TL;DR
Repeat purchase rate is the percentage of customers from a starting cohort who purchase again within a defined time window — most commonly measured at 30, 60, and 90 days for D2C brands. For consumables, healthy 90-day repeat rate is 25–40%; for durables it is 5–15%. Repeat purchase rate is one of the cleanest leading indicators of brand strength and unit-economics health, because it forecasts LTV before LTV is realised.
What is repeat purchase rate?
Repeat purchase rate is the percentage of customers from a defined starting cohort who place at least one additional order within a fixed time window. The standard D2C reporting cadence is 30/60/90 days — measuring how many customers from the cohort that placed their first order in (say) February also placed a second order by 30, 60, and 90 days later.
It is the most-used early-cohort signal in D2C because it shows up faster than LTV (which requires multi-quarter observation) but rhymes with eventual LTV closely enough to forecast unit economics within the first quarter of a cohort's life.
How to calculate it
Repeat purchase rate is calculated cohort-by-cohort: customers acquired in a defined period divided into the subset who repeat-purchased before the cutoff.
30-Day Repeat Purchase Rate = (customers from cohort who placed a 2nd order within 30 days) / (total customers in cohort) × 100 Example: 1,000 first-time customers in February → 180 placed a 2nd order by March 31 → 30-day repeat rate = 18%
Benchmarks
Repeat purchase benchmarks vary heavily by category. Consumables (skincare, supplements, food) have higher repeat rates than durables (apparel, hard goods).
| Category | 30-day | 60-day | 90-day |
|---|---|---|---|
| Consumables (subscription-eligible) | 10–20% | 20–30% | 30–45% |
| Consumables (one-time) | 8–15% | 15–25% | 20–35% |
| Apparel / accessories | 5–10% | 10–18% | 15–25% |
| Durables (home, hard goods) | 2–5% | 4–8% | 6–12% |
Common pitfalls
- 1. Comparing across cohorts with different acquisition channels. Cohorts acquired via Meta retargeting have higher repeat rates than cohorts acquired via Google Shopping cold traffic. Always segment by acquisition channel before comparing.
- 2. Including subscription auto-renewals in 'repeat' counts. Subscription auto-renewal is a different mechanism than active repeat purchase. Report them separately or the metric stops being a brand-strength signal.
- 3. Using calendar-window repeat rather than cohort-window repeat. Calendar-window math (% of customers active in March who were also active in February) double-counts customers and produces inflated repeat rates. Cohort math is the only honest version.
How Fairview computes repeat purchase rate
Fairview's Operating Dashboard joins Shopify order data with ad-platform attribution to compute 30/60/90-day repeat rates per cohort, segmented by acquisition channel. Subscription auto-renewals are tracked as a separate cohort series so brand-strength signal isn't conflated with subscription mechanics.
Related concepts
60-day repeat rate is the most-used checkpoint within this metric family. LTV is the eventual cumulative revenue; repeat rate is the leading indicator. AOV compounds with repeat rate to drive LTV.
At a glance
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Frequently asked questions
Why measure at 30/60/90 days?
These windows align with the typical purchase cycles for most D2C categories — fast enough to provide an early signal (30 days) and slow enough to capture meaningful repeat behaviour for moderate-cycle goods (90 days). For very long-cycle categories (mattresses, appliances), use 180 and 365 days instead.
Should you include returns?
Yes — exclude returned orders from the 'repeat' count. A customer who placed a second order and returned it is not a repeat customer for unit-economics purposes. Most D2C brands report this both ways and reconcile.
What's a healthy 30-day repeat rate?
For consumables: 8–20% depending on category and channel mix. For apparel: 5–10%. For durables: 2–5%. Always benchmark against your own historical cohorts and against category peers — cross-category comparisons are misleading.
Sources
- Shopify D2C benchmarks (2025)
- Klaviyo D2C cohort report
- Fairview customer data (D2C, 2025)
Fairview is an operating intelligence platform that computes 30/60/90-day repeat purchase rates per cohort, segmented by acquisition channel and product mix — making it possible to forecast LTV from first-quarter cohort behaviour. Start your free trial →
Siddharth Gangal is the founder of Fairview. He built the cohort repeat-rate layer after watching D2C operators chase headline-LTV numbers based on six-month-old cohorts when 90-day repeat-rate signals from this-quarter cohorts were already showing the unit economics had broken — they were optimising on lagging data while the leading indicator was screaming.
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