Repeat Purchase Rate

Today, I'm revealing how focusing on your repeat purchase rate can exponentially boost your eCommerce brand's profitability—by shortening order intervals, increasing average order value, and reducing return rates.

Definition: Repeat purchase rate (RPR) is the percentage of customers who buy from you more than once within a defined period. In ecommerce it is best measured on a cohort of first-time buyers: of everyone who placed a first order in a given month or quarter, what share placed a second order within a set number of days?

The more orders a customer places, the shorter the time between orders. This acceleration leads to a higher frequency of orders and more orders per calendar year.

Limited drops can be a great way to further disrupt your customers' usual ordering cycle. Limited drops are a set of products that are only available for a limited time and in a limited quantity.

Here is a checklist for creating a powerful drop strategy:

Orders from existing customers have an average 20% higher AOV than orders from new customers. This effect increases with each subsequent order. The biggest jump is between order 1 and 2.

This is because initial purchases are often a trial run for customers to get to know your brand. This makes it even more important to exceed expectations. From service to product quality to processes.

Subsequent orders tend to have more products per basket and higher average product values that lead to the 20% increase of AOV.

This also means that if your AOV drops one day, first check the percentage of existing customers on that day.

The probability of a 3rd order is on average 60% higher than a 2nd order, and this effect can also be observed with follow-up orders. A 4th order is 30% more likely than a 3rd order. And so on...

Orders from existing customers have 40% lower return and cancellation rates. This translates into more profit per order.

Working on better customer retention leads to a combinatorial explosion with incremental impact on your brand's profit and growth.

You get more orders in less time with higher basket values. On average, one retained customer generates the profit of 4 new customers due to the combined exponential effects mentioned above.

By the way, the fastest growing brands generate 80%+ of their revenue from their existing customer base. New customer acquisition only fuels the existing customer machine.

If your brand's repurchase rate is 70%, it's quite possible that 70% of your customers only buy from you once! 🤯

Sounds contradictory?

The returning customer rate (or repurchase rate) metric only tells you how many of the orders in a period came from new customers and how many came from existing customers.

However, it completely ignores the fact that the likelihood of a repeat order continues to increase:

The probability of a follow-up order is more likely to be as follows

This means that the more history you have, the higher your repeat rate will automatically be.

A small percentage of very loyal customers will push the monthly percentage of repeat orders higher and higher.

So you think your repeat rate is improving, but it's not. – Well, you don't actually know, which is even worse!

In such an example, you could have a repeat customer rate of 70% and at the same time lose 70% of your customers after the first purchase.

And this is not an isolated case. It is the reality for most brands.

That's why it's so important to look at the repeat purchase rate for each step of the purchase journey.

Understanding which step in the customer journey is critical to long-term retention is essential.

There are three versions worth knowing. Only the last two survive the problem described above.

A brand acquires 4,000 first-time customers in Q1. Within 180 days, 1,040 of them place a second order. Of those, 520 place a third.

In the same month, the store ships 10,000 orders, 6,500 of them from customers who had ordered before. Shopify reports a returning customer rate of 65%.

Both numbers are correct. They answer different questions. The 65% says the loyal core is busy. The 26% says three out of four new customers never come back.

Shopify's customer cohort analysis gives you a starting view of returning customers by first-order month. The customer cohorts guide explains how to read that table.

It depends on what you sell and how long you wait. Treat these as directional ranges, not targets:

The better benchmark is your own cohorts over time. If this year's cohorts repeat less than last year's at the same age, your acquisition is bringing in weaker customers, even if revenue is up.

Identify High AOV Segments: Identify customers who spend more than average on their first purchase. Analyze their demographics, what products they buy, which marketing channels & campaigns they came from, and what led to the high first purchase.

Adjust Customer Acquisition: Feed your findings back into your marketing strategy:

→ Leverage products that drive high AOVs for your customer acquisition

→ Target budgets to channels that attract this ideal customer profile.

→ Create customer segments in tools like RetentionX and push them back to your meta campaigns as audiences that can be used as signals. This will tell Meta what kind of customers you are looking for.

Boost AOV Strategies: Implement tactics such as upselling, cross-selling, and bundling to increase AOV. (I have several posts on this topic - check out my timeline)

For a full playbook, see how to increase the repeat purchase rate.

One exception for replenishable products: after a big stock-up promotion, judge repeat behavior stockpile-adjusted. A customer who bought three months of product isn’t late at day 60. Your customers have inventory too shows how to calculate Customer Inventory Days and net out promo pull-forward.

Divide the number of customers who placed more than one order by the total number of customers in the same group and time window. For a cleaner read, use a cohort of first-time buyers: count how many placed a second order within, say, 90 or 180 days, and divide by the cohort size. Then compare cohorts at the same age.

It depends heavily on category and time window. Replenishment products such as supplements or coffee usually repeat far more often than apparel, and durable or high-ticket goods repeat least. Use ranges as orientation only. The most useful benchmark is whether your newer cohorts repeat as well as older ones at the same age.

Repeat purchase rate counts customers: the share who buy again. Shopify's returning customer rate counts orders: the share of orders placed by people who had ordered before. Because loyal customers order often, the returning customer rate can be high even when most new customers never return.

The step from first to second order is where most customers drop off. Once a customer has ordered twice, the chance of a third and fourth order is much higher, intervals get shorter and baskets get bigger. Improving the 1→2 step usually has the largest effect on LTV.

Track it monthly by acquisition cohort, using a fixed window such as 90 or 180 days. Monthly cohorts show quickly whether a change in channels, offers or entry products is bringing in customers who come back, long before it shows up in total revenue.