Repeat Purchase Rate Calculator
Calculate your repeat purchase rate from unique and repeat customers, then see the annualized revenue and profit impact of lifting RPR by 1, 3, or 5 points.
Repeat Purchase Rate Calculator
A tool that converts unique and repeat customer counts into a repeat purchase rate, then projects the revenue impact of a 1-, 3-, or 5-point lift.
Repeat purchase rate (RPR) is the share of customers in a given window who bought more than once. The calculator on this page turns two raw counts — unique customers and customers with two or more orders — into an RPR percentage, then layers your average order value and gross margin to show the annualized revenue and gross-profit upside of incremental improvements.
It is the operational companion to RPR measurement: instead of debating whether retention matters, you can see in seconds what a 3-point RPR lift is worth to your store this year, and decide whether a post-purchase flow, subscription test, or replenishment reminder is worth the build.
Calculate your repeat purchase rate and revenue lift
Unique customers in window
Distinct customers who placed at least one order in your measurement window (e.g. last 12 months).
Repeat customers (2+ orders)
Of those unique customers, how many placed two or more orders in the same window.
Average order value
$
Gross AOV across all orders in the window.
Gross margin
%
Product margin after COGS, before marketing and fulfilment.
Avg extra orders per repeat customer / year
On top of their first order, how many additional orders a repeat customer places annually.
Repeat purchase rate
25.0%
Annual revenue from a +1 point RPR lift
$11,250
Annual revenue from a +3 point RPR lift
$33,750
Annual gross profit from a +5 point RPR lift
$33,750
Use a 12-month window for stable comparisons. Shorter windows (90 days) systematically understate RPR because many customers haven't had time to come back yet.
Two inputs drive the percentage; three more turn it into euros. The calculator multiplies your customer base by an assumed lift, then by the orders each repeat customer adds per year, then by AOV — and finally by margin to show profit instead of just top-line.
The point isn't a perfect forecast. It's to anchor the conversation: when a 3-point RPR lift is worth €34k a year to your store, the calculus on whether to ship a subscription option or a Klaviyo win-back flow changes.
The formula behind the calculator
RPR = Repeat Customers / Unique Customers
Repeat Customers
Customers with 2+ orders
Distinct customers in the window who placed at least two orders.
Unique Customers
Total unique customers
All distinct customers who placed at least one order in the same window.
RPR
Repeat purchase rate
Share of customers who bought more than once, expressed as a percentage.
A beauty brand on Shopify reviews the last 12 months of order data.
Unique customers: 10,000
Repeat customers (2+ orders): 2,500
→ RPR = 2,500 / 10,000 = 25%
One in four customers returned at least once. To project revenue impact, multiply the desired point lift by unique customers, by annual extra orders per repeat buyer, and by AOV.
The revenue projection assumes the lifted customers behave like your existing repeat buyers — same AOV, same repeat cadence. That's a reasonable planning assumption for incremental retention work, but it overstates the case if your test attracts deeply discount-driven buyers who never return at full price.
What a healthy RPR looks like
Typical 12-month repeat purchase rate by vertical
| Vertical | Bottom quartile | Median | Top quartile |
|---|---|---|---|
| Beauty & personal care | 22% | 32% | 45% |
| Apparel & accessories | 15% | 24% | 36% |
| Food & beverage (CPG) | 30% | 45% | 60% |
| Home & lifestyle | 12% | 20% | 30% |
| Consumer electronics | 8% | 14% | 22% |
| Pet supplies | 28% | 42% | 58% |
Consumables outperform considered purchases — pet food and skincare have repeat cycles measured in weeks, while a sofa or a laptop is bought once every few years. Benchmark against your vertical, not against an industry-wide average.
How to use the result
Treat the lift projections as a prioritisation tool. If a +3 point RPR is worth more than the engineering and content cost of a post-purchase flow, that flow moves up the roadmap. If it isn't, you have an objective reason to deprioritise retention work this quarter and focus on acquisition or AOV instead.
Run the calculator quarterly with the same 12-month trailing window. Watching the trend matters more than the absolute number — a store moving from 22% to 26% RPR over three quarters is compounding loyalty even if the headline still trails the vertical median.
Pick your window before you pull the data
RPR is window-sensitive. A 30-day window will show 5-10% RPR for almost any store; a 12-month window typically shows 20-40%. Compare like-for-like windows when benchmarking yourself across quarters, and never compare your 90-day RPR to a competitor's annual figure.
Frequently asked questions
For online retail, 20-30% over a 12-month window is typical. Consumables (beauty, food, pet) can sustain 40%+; considered-purchase categories (electronics, furniture) sit closer to 10-15%. Benchmark within your vertical.
Twelve months is the standard. It's long enough that most customers who would repeat have had the chance to, and it smooths seasonality. Use 90-day RPR only as an early-warning leading indicator, not as your headline number.
In practice, yes — both measure the share of customers who bought more than once in a window. Some teams reserve 'repeat purchase rate' for an order-weighted version (repeat orders / total orders), but the customer-weighted definition used in this calculator is the more common reporting standard.
RPR is the input; LTV is the output. A higher RPR, holding AOV and margin constant, mechanically raises LTV because the average customer contributes more orders before churning. If LTV is flat while RPR rises, check whether AOV on repeat orders is falling.
Shopify's 'returning customer rate' uses orders rather than customers as the denominator. The calculator here uses customers. Both are valid; pick one definition and stick to it so your trend line is comparable quarter to quarter.
The reliable levers are post-purchase email and SMS sequences timed to the product's replenishment cycle, subscription or auto-replenish options on consumables, a tight win-back flow at day 60-90 after last purchase, and removing friction on second checkout (saved payment, one-tap reorder).
No — it assumes the lifted customers behave like your current repeat buyers for one year. For longer-horizon planning, multiply the annual figure by your average customer tenure in years, or model it inside an LTV calculator instead.
Yes, but the numbers will look unusual: subscription stores routinely show 60-80% RPR because the business model guarantees a second order. The lift projections still work for incremental retention tests like reducing churn or upsells to higher tiers.
RPR is binary per customer (did they buy more than once, yes or no). Purchase frequency is the average number of orders per customer in the window. A store can have a moderate RPR with very high frequency among the repeat segment — both metrics together describe loyalty better than either alone.
Monthly for operational dashboards using a rolling 12-month window, quarterly for board reporting. Avoid weekly tracking — the metric moves too slowly to react to, and the noise will distract from real signal.
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