Who Made a Repeat Purchase Within 30 Days or 60 Days?

Who made a repeat purchase within 30 or 60 days?

To find who made a repeat purchase within 30 days or 60 days, start from your order export, group orders by customer, identify each customer’s first and second order dates, then calculate the number of days between those two purchases.

A customer belongs in the 30-day repeat purchase group if their second order happened 0–30 days after their first order. A customer belongs in the 60-day repeat purchase group if their second order happened 0–60 days after their first order. Unless you intentionally split the windows, the 60-day group includes the 30-day repeaters.

The useful output is not just a repeat purchase percentage. The useful output is a customer-level list that tells you:

• Which customer bought for the first time.
• What they bought first.
• When they placed their second order, if they did.
• How many days passed between order one and order two.
• Whether they repeated within 30 days, within 60 days, after 60 days, or not yet.
• What product, discount, cohort, or refund pattern may explain the result.
• Which lifecycle action should happen next.

That gives your team the actual people behind the metric: the customers who came back quickly, the customers who came back late, and the customers who bought once and still need a reason to return.

What this cohort report means

A repeat purchase cohort report groups first-time customers by the timing and quality of their second purchase. Instead of asking, “What is our repeat purchase rate?” it asks, “For this group of first-time buyers, who came back, how fast, after buying what, and with what revenue quality?”

The most common mistake is mixing several retention ideas together. Keep these definitions separate before you build the report.

If you report “60-day repeat purchase” as a single inclusive number, customers who repeated within 30 days are counted inside the 60-day group. If you want cleaner lifecycle segments, split them into mutually exclusive windows: 0–30 days, 31–60 days, after 60 days, and no repeat.

The order-export fields you need

You can answer the core question with a simple order export, but the quality of your diagnosis depends on how much context you include. At minimum, you need a reliable customer identifier, order date, and order ID. To understand why customers came back or did not come back, add product, discount, refund, and channel fields.

Use customer ID when possible. Email is helpful, but it can create duplicates when customers check out with different addresses, typos, or forwarded aliases. If you must use email, normalize it before analysis and watch for duplicate customer records.

For a practical repeat purchase worksheet, create one row per customer with these columns:

• Customer ID or email
• First order ID
• First order date
• First product or SKU
• First product category
• First discount code
• First order value
• First order refund flag and refund amount
• Second order ID
• Second order date
• Days to second order
• Second product or SKU
• Second order value
• 30-day repeat flag
• 60-day repeat flag
• Acquisition cohort month or week
• Lifecycle recommendation
• Priority score

The goal is not to build a perfect data warehouse before taking action. The goal is to create a trustworthy customer list that your retention, lifecycle, and merchandising teams can use immediately.

Find each customer’s first-to-second purchase gap

Build the report as a map from raw orders to one customer-level row. This keeps the work operational: every row should end with a customer, a repeat timing flag, and a next action.

1. Sort orders by customer and purchase date

Sort the export by customer ID first, then by order date from oldest to newest. If the same customer has multiple orders on the same day, sort by order timestamp or order ID as a secondary field.

2. Rank each customer’s orders

Assign an order number inside each customer record: first order, second order, third order, and so on. You only need the first and second order for this report, although later orders are useful for LTV and loyalty analysis.

3. Pull first-order details into the customer row

For each customer, capture first order date, first product, first category, first discount code, first order value, refund flag, and acquisition cohort month. This becomes the baseline for understanding what created the customer relationship.

4. Pull second-order details into the same row

Next, capture second order date, second product, second order value, and second order refund flag. Customers with no second order should remain in the report with blank second-order fields. Do not remove them; they are the no-repeat segment your lifecycle team needs.

5. Calculate days to second order

Conceptually, the formula is simple: second order date minus first order date. If the customer has no second order, leave days to second order blank or label it “No second order.”

6. Create repeat timing flags

Add flags that classify each customer:

• 30-day repeat: days to second order is 0–30.
• 60-day repeat: days to second order is 0–60.
• 31–60-day repeat: days to second order is 31–60, if you want a mutually exclusive segment.
• Late repeat: days to second order is greater than 60.
• No repeat yet: no second order exists.

Once this is built, you can filter directly to answer the original question: “Show me every customer who made a repeat purchase within 30 days” or “Show me every customer who made a repeat purchase within 60 days.”

Build 30-day, 60-day, late-repeat, and no-repeat segments

The same customer-level report should produce several operating segments. Each segment means something different, and each one deserves a different action.

This segmentation is more useful than a single blended repeat purchase rate. A 30-day repeater and a high-AOV no-repeat buyer should not receive the same message.

Diagnose which first products and cohorts are helping or hurting retention

Once every customer has a repeat timing flag, group the report by the variables that explain customer quality. The key is to look beyond first-order volume. A product that drives many first orders may still be a weak retention product if customers do not return, return late, or refund often.

Start with these diagnostic cuts:

• Acquisition month or week: which cohorts repeated faster or slower than normal?
• First product or SKU: which entry products create second orders?
• First product category: which category introduces customers who stay?
• Discount code: do heavy-discount customers repeat, or do they buy once and disappear?
• Campaign or source: which acquisition sources produce customers who actually come back?
• First-order AOV band: do low, mid, or high first-order values lead to better second-order behavior?
• Refund status: are low-repeat cohorts also refund-heavy?
• Location or customer tag: are certain regions, wholesale tags, loyalty tags, or customer types behaving differently?

The product-level questions to answer

• Which first-purchase products have the strongest 30-day repeat behavior?
• Which products create a second order within 60 days, even if they do not create a fast 30-day repeat?
• Which products sell well on the first order but fail to create a second order?
• Which second products are most commonly bought after each first product?
• Which first products have abnormal refund rates?
• Which products should be promoted as acquisition products, and which should be reserved for existing customers?

A teardown-style example

Imagine Product A drives the highest number of first orders. At first glance, it looks like the acquisition winner. But when you group by first product, Product A has weak 60-day repeat behavior and a noticeable refund flag. That means it may be good at getting attention but poor at creating durable customers.

Product B has fewer first orders, but customers who start with Product B repeat within 30 days more often and rarely refund. Product B may be a better first-purchase product for quality growth, even if it does not produce the biggest top-line order count.

Now add discount code. Heavy-discount customers repeat late or not at all, while customers who used a moderate first-order offer return faster. That tells you the issue may not be retention messaging alone. The acquisition offer may be attracting low-intent buyers.

Finally, one acquisition month shows both abnormal refunds and weak second-purchase behavior. That cohort deserves a deeper review: what campaigns ran, which products were featured, whether shipping or inventory issues occurred, and whether the promise in ads or emails matched the product experience.

Turn each segment into email, SMS, replenishment, or winback action

The report is only useful if it changes what your team does next. Use the customer-level segment to decide who receives loyalty messaging, who needs a second-order push, who needs education, and who should be excluded from standard promotions until you understand refund or product-fit issues.

The same logic applies whether your team acts in email, SMS, direct mail, customer support, or sales outreach. The segment should determine the message. A customer who repeated in 12 days deserves reinforcement. A customer who refunded their first order deserves diagnosis. A customer who spent heavily and never came back deserves priority.

Checks before you trust the report

Before you use the report for lifecycle decisions, run a few quality checks. Repeat purchase analysis is simple, but small data choices can change the answer.

After the checks, save the report as a recurring operating view. Refresh it by cohort month, first product, discount code, source, refund flag, and lifecycle segment. The output should tell your team exactly which customers came back within 30 or 60 days, which first purchases created that behavior, and which customers need the next retention action.