Ecommerce second order rate: When to follow up

Your ecommerce second order rate shows how many first buyers purchase again within a stated time. Use its curve to choose a follow-up timing test, alongside your store's buying pattern. It cannot tell you the best day to send an email.

Start with a useful message for someone who has bought once and has not returned. A replenishment reminder, product guide, or relevant suggestion each serves a different need. The timing should fit that need before it fits a benchmark.

What does second order rate mean?

Second order rate is the share of first buyers who make a second purchase within a chosen time. A curve shows that share at several points after the first order. The longer horizons include purchases already counted at the shorter horizons.

Imagine a hypothetical store where one in ten first buyers returns within a month. If another buyer returns later, the earlier result stays the same while the longer result increases. The later point describes everyone who returned by then, rather than only the people who returned during that interval.

Keep the distinction between customers and orders. A frequent buyer can place several orders, but they still represent one person making a second purchase. The share of orders from returning customers answers a different question.

A store can have many returning orders from established regulars while struggling to earn a new buyer's second purchase. Our repeat purchase rate benchmarks explain the broader metric and why its measurement window matters. Here, the question is how long first buyers take to come back.

Read the curve before choosing a send day

The BTN curve shows second purchases continuing beyond the first month. That gives you a reason to keep considering later buyers when planning follow-up. It does not establish that they need more messages or an incentive.

Read each bar as a cumulative result. A higher later bar means a larger share has returned by that horizon. It does not mean that later interval is the busiest time to buy.

The chart also cannot identify a peak purchase day between its labeled horizons. Avoid turning a broad window into a precise schedule. A rising curve is evidence of purchases, while the effect of a message needs its own test.

What counts as good depends on what you sell and how long buyers have had to return. Use the benchmark as context, then compare your own results over matching horizons. A recent group of buyers cannot yet tell you its annual result.

Match the follow-up to the purchase

Choose the reason for contacting the customer before choosing the delay. A reminder helps when another purchase would be useful. Guidance helps when the customer is still learning to use the first purchase.

For a hypothetical coffee store, bean buyers might appreciate a reminder near their usual replenishment point. Grinder buyers might need cleaning advice or help choosing a grind setting. Giving both groups the same reorder message would ignore what they bought.

Look at the gap between first and second purchases in your own store. Consider whether pack size, gift buying, subscriptions, or seasonal demand explain the pattern. These details can change which message makes sense, even when buyers share a first purchase date.

For products that last, a complementary item may be more relevant than another copy of the original purchase. For consumables, an easy route to the same product may help. Treat either choice as a hypothesis until your results support it.

Test an incentive separately if a useful reminder is not enough. A discount might bring an order forward while reducing the profit you would have earned anyway. Compare the profit left across groups before keeping the offer.

Write the audience rule in plain words: customers who bought the relevant product, have marketing permission, and have not ordered again. Remove customers from the reminder when they make another purchase. Check whether another campaign already addresses the same need.

Keep service issues separate from the sales opportunity. A buyer waiting for a delayed delivery may need an update before a replenishment offer. The broader customer retention strategies guide covers other reasons customers might need attention after a purchase.

Test timing against your current approach

Compare a timing change with your current approach while keeping the message and offer the same. Otherwise, a better result could reflect a different discount or product suggestion. Write down what you want to learn before changing the flow.

In the coffee example, the owner could test an earlier replenishment reminder against the existing reminder. Both groups would receive the same product suggestion and buying link. The owner would stop reminders after another purchase and watch for overlapping campaigns.

Randomly assign eligible customers to the timing options where your tools allow it. Give both groups the same amount of time after their first purchase before comparing results. If you also want to know whether the reminder helps at all, include a group without that reminder.

Judge the test by second purchases and the profit left after the offer. Check unsubscribes and complaints alongside sales. More opens can tell you about attention, but they do not establish that more customers bought again.

Keep the first test small in scope. Changing timing, message, discount, and audience together makes the result harder to interpret. If there are too few purchases to make a clear decision, continue observing rather than declaring a winner.

When comparing groups acquired in different months, consider promotions and product availability before crediting the flow. Our ecommerce cohort analysis guide explains how to read those groups over time. A seasonal change can move the curve even if your reminder stays the same.

By the Numbers' cohort analysis reports include a Cohort Repurchase Rate report showing repeat buying within stated horizons. Use that view to understand the purchase pattern, then evaluate the message in your campaign test. The report alone does not show what an email caused.

Your next step is to choose a buyer group, a useful message, and a timing change to test. Write the exit rule and success measure beside them. That turns a curve into a decision you can evaluate without pretending it supplies a universal send day.

How we measured these benchmarks

These benchmarks use anonymized, aggregated data from Shopify stores in the By the Numbers network. No individual store is identifiable from any figure. Stores that use an analytics app are not a random sample of all Shopify stores, so read the figures as a reference point, not as an average for the whole market.

Keep reading

If you want the full picture on cohort analysis and retention, start with the cohort analysis framework.

Related reading: Cohort analysis: Read ecommerce retention, Customer Churn Rate Benchmarks and Customer Retention Rate Benchmarks.

By the Numbers builds this into the dashboard. See cohort analysis.