Customer Retention Metrics for eCommerce: What to Track and How to Calculate Each

An eCommerce brand should track five retention metrics: retention rate, churn, returning customer rate, time between orders, and revenue from retention. Together they roll up into one number, lifetime gross profit to CAC. Hayes Media, an eCommerce growth agency for DTC and Shopify brands, judges retention on that ratio and builds email, SMS, loyalty programs, and subscribe-and-save to move it.

Most guides to customer retention metrics are written for software companies, where a customer cancels on a known date. In a store, customers just stop buying, or they're between orders, and from the outside the two look the same. This guide gives you the store version of each metric, with the formula and a worked example. Every example uses made-up numbers.

Which customer retention metrics should an eCommerce brand track?

Here's each of the five in a line, plus the scoreboard they feed:

Retention rate. The share of customers from one period who bought again in the next.

Churn. The share who've stopped buying. For a store you measure it as lapsed customers, since nobody cancels.

Returning customer rate. The share of the people who ordered this period who had ordered before.

Time between orders. How many days usually pass between one order and the next, taken from your own order data.

Revenue from retention. The share of revenue that comes from customers who've bought before.

Lifetime gross profit to CAC. What one customer returns in margin over their life with you, divided by what you paid to acquire them. This is the scoreboard the other five feed.

Why don't SaaS retention metrics fit a store?

Software retention formulas assume you can see the day a customer leaves. A shopper who bought a candle in March sends no signal at all. They might come back next month, next year, or never.

So you can't count who left. You count who came back inside a window you choose. Too short, and normal shoppers who are simply between orders look like lost customers. Too long, and a real drop stays hidden for months. That's why time between orders sits in the middle of this list: it's how you pick the window for everything else.

The exception is subscribe-and-save. If part of your revenue bills on a schedule, that part does have a cancel date, and the software formulas work for it. Track it separately from one-off buyers.

What's the customer retention rate formula?

The classic formula is:

Retention rate = (customers at the end of the period minus new customers gained in the period) divided by customers at the start of the period, times 100.

Worked example with made-up numbers: you start the quarter with 5,000 customers, gain 1,200 new ones, and end with 5,400. That's 5,400 minus 1,200, which is 4,200. Divide by 5,000 and you get 84%.

The catch for a store is the word "customers". To count customers at the end of the period, you need a rule for who still counts as one. Without a cancel date, that rule is a guess, and the answer moves with it.

So most stores are better served by a version built on orders:

Store retention rate = customers who ordered in the last period and ordered again in this period, divided by customers who ordered in the last period.

Worked example with made-up numbers: 4,000 customers ordered in the first quarter. Of those, 1,000 ordered again in the second quarter. Your retention rate for the second quarter is 1,000 divided by 4,000, which is 25%.

Make each period at least as long as your usual time between orders, or normal reorder gaps will read as lost customers.

How do you measure churn when nobody cancels?

For one-off buyers, churn becomes a lapse rule. You decide how long a customer can go without ordering before you treat them as lapsed, and you set that line from your own data, not from someone else's benchmark.

Lapsed rate = customers whose last order is older than your lapse line, divided by all customers who've ordered at least once in the time you're reviewing.

Worked example with made-up numbers: your order data shows that most second orders happen within 70 days of the first, so you set the lapse line at 70 days. Of 8,000 customers who ordered in the past year, 5,200 haven't ordered in the last 70 days. Your lapsed rate is 65%.

Plenty of people buy once, so this number often looks high. The useful part is the trend and the split: watch whether it grows month over month, and check which first products or offers produce the most lapsed customers.

For subscribe-and-save, use the standard version:

Subscription churn = subscribers who cancelled in the month, divided by active subscribers at the start of the month.

Worked example with made-up numbers: 600 active subscribers on the first of the month, 48 cancel by the end of it. That's 8% monthly subscription churn.

What's returning customer rate, and how's it different from repeat purchase rate?

Returning customer rate = customers who ordered this period and had ordered before, divided by all customers who ordered this period.

Worked example with made-up numbers: 3,000 customers ordered in October, and 1,200 of them had bought from you before. Your returning customer rate for October is 40%.

Repeat purchase rate asks a different question: of the customers in a window, how many ordered twice or more. Its formula and windows are in our repeat purchase rate guide.

Returning customer rate has one trap. It moves when acquisition moves. Spend more on ads, bring in more first-time buyers, and the rate drops even if exactly the same number of past customers came back. Always read it next to the count of returning customers, not on its own.

For reference, one retention account on Hayes Media's retention site shows a "54% Returning Customer Rate". That's a single client result that Hayes published itself, not a benchmark for your store.

How do you find time between orders from your own data?

Never borrow this number from someone else. Your product, price, and pack size decide how fast people run out, so only your data can tell you.

Here's how to work it out from a Shopify order export or any order list:

Step 1. Remove test orders, fully refunded orders, and internal orders.

Step 2. Group the orders by customer and sort each customer's orders by date.

Step 3. For every customer with two or more orders, count the days between the first and second order. Do the same for the second and third.

Step 4. Take the median of each gap, not the average. A few customers who come back after a year will drag the average up and make your gap look longer than it is for most people.

Step 5. Look at the spread as well. Find the gap by which most of your second orders have already happened. That's a sensible place to draw your lapse line.

Worked example with made-up numbers: across 2,400 customers with a second order, the median gap from first to second order is 38 days, and most second orders have happened by day 70. The median gap from second to third is 31 days. That tells you three things: reminders should go out before day 38, a customer quiet past day 70 is a win-back case, and people who make it to a second order reorder faster after that.

If you want to see how that gap shifts by acquisition month, run it inside an eCommerce cohort analysis, which tracks time to second order for each group of new customers.

How do you measure revenue from retention?

Revenue from retention = revenue from customers who had ordered before, divided by total revenue for the period.

Worked example with made-up numbers: your store took $400,000 in September, and $140,000 of it came from returning customers. Revenue from retention is 35%.

Pull this from your store's order data. Email and SMS reports credit revenue to messages using that tool's attribution settings, and the store number doesn't depend on them.

Like returning customer rate, this share rises when acquisition slows and falls when it speeds up. Read it next to the dollar amount from returning customers. If the share is up but the dollars are flat, retention didn't improve; new customer revenue dropped.

This is the metric Hayes Media points to first when it shows retention work. One account on its retention site went from "0% to 59% of Revenue from Retention" alongside "+140% Total Revenue YoY". Hayes published that result on its own site.

How do these metrics roll up into lifetime gross profit to CAC?

Each metric above changes one input in the scoreboard. Retention rate, churn, and time between orders decide how many orders a customer places. Revenue from retention shows whether those orders add up to real money. Then:

Lifetime gross profit per customer = average order value times orders per customer times gross margin.

Lifetime gross profit to CAC = lifetime gross profit per customer divided by CAC, which is what you pay to acquire one new customer.

Worked example with made-up numbers: AOV is $60, customers place 1.8 orders in their first 12 months, and gross margin is 60%. That's $60 times 1.8 times 0.6, or $64.80 of lifetime gross profit. With a CAC of $40, the ratio is 1.62. Lift orders per customer to 2.1 through better retention, change nothing else, and lifetime gross profit becomes $75.60. The ratio moves to 1.89, with the same ad spend.

That's why retention decides what you can afford to pay for a new customer. For the full build, including which costs go into gross margin, see the customer lifetime value formula. For working on both sides of the ratio, read how to improve LTV to CAC, and for the wider picture, our guide to eCommerce unit economics.

How should you read the metrics together?

No single one of these numbers is a verdict. Read them in pairs:

Returning customer rate down, returning customers flat. You brought in more new buyers. That's an acquisition change, not a retention problem.

Retention rate down, time between orders longer. Customers are still coming back, just slower. Check pricing, pack size, stock, and whether your reminders still land before the median gap.

Lapsed rate up for one first product. That product may be bringing in buyers who don't come back. Look at what happens after that first order.

Revenue from retention up, lifetime gross profit flat. Returning customers may be buying on heavy discounts. Check margin on repeat orders, not just revenue.

What moves these numbers?

Retention metrics move when the whole experience gives people a reason to buy again. Loyalty starts before the first order, in the product, the site, the delivery, and every message along the way. The second order is where it shows up in the numbers.

The levers Hayes Media works on are email, SMS, loyalty programs, and subscribe-and-save. Flows reach customers at the moments your reorder data points to, and subscribe-and-save turns a product people run out of into a scheduled order. Our eCommerce retention marketing playbook covers how those pieces fit together.

When does this not apply?

Very new stores. If most of your customers bought for the first time recently, they haven't had time to reorder, so retention will look low. Wait until your first customers have passed your expected reorder gap before reading them.

Products people buy once. A store selling mattresses or wedding rings won't see many second orders, whatever it does. Judge the business on first order gross profit against CAC instead.

Pure subscription businesses. If every customer is on a billed plan, use subscription churn and the classic formula directly. The lapse rules in this guide are for one-off buyers.

How does Hayes Media work on retention metrics?

Hayes Media builds and runs retention for eCommerce brands: email, SMS, loyalty programs, and subscribe-and-save. We work in Klaviyo for most accounts and have experience in most other email and SMS platforms. Email on its own is a full engagement, and you can see what's included on our email and SMS service page.

Hayes Media's retention site lists "8 yrs Retention expertise", "$500M+ Client revenue driven", and "15x Average ROI". Those figures and our case studies are published by Hayes on its own sites. Hayes has no third-party review profile and no Klaviyo partner listing.

Hayes Media works with brands doing at least $100K per month in revenue. It's a guideline, not a gate: a smaller brand with a clear budget set aside to scale and real order volume already qualifies. One-time email projects start from $2,000 USD, and retainers for ongoing full management start from $4,000 USD per month, scoped on the discovery call.

If you want to know which of these numbers is holding your store back, we'll audit your retention metrics, your time between orders, and your flows on the call. Book a discovery call. No onboarding fees. No lock-in contracts. No junior marketers.

Sources

Hayes Media retention site. Source for "8 yrs Retention expertise", "$500M+ Client revenue driven", "15x Average ROI", and the account result "0% to 59% of Revenue from Retention", "+140% Total Revenue YoY", and "54% Returning Customer Rate". Fetched 2026-11-09.