their lifetime. A declining AOV trend indicates category exhaustion or channel mix shift.
Cohort analysis for ecommerce: what to measure and how
An ecommerce cohort is a group of customers who made their first purchase in the same time period (typically a calendar month). The most important cohort analyses: revenue per cohort over 12 months (shows LTV trajectory), repeat purchase rate by cohort (shows whether the product drives repurchase or is a one-and-done purchase), and cohort performance by acquisition channel (do Facebook-acquired customers retain differently than Google-acquired?). The most common mistake is analyzing aggregate retention rates instead of cohort curves — aggregate rates mix old cohorts (who've had time to repurchase) with new cohorts (who haven't), making trends unreadable.
The ecommerce analytics stack
The modern ecommerce analytics stack: Shopify/WooCommerce analytics (transaction-level data), a product analytics tool like Amplitude for behavioral tracking (page views, product views, cart events), Google Analytics 4 for SEO and ad attribution, a marketing attribution tool (Triple Whale, Northbeam, or Rockerbox) for multi-touch attribution and ROAS by channel, and a data warehouse (BigQuery) once you're joining sources for LTV analysis. The most important integration is between your ecommerce platform and your marketing attribution tool — without clean order attribution, you can't calculate LTV by channel accurately.
Ecommerce growth analytics checklist
- 30/60/90-day repeat purchase rate tracked by monthly cohort
- LTV calculated at 12 months, segmented by acquisition channel
- Cohort curves built as actual cohort curves, not aggregate retention averages
- CAC by channel compared to LTV at 12 months (not blended averages)
- Payback period calculated by channel
- RFM segmentation built (recency, frequency, monetary value)
- Cart abandonment rate tracked by checkout step and device type
- Email capture rate and email-driven repeat purchase rate tracked
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Talk to Adasight →Frequently asked questions
What is a good repeat purchase rate for ecommerce?
60-day repeat purchase rates vary significantly by category. Consumables (supplements, coffee, pet food): 30–50% is achievable. Fashion/apparel: 20–30% at 60 days is strong. Furniture/home: 10–15% at 60 days is typical (high-ticket, less frequent). Benchmarking within your category is more useful than cross-category comparison.
How do you calculate ecommerce LTV?
The simplest approach: take the average revenue per customer over 12 months for cohorts that have been active for at least 12 months, multiplied by your gross margin. More sophisticated approaches use survival analysis to project LTV beyond the observation period. For D2C brands with 3+ years of data, 36-month LTV is the more reliable planning figure.
What is the best analytics tool for ecommerce?
For most D2C ecommerce brands: Amplitude or Mixpanel for behavioral product analytics (session, funnel, retention), Google Analytics 4 for SEO and ad attribution, and Triple Whale or Northbeam for multi-touch marketing attribution. The Shopify analytics dashboard is good for transactional metrics but insufficient for behavioral analysis. At scale, a data warehouse (BigQuery + dbt) that unifies all sources is the right long-term investment.
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