Customer Retention

Time Between Orders: How to Calculate and Use TBO

First published May 10, 2023Updated October 20, 20266 min read

Content & Copywriter

Published: May 10, 2023Updated: Oct 20, 2026

Time between orders: two shopping bags joined by a blue dotted arc over an hourglass

Quick Answer

Time between orders (TBO) is the number of days between two consecutive orders placed by the same customer, and its average tells you when that customer's next order is due. The metric matters because repeat buying drives profit: in financial services, a 5% increase in customer retention produces more than a 25% increase in profit [Bain & Company, 2001]. This guide gives you the formula, worked examples, the correct way to calculate the average time between orders, and how to use it to predict the next purchase.

Key Takeaways

Subtract the previous order date from the current order date to get the time between orders for one pair of orders.
Divide the days from first to last order by the number of orders minus one, not by the number of orders, to get a customer's average.
Report the median gap next to the mean, because a few customers with very long gaps pull the store average up.
Add a customer's typical gap to their last order date to predict the next purchase, and time your message just before it.
Flag customers whose current gap runs well past their usual rhythm as at risk of churn, then start a win-back flow.
Calculate the metric by product category, since consumables and durables reorder on very different cycles.

2,500+ brands use Omniconvert 70,000+ experiments run 13 years of CRO expertise 5% more retention, 25%+ more profit in financial services [Bain & Company, 2001]

Time between orders (TBO) is the number of days between two consecutive orders placed by the same customer with the same brand. A customer who orders on April 1 and comes back on April 15 has a time between orders of 14 days. Track it for every repeat customer to learn when each will likely buy again.

Stores use it to time messages, spot customers drifting away and plan stock. It works best next to customer lifetime value (CLV) and average order value (AOV), as part of a wider customer behavior analysis. This guide covers the formula, the average and how to predict the next purchase.

What is time between orders?

Time between orders is defined as the time that passes between two consecutive orders placed by one customer with the same brand, usually counted in days. Other names for it are time between purchases (TBP) and repeat purchase interval (RPI).

The metric needs at least two orders, so it describes repeat customers only. The pattern of a customer's gaps is their buying rhythm. A short, steady gap points to a loyal customer who buys out of habit. A gap that keeps growing points to a customer who is losing interest or buying elsewhere, often weeks before they stop ordering.

How to calculate time between purchases

To calculate the time between purchases, subtract the date of the previous order from the date of the current order. Do this for every pair of consecutive orders in a customer's history.

The formula is: Time between orders = date of current order − date of previous order.

An export of customer ID and order date from your store platform or CRM is enough (for SaaS, see ideas to increase SaaS sales). Take Jane, a repeat customer of an online fashion store:

Order #1001: March 1
Order #1017: April 15
Order #1032: May 8

The first gap is April 15 − March 1 = 45 days. The second gap is May 8 − April 15 = 23 days. Jane is buying faster, a good sign.

How to calculate the average time between orders

Average time between orders is defined as the total number of days between a customer's first and last order divided by the number of gaps, which is the number of orders minus one. For the whole store, average every gap between consecutive orders and report the median too.

The formula for one customer is: Average time between orders = (date of last order − date of first order) ÷ (number of orders − 1).

Jane's three orders span 68 days with two gaps between them, so her average is 68 ÷ 2 = 34 days. Dividing by the number of orders instead (68 ÷ 3 = 22.7 days) is a common mistake: it understates the gap and makes every reminder arrive too early.

For the whole store, collect every repeat customer's gaps. Suppose seven gaps come out as 20, 25, 30, 30, 35, 40 and 180 days. The mean is 360 ÷ 7 = 51.4 days, but the median is 30 days. One slow customer moved the mean by three weeks, so report both. For a rough estimate, divide 365 by your purchase frequency per year: 2.4 orders per customer gives about 152 days. It includes one-time buyers, so it reads longer than the real gap among repeat customers.

Time between orders vs purchase frequency, recency and repeat purchase rate

Time between orders measures the gap between orders, purchase frequency counts the orders, recency counts the days since the last order, and repeat purchase rate measures the share of customers who come back at all.

Source: Omniconvert
Metric	What it measures	Unit	Question it answers
Time between orders	Gap between two consecutive orders	Days	When is this customer's next order due?
Purchase frequency	Orders per customer in a period	Orders per year	How often do customers buy?
Recency	Days since the last order	Days	How long ago did this customer buy?
Repeat purchase rate	Share of customers with two or more orders	Percent	How many customers come back at all?

Recency is the "R" in an RFM score. On its own, it cannot tell you whether 40 days since the last order is normal. Next to the customer's usual gap, it can: 40 days is fine for someone who orders every 60 days, and a warning for someone who orders every 20. Whether customers return at all is covered in how to calculate repeat purchase rate.

How to use time between orders to predict the next purchase

To predict the next purchase, add a customer's typical time between orders to the date of their last order. Time your message just before that date, and treat a gap that runs well past it as a churn signal.

Find each customer's typical gap
Use the median of their past gaps. With only two orders, use the median gap for the same product.
Set an expected next order date
Expected next order = last order date + typical gap. Jane (May 8 + 34 days) is due around June 11.
Send the message before the due date
Send a reminder or a relevant recommendation a few days early, and hold back offers before that.
Set an overdue threshold
Pick a multiple of the typical gap, for example 1.5 times, and test it on your data. At 1.5 times, Jane is overdue on June 28.
Move overdue customers into a win-back flow
Ask what changed, offer help or an incentive, and follow the lost customer win-back playbook.

The most common failure here is building the reminder flow on the store-wide mean. With the gaps above, a team sets its "time to reorder" email at day 51, the mean. Most customers reorder around day 30, so the email arrives three weeks late, after they reordered or bought from a competitor. The flow converts poorly, and the team blames the copy when the problem is the timing. Use the median, set it per product category, and check it against real reorder dates.

Timing is a hypothesis, so test it. AliveCor used Omniconvert to run a structured A/B testing program and achieved a 21 percent lift in conversion rate, a 5 percent lift in revenue per visitor, and 94 percent statistical relevance across their experiments [Omniconvert, AliveCor case study]. Apply the same A/B testing discipline to retention: send the reminder at day 25 to half a segment and at day 30 to the other half, and keep the version that brings the next order sooner.

Why time between orders matters for retention, inventory and marketing

Time between orders shows when each customer is due to buy again, which helps you keep customers longer, stock the right products, and send fewer but better-timed messages.

Customer retention. Small gains in retention move profit: in financial services, Bain & Company found that a 5% increase in customer retention produces more than a 25% increase in profit [Bain & Company, 2001]. See more customer retention statistics. A growing gap is an early sign a valuable customer is leaving. It belongs in any customer retention strategy (see customer loyalty vs customer retention), and every retained customer adds to your eCommerce profit margin.

Inventory. Knowing when repeat customers are due lets you forecast demand for what they reorder, so you avoid stockouts that send them elsewhere and excess stock that ties up cash.

Marketing cadence. Customers close to their next order respond best to an offer. Target them, leave the others alone, and you send fewer emails while driving more repeat orders. Over-mailing risks spam flags (see email spam statistics).

Segmentation. Short, medium and long gaps make a simple first customer segmentation, even before you build RFM segments. Short-gap customers suit loyalty programs and cross-sells; long-gap customers may need reassurance or a better customer experience.

Omniconvert Commander (formerly Nexus) does this from your order history. It builds RFM segments, flags churn risk, calculates True Profit per order and per customer, and pushes the segments directly to Meta Ads, Google Ads and Klaviyo.

Limitations of the time between orders metric

Time between orders ignores one-time buyers, shifts with the seasons, is skewed by outliers, and says nothing about order value or satisfaction. Read it next to other metrics.

Seasonality: gaps shrink around the holidays and stretch in quiet months; compare like seasons, or use cohort analysis.
One-time purchases: it says nothing about durables or customers who never return.
Outliers: one yearly buyer or one five-orders-a-week buyer distorts the mean.
Data quality: guest checkouts and duplicate accounts split one customer into several.
No context: a long gap may come from a move, a gift or a bad experience.

Time between orders tells you when behavior changed. Interviews or a short survey tell you why.

Time between orders FAQs

1What is time between orders?

Time between orders (TBO) is the number of days that pass between two consecutive orders placed by the same customer with the same brand. If a customer orders on April 1 and again on April 15, the time between orders is 14 days. The metric is also called time between purchases or repeat purchase interval.

2How do you calculate time between purchases?

Subtract the date of the previous order from the date of the current order. For example, an order on January 1 at 10:00 AM followed by an order on January 3 at 2:00 PM gives a time between purchases of 2 days and 4 hours. Most stores round to whole days and calculate the gap for every pair of consecutive orders in the customer's history.

3How do you calculate the average time between orders?

For one customer, divide the days between the first and the last order by the number of orders minus one. Three orders on March 1, April 15 and May 8 span 68 days with two gaps, so the average time between orders is 34 days. For the whole store, collect every gap between consecutive orders from repeat customers and report the median next to the mean.

4How can you predict a customer's next purchase?

Add the customer's typical time between orders to the date of their last order. A customer who orders every 34 days on average and last ordered on May 8 is due around June 11. Send the reminder a few days before that date, and flag the customer as at risk if the gap grows well past their usual rhythm, for example 1.5 times it.

5What is the difference between time between orders and purchase frequency?

Purchase frequency counts how many orders a customer places in a period, such as 4 orders a year. Time between orders measures the gap between those orders in days, such as 90 days. They describe the same buying rhythm from two sides, but time between orders tells you when the next order is due, so it is the one you use to time messages.

6What is a good average time between orders?

There is no single good number, because the gap depends on how fast the product is used up. Consumables such as coffee, pet food or skincare reorder in weeks, while furniture or electronics reorder in months or years. Compare each customer with their own past gaps and with customers who buy the same products, and work to keep the gap stable or shorter over time.

7How does Omniconvert Commander help you use time between orders?

Omniconvert Commander is an AI eCommerce growth engine that reads your order history, groups customers into RFM segments by recency, frequency and monetary value, and flags customers at risk of churn. It pushes those segments directly to Meta Ads, Google Ads and Klaviyo, so customers who are due or overdue for their next order get the right message at the right time.

Put a Due Date on Every Repeat Customer

Export your order history this week and calculate the gap between every pair of consecutive orders. Group the gaps by your main product categories and write down the median for each one. That number is the reorder cycle your messages should follow.

Then give every repeat customer an expected next order date: last order plus their typical gap. Send the reminder just before it, and move anyone who runs well past it into a win-back flow. See how Omniconvert Commander segments customers by recency and frequency.

Oana Predoiu

Content & Copywriter

Oana Predoiu is a content writer and copywriter who turns ideas into compelling narratives. She writes about how data shapes customer experience, A/B testing, user testing, CRO, and sales, and enjoys researching the qualitative side of customer behavior.

Know which customers are due for their next order

Omniconvert Commander reads your order history, builds RFM segments, flags churn risk and calculates True Profit per customer. Push the segments to Meta Ads, Google Ads and Klaviyo and reach each customer at the moment they are ready to buy again.

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