What repeat purchase rate actually measures
Repeat purchase rate is the percentage of your customers who have placed more than one order. That definition sounds trivial until you try to compute it on a real Shopify export, which is when most operators discover their dashboard, their Klaviyo report, and their finance team are all measuring slightly different numbers.
RPR is the cleanest single signal for whether your retention program is doing anything. Email open rates move with subject line tweaks. Repeat customer rate moves with cohort timing. LTV moves with margin assumptions you can adjust to taste. RPR is stubborn. It only goes up when customers actually come back, and it stays flat or drops when they don't. That's the property that makes it useful for diagnosis.
It also gets confused with two other metrics that aren't quite the same. Repeat customer ratemeasures the share of customers in a defined cohort who returned for at least one additional order during a fixed window. It's cohort-bounded. Customer retention rate measures the percentage of a starting customer base still active at the end of a period. It's retention math. RPR is portfolio-level: it doesn't care when a customer first bought, only whether they ever bought twice.
The three formulas (and when each is right)
Most operators only know one RPR formula. Which one they know tells you a lot about which mistake they'll make. Below are the three you actually need.
1. Order-based RPR
(Number of repeat orders / Total orders) × 100. This is the marketing-decision formula. Repeat orders are any order placed by a customer who has at least one prior order in your system. If you ran 4,200 orders last quarter and 1,260 of them came from previously-acquired customers, your order-based RPR is 30%.
Use this when you're benchmarking the impact of a specific retention campaign or a flow change. It's sensitive to recent behavior, which is what you want when you're tuning. The order-based formula will move first when a campaign works, which means it's the right number to put on a weekly dashboard.
2. Customer-based RPR
(Customers with 2+ orders / Total customers) × 100. This is the board-reporting formula. It collapses the entire customer base into a single statement: what fraction of people who ever bought from you came back at least once.
Use this when you're telling a strategic story about retention over time. It's less reactive than the order-based version because every customer counts equally regardless of how recently they ordered. The customer-based number is what most published DTC benchmarks measure, so use this when you're comparing your brand against external ranges.
3. Window-based RPR
(Customers acquired in period X who reordered within window Y / Customers acquired in period X) × 100. This is the cohort formula, and it's the one most operators skip because it's harder to compute. You pick an acquisition cohort (say, all customers acquired in February) and measure what percentage placed a second order within a fixed window (say, 90 days).
The window-based formula is the one you use to forecast. It tells you which cohorts are converting on the second purchase and at what speed. Two brands with the same customer-based RPR can have dramatically different window-based numbers, and the brand with the faster second-purchase window will out-LTV the slower one every time. If you're only tracking one of the three, make it this one. Your repeat purchase rate calculator run with rolling 90-day windows will show you the trajectory your aggregate number is hiding.
DTC repeat purchase rate benchmarks by vertical
The ranges below are 12-month customer-based RPR for brands in the $1M–$50M revenue band, synthesized from Klaviyo benchmark reports, ReCharge subscription data, and operator surveys conducted across 2023 to 2025. They are directional. Your AOV, your subscription attach rate, and your acquisition mix will shift you inside the range.
| Vertical | Median 12-mo RPR | Top-quartile RPR | Typical second-purchase window |
|---|---|---|---|
| Skincare & beauty | 28% – 36% | 48% | 45 – 75 days |
| Supplements & wellness | 38% – 52% | 64% | 30 – 45 days |
| Apparel | 16% – 24% | 36% | 90 – 180 days |
| Home goods | 12% – 22% | 32% | 120 – 240 days |
| Food & beverage | 32% – 44% | 58% | 21 – 35 days |
| Subscription boxes & repeat consumables | 54% – 68% | 78% | N/A (recurring) |
A pattern worth noticing. The brands with the highest RPR also have the shortest second-purchase windows. That isn't a coincidence. The customer is consumed by the product (literally, in supplements and food) inside a window short enough that the purchase decision is still fresh. By the time apparel and home goods customers come up for a reorder, they've had time to forget your brand, encounter five alternatives, and reset their consideration set. The product cycle determines the ceiling, and the second-purchase window is where the ceiling is set.
Why your number varies (more than the benchmark suggests)
Three inputs determine where your brand lands within a vertical range, and they matter more than the vertical label itself.
Repurchase cycle.A 30-day consumable will have radically higher RPR than a 180-day consumable in the same vertical. If your category sits inside “supplements” but your product is a once-a-year immunity bundle, your RPR will look closer to the apparel range. Benchmark against your cycle, not your vertical.
AOV bracket.Higher-AOV brands ($120+) usually run lower RPR for the same category because the customer is buying a larger quantity per order and going longer between replenishments. The trap is reading lower RPR as a retention problem when it's actually a bundling effect. Look at per-customer revenue per year, not just RPR, before concluding your retention is broken.
Acquisition source mix.Customers from organic search and referral routinely produce 1.5–2.5x the RPR of customers from paid social. If your aggregate RPR is below your vertical median and your paid mix is heavy, that's not a retention problem in the abstract. It's a channel-quality problem that retention can't fully fix. Cut the worst channel before you launch a new flow.
Five ways to improve repeat purchase rate (ordered by impact)
We've watched brands try all of these. Here's how they actually rank when you measure the lift over 12 months across a comparable cohort.
1. Win the second-purchase window
The single most decisive intervention. Set a triggered flow that fires 14, 21, and 35 days after the first order, with content that's specific to the product they bought (not a generic “come back” email). Brands that get this flow right lift RPR by 8–14 points within a quarter. Brands that don't are leaving the easiest retention win on the table.
The reason this works has nothing to do with email copy. It works because the second purchase is the hardest one. Once a customer makes it, the probability of a third is dramatically higher than the probability of a second was. A second-purchase flow doesn't persuade the customer; it removes friction from a decision the satisfied ones were already inclined toward.
2. Run product-level cross-sell post-purchase
Most brands send a generic “you may also like” email. Brands that lift RPR send a product-specific cross-sell tied to the SKU the customer bought. If they bought a daytime serum, the cross-sell is the nighttime version, not a random mask. Specificity is the unlock. Expect 3–5 points of RPR lift over a year when this is wired correctly.
3. Build a subscribe-and-save lane into your top SKUs
A subscription option on the top 3 SKUs lifts category-level RPR materially in consumable categories. The brands that resist this because “our customers don't want subscriptions” usually have a UX problem rather than a demand problem. Subscribe-and-save converts 8–14% of first-time customers in supplements, food, and beauty when the option is offered at checkout with a meaningful discount.
4. Re-engage the 90-day window before it closes
Customers who go quiet between days 60 and 120 are the most recoverable. They haven't reset their consideration set yet. A win-back flow during this window recovers 4–8% of would-be-lapsed customers, which compounds into a 2–3 point RPR lift over a year. After day 180, recovery rates drop by half. Time matters.
5. Stop over-discounting first-time customers
A 25% off welcome offer trains the customer to expect 25% off forever. Brands that switch from welcome discount to welcome credit (a $10 credit on the next order, redeemable within 30 days) see RPR lift 2–4 points because the discount is conditional on the second purchase rather than a sunk inducement to make the first. The change is uncomfortable for the first quarter as new-customer conversion dips slightly, then the math compounds.
The common mistakes (and how to avoid each)
Three mistakes account for most of the bad RPR numbers in DTC board decks.
Reporting customer-based RPR while pricing as if you knew the cohort number. Aggregate customer-based RPR can look strong even when recent cohorts are softening, because historical heavy buyers continue to drag the average up. The fix is to always report both the aggregate number and the trailing 90-day cohort RPR side by side. If they diverge by more than 5 points, the recent number is the truth.
Counting refunds and exchanges as repeat purchases. A returned-then-rebought order looks like a second purchase in a raw Shopify export. If you don't filter for net orders before computing RPR, your number is 2–4 points higher than reality, and the inflation is concentrated in apparel and home goods where return rates are highest.
Comparing RPR to a stale benchmark. Post-iOS-14 acquisition mix shifted across most DTC brands. Brands now run heavier on paid social with shorter-lived customers, which compresses category averages. A 2021 benchmark will overstate where the bar sits today. The ranges in this post reflect 2024 data; recheck quarterly if you're using RPR to set acquisition budgets.
What to do with your number
Look up your number against the table above, then ask three questions in this order. Is your second-purchase window inside your vertical's typical range? If not, the second-purchase flow is your fix. Is your paid acquisition mix above 60% of new customers? If so, channel quality is dragging your aggregate RPR and retention work alone won't close the gap. Are you discounting first-time customers heavily? If so, that's where the easiest RPR lift sits, hidden in plain sight.
Once you know where you are, plug your numbers into the repeat purchase rate calculator to see all three formulas side by side. Then read the LTV benchmarks by vertical piece to see how a 5-point RPR move translates into LTV. If you want the full retention-improvement playbook, the 12 tactics to increase LTV guide is the natural next step. To move the number automatically rather than by hand, a Shopify customer retention app scores who is most likely to reorder and routes them into Klaviyo on a schedule.
Frequently asked questions
Is repeat purchase rate the same as customer retention rate?
No. Customer retention rate measures whether a known cohort of customers is still active over a defined window, usually expressed as the percentage who placed at least one order in the period. Repeat purchase rate measures whether any of your existing customers came back to buy again, regardless of when they first purchased. CRR is a cohort question. RPR is a portfolio question. Brands often confuse the two and end up reporting the easier one when investors ask for the harder one.
How long after launch can a new DTC brand calculate RPR reliably?
You need at least one full repurchase cycle of order history, which for most DTC categories is 90 to 180 days. Before that you're measuring noise. Skincare and supplements can produce a defensible RPR after 90 days because the consumable cadence is short. Apparel and home goods usually need a full year before the number stabilizes. Calculating RPR on a brand with three months of data and a 12-month natural repurchase cycle gives you a number that has nothing to do with your actual retention.
What's the relationship between repeat purchase rate and LTV?
RPR is the input. LTV is the output. A 5-point lift in RPR moves LTV more than any equivalent change to AOV or margin, because the lift compounds across future cohorts. The math: doubling RPR from 20% to 40% roughly doubles average orders per customer, which roughly doubles LTV at constant AOV. Doubling AOV is rarely available without breaking the product, but doubling RPR is a multi-year campaign that almost every DTC brand has runway to execute.