Choosing the RPR Window: 90, 180, or 365 Days for DTC
Picking the wrong measurement window makes repeat purchase rate meaningless. Here's how to match the window to your category's purchase cycle in under five minutes.
Quick answer
Match the RPR window to your category's natural purchase cycle: 90 days for consumables (coffee, supplements, skincare refills), 180 days for apparel and accessories, 365 days for considered or durable goods (footwear, home, electronics). Using a 365-day window for a coffee brand inflates RPR; using 90 days for a winter-coat brand crushes it to near zero. Pick the window once, then hold it constant across every reporting period.
RPR measurement window
The time period over which you count whether a customer placed a second order — 90, 180, or 365 days is standard for DTC.
The repeat purchase rate (RPR) measurement window is the lookback period you use to decide whether a first-time buyer counts as a repeat customer. A customer who bought once in March is a repeater under a 365-day window if they buy again by the following March — but a churner under a 90-day window if their second order doesn't land by June.
The window isn't a stylistic choice. It has to match how often someone in your category would realistically reorder. Get it wrong and the number you put in front of the team is either flattering noise or unfair pessimism — and every cohort comparison after that inherits the same distortion.
This page exists because the most common mistake teams make with the Repeat Purchase Rate Calculator isn't the arithmetic — it's the window. Below: how to pick yours in under five minutes, how to spot a wrong one, and a category-by-category cheat sheet.
Why the window has to match your purchase cycle
Every category has a median time between orders — what cohort analysts call the inter-purchase interval. For a daily-use supplement it's roughly 30-45 days. For a pair of running shoes it's closer to 9-12 months. The window should be at least one full interval, ideally 1.5x, so the median customer has had a realistic chance to come back.
If your window is shorter than the interval, you're measuring how many customers reorder unusually fast — not how loyal the base is. If it's much longer, you're crediting the brand for repeats that took a year to materialise, which inflates the number and hides churn problems happening right now.
The most expensive mistake
A coffee subscription store using a 365-day window saw RPR climb from 38% to 62% year-over-year and assumed retention was improving. It wasn't — they'd simply accumulated more time for old cohorts to register a second order. Switching to a fixed 90-day window showed RPR was actually flat at 41%. The team had been celebrating a measurement artefact for two quarters.
How to detect you've picked the wrong window
Three signals tell you the window is off. First: RPR drifts month over month even when nothing about the product, traffic, or post-purchase flow has changed. That's almost always window-related — older cohorts are simply having more time to mature.
Second: the number looks suspiciously high (above 55% for a non-subscription brand) or implausibly low (under 8% for a consumable). Third: when you pull the median days-between-orders from your Shopify export, it sits outside your chosen window. If your median repeater comes back at day 140 and you're using a 90-day window, you're filtering out the majority of real loyalty.
Pick your window: category cheat sheet
Use the table below as a starting point, then validate by pulling 12 months of order data and checking the median interval between first and second orders for buyers who did repeat. If that median falls inside your chosen window, you're calibrated.
Recommended RPR measurement window by DTC category
| Category | Typical reorder cycle | Recommended window | Healthy RPR range |
|---|---|---|---|
| Coffee, tea, pet food, supplements | 30-60 days | 90 days | 35-55% |
| Skincare, haircare, cosmetics refills | 45-90 days | 90 days | 28-45% |
| Apparel, accessories, jewellery | 90-180 days | 180 days | 20-35% |
| Footwear, bags, eyewear | 180-365 days | 365 days | 18-30% |
| Home goods, kitchenware, decor | 180-365 days | 365 days | 15-25% |
| Electronics, furniture, appliances | 365+ days | 365 days | 8-18% |
Hybrid catalogues (a beauty brand selling both daily moisturiser and a once-a-year fragrance) should default to the window of the bestseller, not the average. The bestseller drives the order volume that defines the cohort's behaviour.
Once you've picked: lock it in
The window is a measurement convention, not a tuning knob. Pick it once, document it ("RPR = % of customers who placed a 2nd order within 180 days of their first"), and use the same window forever after. The next step is pulling the raw repeat-customer counts from Shopify — that page picks up where this one ends.
What to do if your category spans two windows
Some stores genuinely straddle two cycles — an apparel brand whose accessories reorder every 90 days but whose outerwear reorders every 12 months. The fix isn't a single compromise window. Report two RPR figures, one per product family, and track them side by side.
If leadership demands a single headline number, weight by revenue contribution: if accessories drive 70% of orders, the headline RPR uses the 90-day window. Always disclose the window in the same chart title — the number on its own is meaningless without it.
Frequently asked questions
A 90-day window counts a customer as a repeater only if their second order lands within three months of the first — strict, fast, useful for consumables. A 365-day window gives them a full year, so the same customer base will always produce a higher RPR figure. The two numbers are not comparable; pick one and stick with it.
90 days. Coffee has a 30-45 day reorder cycle for regular drinkers, so a 90-day window covers roughly two purchase opportunities and gives a realistic loyalty signal. A 180 or 365-day window will inflate the number and obscure churn happening in month two.
Yes, 180 days is the default for apparel, accessories, and jewellery brands. Most apparel buyers don't reorder within 90 days (too soon for wardrobe refresh) but most loyal customers do come back within six months. 365 days works for outerwear-heavy catalogues where seasonal cycles dominate.
You can, but you have to restate historical periods using the new window before comparing. Otherwise the trend line will show a jump that's pure methodology, not real change. Treat window changes like accounting policy changes — disclose them and recompute back at least 12 months.
For subscriptions, RPR usually isn't the right metric — use retention rate at month 1, 3, 6 instead, because the reorder is automatic. Reserve RPR for one-time-purchase SKUs in your catalogue, and use the consumable-appropriate 90-day window for those.
Yes. For cohort analysis, the window matters even more because you're comparing month-1 cohorts to month-12 cohorts. Use a fixed window measured from each customer's first order date (not from a calendar date), otherwise older cohorts will always look better simply because more time has passed.
Shopify's native Returning Customer Rate is calculated lifetime — every customer who has ever placed a second order. That's not a true RPR and it always trends upward as the store ages. Pull raw repeat-customer counts from Shopify Admin and apply your own fixed window for an accurate figure.
Not directly, but they're related. If your CAC payback is 6 months, a 180-day RPR window tells you whether repeat revenue is arriving before customers pay back. Aligning the two windows makes the metrics talk to each other in finance reviews.
Use the category default from the cheat sheet above and accept the RPR figure will be a lower bound (your oldest cohort hasn't had a full window to mature). Recompute monthly and expect the number to stabilise around month 9-12 once enough cohorts have aged through.
It affects interpretation, not the window itself. A Q4-heavy brand will see RPR spike in Q2 reporting because Q4 buyers had time to return. Keep the window fixed and report RPR as a trailing 12-month average to smooth seasonal noise rather than shrinking the window.
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