Purchase Frequency: Formula and How to Increase It
- Divide total orders by unique customers in one period to get purchase frequency.
- Choose the period from your reorder cycle: a month or quarter for consumables, 12 months or more for durable goods.
- Separate purchase frequency (orders per customer) from repeat purchase rate (share of customers who return) and ad frequency (impressions per person).
- Multiply customers by average order value by purchase frequency to see revenue; doubling frequency doubles revenue from the same customers.
- Compare frequency by acquisition cohort, because a wave of new customers lowers the blended average even when loyalty is unchanged.
- Time reminders to each product's real reorder cycle, and check margin before you buy extra orders with discounts.
Purchase frequency is the average number of orders each customer places in a set period: total orders divided by unique customers. A purchase frequency of 3 over 12 months means the typical customer bought three times that year. It tells you how strong the buying habit is among the customers you already have, which makes it one of the core metrics of any customer retention strategy.
This guide covers the purchase frequency formula with a worked example, how it differs from repeat purchase rate and ad frequency, why it moves revenue, what a good number looks like and how to increase it. Omniconvert Commander (formerly Nexus), the AI eCommerce growth engine, tracks it per customer segment from your store data.
What is purchase frequency?
Online stores, subscription brands and retailers use the metric to spot loyal buyers, forecast revenue and plan promotions and stock. A rising purchase frequency means demand from the same customer base is growing. A falling one is often the first sign of churn, well before customers stop buying entirely.
The period you choose changes the number, so pick it from your sales cycle. A month or a quarter suits fast-moving goods such as food, coffee or beauty products. Twelve months or longer suits high-value or occasional purchases such as furniture or electronics. Keep the same period in every report.
Purchase frequency formula: how to calculate it
You need two inputs: the number of completed orders in the period, and the number of distinct customers who placed at least one of them. Remove duplicate customer records and exclude cancelled or fully refunded orders, or the result runs high.
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Define the periodExample: January 1 to December 31.
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Count total ordersExample: 2,400 completed orders.
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Count unique customersExample: 600 distinct customers placed at least one order.
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Divide orders by customers2,400 ÷ 600 = 4. The average customer ordered four times that year.
Average purchase frequency is the same calculation; the word "average" is a useful reminder that it hides a spread. An average of 2 can mean most customers ordered once and a small loyal group ordered six or more times. Always look at the distribution of orders per customer next to the average.
Purchase frequency vs repeat purchase rate, retention and ad frequency
| Metric | Formula | What it answers |
|---|---|---|
| Purchase frequency | Total orders ÷ unique customers | How many times does the average customer buy? |
| Repeat purchase rate | Customers with 2+ orders ÷ total customers × 100 | What share of customers come back at all? |
| Customer retention rate | (Customers at end − new customers) ÷ customers at start × 100 | What share of customers stay active over the period? |
| Time between orders | Average days between consecutive orders of the same customer | How long until a customer buys again? |
| Ad frequency | Impressions ÷ reach | How often does one person see an ad? |
Purchase frequency measures how much customers buy again; repeat purchase rate measures how many come back at all. You can have a high frequency driven by a small group of loyal buyers and a low repeat rate at the same time, so track both. The guide to how to calculate repeat purchase rate covers the second metric, and time between orders shows the same habit as a gap in days rather than a count.
Ad frequency is a media metric with a similar name. Ad frequency is defined as the average number of times one person sees an ad during a campaign, calculated as impressions divided by reach. A campaign with 50,000 impressions and 10,000 unique users has an ad frequency of 5. Some repetition builds recall, but too much causes ad fatigue: click-through rate falls and cost per acquisition rises.
Why purchase frequency drives revenue and CLV
Take 1,000 customers with an average order value of $50 and a purchase frequency of 2: revenue is $100,000. Raise frequency to 4 and revenue doubles to $200,000, with no new customers and no change in basket size. Because acquisition cost is already paid, each extra order spreads that cost over more revenue.
The effect compounds with time. Bain & Company found that in online apparel, the average repeat customer spent 67 percent more in months 31 to 36 of the relationship than in months zero to six, partly from more frequent shopping and partly from larger orders [Bain & Company, 2000]. That is why frequency is a direct input of customer lifetime value, and one of the KPIs with the biggest impact on customer lifetime value.
What is a good purchase frequency?
Three factors set the natural ceiling. Product lifecycle decides how soon the item runs out or wears out. Price matters, because cheaper items get bought more often. Necessity matters too: essentials are bought on a routine, while luxury and non-essential purchases come at longer and less predictable intervals.
The mistake we see most often is reading the blended 12-month number during a growth phase. A store doubles its acquisition spend in the autumn, and thousands of new customers enter the period with only a few weeks to place a second order. The average purchase frequency falls, the team concludes that loyalty is slipping, and it launches discounts for customers who were never at risk. Measure frequency by acquisition cohort instead: compare customers acquired in the same month at the same age, using cohort retention rate as the companion view. Then a real decline shows up, and a false one does not.
How to increase purchase frequency
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Segment by behaviorGroup customers by recency, frequency and monetary value with customer segmentation. New, active, at-risk and lapsed customers need different messages.
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Time replenishment remindersUse each product's typical reorder gap to trigger a reminder just before the customer runs out, by email, SMS or push.
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Build a post-purchase flowThe second order is the hardest. Follow up with usage tips, a review request and a relevant next product.
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Reward repeat ordersPoints or tiers give customers a reason to return to you rather than a competitor. See these customer loyalty program examples for formats that work.
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Offer subscriptions for consumablesCoffee, vitamins and pet supplies suit recurring plans with flexible intervals, which turn a habit into a schedule.
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Personalize and remove frictionRecommend products from past purchases, and fix slow delivery, a hard checkout and slow support. A bad experience breaks the buying habit faster than any campaign builds it.
More orders do not always mean more profit. Take a $100 order with a 20% margin: profit is $20. Give a 10% discount and revenue falls to $90 while costs stay at $80, so profit drops to $10. That discount must double purchase frequency just to break even, and frequent deep discounts also train customers to wait for the next sale. Track True Profit next to frequency.
Test each tactic before you scale 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]. That program tested on-site conversion rather than repeat orders, but the method carries over: a controlled test shows which reminder, offer or flow actually brings customers back sooner.
Omniconvert Commander does the segmentation part for eCommerce. It builds RFM segments from your order data, calculates True Profit per order and per customer, and pushes segments directly to Meta Ads, Google Ads and Klaviyo, so each reminder reaches customers when they are due to buy.
Purchase frequency FAQs
Purchase frequency is the average number of orders each customer places in a set period, usually 12 months. It shows how often existing customers come back to buy, so it is a direct measure of buying habit and one of the main inputs of customer lifetime value.
Average purchase frequency is the same metric: total orders divided by unique customers for one period. The word average is a reminder that it hides a spread. A store with an average of 2 may have most customers at 1 order and a small loyal group at 6 or more, so read it next to the distribution of orders per customer.
Pick a period, count all completed orders in it, count the unique customers who placed at least one of those orders, and divide orders by customers. For example, 2,400 orders from 600 unique customers in one year give a purchase frequency of 4. Remove duplicate customer records and cancelled orders first.
Segment customers by recency, frequency and value, time your reminders to each product's real reorder cycle, build a post-purchase flow that leads to the second order, and reward repeat orders with a loyalty program or subscription. Personalize recommendations from past purchases, fix friction in checkout and delivery, and check that each tactic adds profit, not only orders.
No. Purchase frequency is the average number of orders per customer, so it measures how much customers buy again. Repeat purchase rate is the share of customers with more than one order, so it measures how many come back at all. A store can have a high purchase frequency driven by a small loyal group and a low repeat purchase rate at the same time.
It depends on what you sell. Consumables such as coffee, pet food or skincare naturally reorder several times a year, while furniture or electronics may sell once every few years. The most useful benchmark is your own history: compare the same cohort period year on year and look for a steady rise.
Omniconvert Commander, the AI eCommerce growth engine, groups customers into RFM segments from your store data, so you see who buys often, who is slowing down and who is about to lapse. It pushes those segments directly to Meta Ads, Google Ads and Klaviyo for timed campaigns, and it calculates True Profit per customer, so you know which extra orders actually pay.
Calculate purchase frequency for your last 12 months, then split it by acquisition cohort and by product category. The category with the shortest natural reorder cycle and the lowest frequency is where customers forget you, and that is your first reminder flow to build.
Next, list your one-time buyers by days since first order and send the second-order message before they pass your typical reorder gap. Track frequency and True Profit per customer together each quarter, so you grow orders that pay rather than orders you bought with discounts.
See who buys often, and who is about to stop
Omniconvert Commander builds RFM segments from your store data, calculates True Profit per customer, and pushes segments to Meta Ads, Google Ads and Klaviyo, so every reminder reaches the right customer at the right time.