How To Calculate Repeat Purchase Rate

Introduction

Repeat purchase rate is one of the clearest, most actionable indicators of customer loyalty for e-commerce businesses. When customers come back, they cost less to convert and deliver more lifetime value. At Growave, our mission is to turn retention into a growth engine for e-commerce brands, and teaching merchants how to measure and act on repeat purchase behavior is central to that mission.

Short answer: Repeat purchase rate is the percentage of customers who make more than one purchase within a defined period. To calculate it, divide the number of customers who placed at least a second order by the total number of unique customers in the same period, then multiply by 100. This simple ratio shows how effectively you turn first-time buyers into repeat customers.

This post explains what repeat purchase rate measures and why it matters. We’ll walk through the math, show how to calculate the metric in spreadsheets and in SQL, discuss cohort and intra-order analyses, flag common measurement mistakes, offer practical optimization strategies, and show how our retention suite helps you raise repeat purchase rates while keeping your stack lean. Our main message: measuring repeat purchase rate is easy, and acting on it consistently is one of the fastest, most reliable ways to increase customer lifetime value.

What Repeat Purchase Rate Actually Measures

Definition and core idea

Repeat purchase rate measures the share of customers who return and buy again. It captures a very specific behavior: making at least one additional purchase after the first. Because it focuses on actual purchase behavior rather than intent or engagement, it’s a practical, transaction-driven proxy for loyalty.

What it is not

• It is not the same as retention rate, which often measures active customers over multiple periods or subscriptions.
• It is not average orders per customer — that metric looks at total orders divided by total customers, while repeat purchase rate only counts customers who bought at least twice.
• It is not lifetime value, but it drives LTV: higher repeat purchase rates usually translate into higher average lifetime revenue.

When the metric is most useful

Repeat purchase rate is especially useful for stores that sell consumables, replenishable goods, or products with frequent purchase cycles. It’s still valuable for many retail categories because it highlights the effectiveness of post-purchase experience, reactivation, and cross-sell efforts.

The Basic Formula

Repeat Purchase Rate = (Number of Repeat Customers ÷ Total Number of Customers) × 100

• Number of Repeat Customers: customers who have placed two or more orders in the period.
• Total Number of Customers: all unique paying customers who placed at least one order during the period.

This formula is intentionally simple. Its value comes from consistent application and careful interpretation.

Step-By-Step Calculation (Explained)

Below is a clear, stepwise explanation written as prose and short bulleted checkpoints that you can follow in a spreadsheet or analytics tool.

• Choose the period you want to measure (day, week, month, quarter, year, or custom window). Keep the window consistent for trend analysis.
• Identify unique customers who made at least one purchase in that period. Exclude accounts that never completed a purchase.
• Within that same customer set, count how many made at least one additional purchase (a second order) either within the same period or within the window you define for repeat behavior—be explicit about which you choose.
• Divide the repeat-customer count by the total-unique-customer count and multiply by 100 to express as a percentage.

Important nuance: when you define the “repeat” window differently than the reporting period, you should label the metric accordingly (for example, “Monthly cohort repeat rate: % of customers acquired in January who repurchased within 90 days”).

Common Time-Window Choices and What They Mean

Different time windows tell different stories. Choose one that aligns with product life cycle and customer purchase cadence.

• Short windows (weekly to monthly) show how quickly newly acquired customers convert to a second purchase.
• Medium windows (90 days to 6 months) are useful for consumables and seasonal products.
• Long windows (12+ months) are better for high-consideration or durable-goods businesses where repurchase cycles are long.

You can and should track multiple windows simultaneously. Comparing repeat rates over 30, 90, and 365 days gives a fuller picture of purchase behavior.

Example Calculations (Generic)

Example A — short window:

• Total unique customers in March: 1,200
• Customers who made at least one additional purchase in March or April: 240
• Repeat Purchase Rate = (240 / 1,200) × 100 = 20%

Example B — cohort window:

• Customers acquired in Q1: 5,000
• Those who placed a second order within 90 days: 1,000
• 90-Day Cohort Repeat Rate = (1,000 / 5,000) × 100 = 20%

These illustrations are generic but practical — run the math for your time windows and customer sets to uncover where repeat behavior is strong or weak.

Intra-Order (Order-Step) Repeat Analysis

Looking beyond the first repurchase uncovers deeper retention dynamics. Intra-order analysis tracks conversion rates across order steps:

• From first to second order: what percentage of first-time buyers purchase again?
• From second to third: the share of second-order customers who place a third order.
• And so on.

This view shows where customers drop off across the customer lifecycle. Improving the conversion between the second and third order often yields outsized revenue gains because it compounds across the customer base over time.

Cohort Analysis: A More Accurate View

Why cohorts matter

Aggregated repeat purchase rate can hide shifts in acquisition quality, seasonality, and behavior. Cohort analysis segments customers by their first purchase date and tracks repurchase behavior over time. This isolates the effect of acquisition channels and campaigns.

How to set up cohorts

• Group customers by first purchase date (week, month, quarter).
• For each cohort, calculate the percentage who placed a second order in subsequent time buckets (30, 60, 90 days).
• Visualize as a heatmap or line chart to identify patterns and trends.

Cohorts reveal whether changes in repeat rates are driven by customer quality shifts or by improvements to the product and retention flows.

Calculating Repeat Purchase Rate in a Spreadsheet

Follow this plain-language approach and use the spreadsheet formulas that best suit your setup.

• Export orders with customer ID and order date.
• Create a unique customer list with first order date and total order count.
• Flag customers with order count greater than or equal to two as repeat customers.
• Use a pivot table or COUNTIFS to calculate the counts per period.
• Compute the ratio and format as percentage.

Bulleted checklist for Excel:

• Include columns: customer_id, order_date, order_number, total_orders.
• Use formulas to compute each customer’s total_orders (SUMIFS or pivot).
• Use COUNTIFS to count total unique customers and repeat customers for the period.
• Divide and format as a percentage.

Tip: If you track by acquisition cohort, add the first_order_date column and use it to group customers.

Calculating Repeat Purchase Rate Using SQL

A simple SQL pattern that outlines the logic (adapt to your schema):

• Aggregate orders by customer to compute order_count and first_order_date.
• Filter customers with first_order_date in the target acquisition window (if using cohort analysis).
• Count total customers and those with order_count >= 2.
• Compute the ratio.

General SQL flow explained:

• Use a subquery to count orders per customer.
• Use conditional aggregation to create binary flags for repeat buyers.
• Compute the final percentage with CAST for decimal precision.

If you use a data warehouse, schedule these queries as a daily job and persist results to a dashboard to monitor trend lines.

Using Platform Reports (Shop, Platform, and Caveats)

Most e-commerce platforms and analytics tools provide reports that approximate repeat purchase metrics. There are a few important caveats:

• Reporting definitions can vary. Confirm how “returning customer” is defined.
• Platform-level measurements may double-count accounts or misclassify guest checkouts if not deduplicated.
• If you depend on platform reports, validate them against a direct export of transaction data to avoid measurement bias.

If you want a quick path to install and start tracking with a unified retention solution, you can install Growave from the Shopify marketplace or see our pricing plans to evaluate what’s included. These choices are contextual conveniences for merchants—choose whichever method matches your data and workflow.

Interpreting Your Repeat Purchase Rate

What counts as “good”?

Benchmarks vary by vertical and product type. Typical e-commerce ranges:

• Consumables and replenishable goods often see higher repeat rates.
• Durable and high-consideration items generally show lower repeat rates.
• Many merchants view a repeat purchase rate in the 20–30% range as a solid baseline, while higher rates indicate strong retention.

Always compare your numbers against your own historical trends and similar product categories rather than generic benchmarks.

How to extract the actionable insight

• Pair repeat purchase rate with average order value and customer acquisition cost to estimate return on acquisition.
• Look at repeat rate by acquisition source to find channels that deliver higher-quality customers.
• Track changes after retention campaigns to measure lift.

Common Measurement Pitfalls and How To Avoid Them

• Counting non-paying accounts as customers. Ensure the denominator includes only paying customers.
• Mixing time windows. Be explicit about the window for “repeat” and the reporting period.
• Ignoring multiple accounts. Deduplicate customers across email variations and guest checkouts.
• Relying solely on a single aggregate number. Use cohorts and intra-order analysis for deeper insight.

Fixes:

• Use unique customer identifiers (email or customer ID).
• Maintain a clear naming convention for metrics and time windows.
• Validate platform reports against raw order exports.

How Improving Repeat Purchase Rate Affects Revenue

Repeat purchase rate compounds revenue over time. A higher rate increases second, third, and subsequent orders — the compounding effect of retention means small percentage improvements can drive large lifetime revenue gains. For example, increasing a repeat rate from 20% to 30% can produce materially higher total orders from the same acquisition base, reducing the effective cost of acquisition and improving unit economics.

Tactical Ways To Increase Repeat Purchase Rate

Below are practical strategies that move the needle. We’ll connect each tactic to how a retention platform can support it.

• Create a frictionless reorder experience:
  • Make reorders one click from the order history or a dedicated “reorder” button.
  • Offer subscription options for consumables to automate repurchases.
• Optimize post-purchase flows:
  • Send timely confirmation, shipping, and usage-tip messages.
  • Trigger replenishment reminders before customers run out.
• Reward repeat behavior:
  • Incentivize second purchases with targeted discounts or points.
  • Create multi-tier loyalty programs that grow in value as customers reorder.
• Personalize recommendations:
  • Use purchase history to recommend complementary or replenishment products.
• Use social proof:
  • Collect and surface on-site reviews and user-generated content to reduce hesitation on subsequent orders.
• Segment and re-engage:
  • Identify first-time buyers who haven’t returned and deploy a win-back sequence.
  • Treat VIP repeat buyers differently with exclusive offers to keep them engaged.

Each of these tactics benefits from automation and unified customer data, which avoids app fatigue and helps you act at scale.

How Growave Helps Improve Repeat Purchase Rate

At Growave we build merchant-first retention solutions that simplify execution. Our platform embodies the More Growth, Less Stack approach by consolidating loyalty, reviews, referrals, wishlists, and shoppable social features into a single retention suite. That means fewer integrations to manage and more coordinated campaigns that increase repeat purchases.

• Build a loyalty program that rewards repeat buyers.
  • You can build a loyalty program that rewards repeat buyers to incentivize second and subsequent purchases with points, tiers, and perks.
  • Loyalty programs increase repeat purchase behavior by making customers feel recognized and by giving them a clear reason to reorder.
• Capture and use reviews and UGC to reduce friction.
  • Collect social proof and make it shoppable by integrating reviews into product pages and marketing emails using our social review capabilities.
  • Reviews convert returning visitors and help first-time buyers become confident second-time buyers.
• Reorder and subscription flows:
  • Use automation within the platform to send replenishment reminders and make reorders effortless.
• Referral and wishlist features:
  • Turn satisfied repeat buyers into advocates with referral incentives and let wishlists drive return visits for planned purchases.
• One unified view:
  • Instead of managing multiple tools, use one solution that centralizes rewards, reviews, and referral data so retention campaigns are coordinated and effective.

If you want to explore how these features fit into your store’s setup, you can see our pricing plans or install Growave from the Shopify marketplace. We are trusted by 15,000+ brands and hold a 4.8-star rating on Shopify, and our platform comes with a 14-day free trial on all paid plans so you can test the impact risk-free.

Measurement Maturity: From Manual to Automated

As you scale, move from ad-hoc spreadsheets to automated tracking:

• Start with a clean spreadsheet and scheduled exports to validate core numbers.
• Move to automated SQL queries or a BI platform to compute cohorts and intra-order funnels.
• Adopt a retention suite to centralize customer data, trigger flows, and measure the direct lift from loyalty and review campaigns.

Automation reduces errors and lets you run more experiments. The fewer tools you stitch together, the fewer integration bugs you’ll need to fix—another reason to prioritize a unified solution that replaces multiple single-purpose platforms.

Reporting Cadence and KPIs to Track Alongside Repeat Purchase Rate

Track RPR alongside:

• Average Order Value (AOV)
• Customer Lifetime Value (LTV)
• Customer Acquisition Cost (CAC)
• Purchase Frequency
• Churn or lapse rate for customers who stop purchasing

Report regularly:

• Weekly for tactical checks
• Monthly for trend and cohort analysis
• Quarterly for strategic reviews and to evaluate initiatives

Use visualizations that show cohort trajectories and intra-order funnels to make it easier for stakeholders to see the long-term impact of retention work.

Practical Playbook: From Data to Action

Here’s a pragmatic sequence of steps to calculate, diagnose, and act on repeat purchase rate in a typical merchant workflow.

• Calculate the baseline:
  • Choose a time window and compute the repeat purchase rate for the last 90 days.
• Segment:
  • Break the baseline down by acquisition channel, product category, and customer cohort.
• Identify the weakest step:
  • Use intra-order analysis to see where the drop-off is largest (e.g., 2nd to 3rd order).
• Test a targeted intervention:
  • Example interventions: a loyalty double-points campaign for first-time buyers, a replenishment reminder, or a reorder CTA on the account page.
• Measure lift:
  • Compare cohorts or run an A/B test to see the change in repeat purchase rate.
• Scale winners:
  • Roll out the successful interventions across similar cohorts.

This iterative playbook is efficient when you use a platform that centralizes orchestration, customer profiles, and measurement.

Realistic Benchmarks and Industry Differences

• Consumable goods: tend to have higher repeat rates; aim to beat your historical baseline.
• Fashion & seasonal goods: repeat rates vary; focus on cross-sell and new collection re-engagement.
• Electronics and durable goods: lower repeat rates are normal; prioritize accessory and warranty upsells to increase repeat behavior.

Set internal goals based on historic trends and the economics of acquisition. For many stores, improving repeat purchase rate by just a few percentage points materially raises lifetime value.

Troubleshooting Low Repeat Purchase Rates

If your repeat purchase rate is lower than expected, check these common causes:

• Poor post-purchase experience: slow shipping, confusing communications, or bad packaging reduce the chance of a second order.
• Product-market fit issues: customers aren’t compelled to reorder because the product doesn’t meet expectations.
• Weak reactivation flows: failing to remind customers when they need replenishment or to offer incentives to come back.
• Poor acquisition mix: expensive, one-off promotions that attract coupon-driven buyers who don’t stick around.

Solutions:

• Fix operational issues first (fulfillment, returns, product quality).
• Use targeted reactivation campaigns and loyalty incentives.
• Re-evaluate acquisition channels and test shifting budget toward channels that deliver higher repeat rates.

Integrating Repeat Metrics into Growth Strategy

Repeat purchase rate should be a leading metric in your growth dashboard. When you measure it alongside CAC and LTV, you can answer critical strategic questions:

• Are we attracting customers who become valuable over time?
• Which channels offer the best long-term value?
• What retention investments produce the best ROI?

By making repeat purchase rate a KPI for marketing, product, and ops teams, you ensure cross-functional focus on long-term profitability rather than short-term acquisition volume.

Why Unified Retention Tools Matter

Many merchants suffer from “platform fatigue” — stitching together multiple single-purpose tools for loyalty, reviews, and referrals. That slows experiments, creates inconsistent customer experiences, and increases integration overhead.

Our More Growth, Less Stack philosophy means we design features so that loyalty, reviews, wishlists, referrals, and shoppable social work together. Delivering consistent messages across customer touchpoints and using unified data ensures retention initiatives complement each other and raise repeat purchase rates more effectively than disconnected solutions.

For example:

• Points earned through purchases (loyalty) can automatically trigger referral invitations and voucher offers.
• Reviews captured after delivery (social reviews) can be surfaced in emails that also promote loyalty-based incentives.
• Wishlists feed personalized re-engagement to bring customers back to purchase items they saved.

These coordinated tactics are easier to run and measure when you use one platform rather than multiple integrations.

Quick Implementation Checklist (For The First 90 Days)

• Audit your raw order data and validate repeat calculations in a spreadsheet.
• Set up a loyalty program to reward second purchase behavior; start with a simple, clear offer.
• Create automated post-purchase email flows that include review requests and a reorder CTA.
• Add review collection points and display fresh reviews on product pages.
• Launch a small test campaign to measure lift in repeat purchase rate for a target cohort.

If you want a platform that consolidates these steps and shortens time to impact, see our pricing plans to learn how the features map to merchant needs.

Measurement Examples You Can Run Today

Try these simple analyses this week:

• Cohort repeat comparison: compare customers acquired through two different campaigns and measure their 90-day repeat rate.
• Channel repeat ranking: rank acquisition channels by repeat purchase rate rather than cost per acquisition.
• Reorder latency: measure average days between first and second orders to time replenishment reminders.

These analyses reveal low-effort, high-impact opportunities for targeted retention campaigns.

Closing The Loop: Measuring Impact of Retention Tactics

To prove causality:

• Use control groups or A/B tests where possible.
• Attribute revenue uplift from repeat purchases to specific retention activities through cohort tracking.
• Track the economics: monitor CAC, changes in repeat purchase rate, and resulting shifts in LTV.

A disciplined measurement approach turns guesswork into scalable strategies.

Conclusion

Repeat purchase rate is a straightforward metric that directly connects retention efforts to revenue. Calculating it correctly and using cohort and intra-order analyses lets you spot weaknesses and prioritize high-ROI retention tactics. Our approach at Growave is merchant-first: we help brands drive sustainable growth through retention while offering better value for money and reducing platform complexity with a single unified solution.

If you're ready to test retention features and see the effect on repeat purchases, explore our plans and start your 14-day free trial today. (Hard CTA)

We’re trusted by 15,000+ brands, rated 4.8 stars on Shopify, and committed to helping merchants turn retention into predictable growth — with more growth and less stack.

FAQ

What is the simplest way to start measuring repeat purchase rate?

• Start with a clean export of orders, create a unique customer list, count customers with two or more orders in your period, and divide by total unique customers. Track the metric consistently and validate platform reports against raw data.

How often should we report repeat purchase rate?

• Report weekly for tactical work and monthly for trend analysis. Use quarterly reviews for strategy and to evaluate long-term initiatives.

Should we use a single metric or multiple repeat purchase windows?

• Use multiple windows (30, 90, 365 days) and cohort analysis to capture short-term activation and longer-term loyalty. Combining windows gives a robust view of customer behavior.

How do loyalty programs affect repeat purchase rate?

• Well-designed loyalty programs increase the perceived value of repeat purchases, create habits, and give customers reasons to come back. Track members vs non-members to measure the program’s lift and iterate on rewards and earning thresholds.

Further reading and tools:

• To explore how loyalty and reviews can work together to increase repurchases, check features that let you build a loyalty program that rewards repeat buyers and collect social proof with on-site reviews. For pricing and plan details, see our pricing plans, or if you prefer to install directly you can install Growave from the Shopify marketplace.