Margin-adjusted customer lifetime value calculator
Adjust the sliders to reflect your brand. No sign-up or shared data required. Just maths.
What happens when more of your customers come back
Extra annual contribution from lifting your repeat purchase rate.
Book a demo to find the — sitting in your repeat rate.
We’ll show you which customers are about to lapse, what each segment contributes after margin and returns, and where the gap to your category’s strong band actually lives. Using your data, not a sample.
How this calculator works
It takes four things: how many customers have bought from you, what a typical order is worth, your gross margin, and how many first-time buyers come back within a year. The market and category selectors set the starting points, so you can get a usable answer without looking anything up.
Then it does what most lifetime value calculators skip. It takes returns out of order value first, applies your gross margin, and only then multiplies by how many orders a customer is expected to place. What comes out the other side is contribution rather than revenue, which is money the business merely processes.
It doesn’t know your actual numbers. The starting points are category benchmarks and, where no benchmark exists, our own estimates. Every one of them is labelled on the page. Move a slider to something you know is true and the whole result corrects itself.
What period each number covers
- Customers who have purchased = a stock of everyone who has ever bought from you
- Average order value = one order. Not a year, and not a customer’s annual spend
- Gross margin = a rate. Use merchandise margin after cost of goods and before overheads
- Repeat purchase rate = one year. The share of new customers who buy again within twelve months
- New customers in the last 12 months = one year. The share of your customer base whose first purchase was in the past twelve months
- Contribution lifetime value, and the total in your base = whole of relationship
- The opportunity figures and the demo figure = per year
Why your real number is hard to get
Retailers often struggle to pull their true margin-adjusted lifetime value.
Margin usually lives at product level, in the merchandise system. Customers live somewhere else entirely, either in ecommerce, in point of sale, in the loyalty programme, or in the email platform, and the same person appears in each as a separate record. Someone who bought online in April and in store in September looks like two customers who each bought once, rather than one customer who came back. So the repeat rate most retailers can pull is worse than reality, and the margin figure isn’t attached to a customer at all.
What to do with the answer
- Check your acquisition ceiling. If you’ve been bidding against a revenue-based lifetime value, you’ve been paying roughly the overstatement figure too much for a customer.
- Rank segments by contribution rather than revenue. A heavily discounted, high-returns cohort will outrank a smaller full-price one on revenue and lose to it on contribution every time.
- Work out how long a second purchase normally takes, then reach people inside that window rather than after it.
- Start with recognising more customers in store. It’s where most omnichannel retailers lose the link between a sale and a person, and it’s the clearest difference between average and strong repeat rates
FAQs
What is margin-adjusted customer lifetime value?
It’s the total gross contribution a customer delivers over their relationship with you, rather than the total revenue. Returns come out of order value first, then gross margin is applied, so the result reflects money you keep rather than money you process.
Why is revenue-based lifetime value misleading?
Because it counts money that never becomes profit. On a 40% margin with 19.3% of online orders returned, you keep about 32 cents of contribution per dollar of revenue-based lifetime value, so the revenue figure runs about 3.1 times high. Acquisition budgets set against it will overpay.
What is a good repeat purchase rate for a retailer?
Bluecore’s benchmarks across 100+ retailers put the average first-year repeat purchase rate at 16.5%, with apparel at 20.2%, health and beauty at 21.5%, and sporting goods and outdoor at 21.2%. Retailers recognising more than 40% of their customers ran 53% above average, which is a fair target band.
The maths, and where the numbers come from
Every benchmark on this page has a source and a date. Where we couldn’t find one, we say so instead of inventing a figure.
The overstatement figure cancels out both order value and repeat rate, so it depends only on your margin and your returns rate. That’s why it holds steady while the other numbers move.
Note the last two lines. The lifetime value figures above them are whole-of-relationship, but the opportunity and demo figures are deliberately annual. They count the extra second orders a higher repeat rate would produce inside a year. Taking the difference between two lifetime multipliers and calling it annual would overstate the yearly figure by roughly double.
Sources
| Figure | Source | Date | Sample |
|---|---|---|---|
| Repeat purchase rate by category | Bluecore, Customer Growth Benchmarks Report | April 2024, calendar 2023 data | 100+ retailers, seven verticals, United States |
| Strong and behind bands (×1.53 and ×0.67) | Bluecore, same report — cohorts recognising over 40% and under 10% of customers | April 2024 | As above |
| Returns rate, 19.3% of online sales | National Retail Federation and Happy Returns, 2025 Retail Returns Landscape | October 2025 | 2,006 consumers; 358 ecommerce professionals at US merchants above $500M revenue |
| Australian order value starting point, A$96 | Australia Post, eCommerce Report 2026 | 2026 edition, calendar 2025 data | Australian online shopping, 9.8 million households |
| Gross margin starting points | Assumption. Anchored on CSIMarket industry margins (Retail Apparel 34.62%, Apparel/Footwear/Accessories 50.12%) | Q1 2026, trailing twelve months | Listed companies — not a mid-market sample |
What we’re assuming
Gross margin starting points are estimates, not benchmarks. The published figures come from listed large-cap retailers, which aren’t a good match for a mid-market omnichannel brand. Move the slider to your real number and the result corrects itself.
The new-customer share starts at an estimate. We found no published benchmark for how much of a retailer’s base is acquired each year, so the 20% starting point is ours. Set it to your own figure for an accurate annual number.
Repeat purchase rates are United States figures. No equivalent Australian or New Zealand benchmark is published. Both settings use the same category rates, and the New Zealand order value starting point is inherited from Australia.
The United States order value starting point is our estimate. We found no credible published figure, so US$110 is ours. Australia’s A$96 comes from Australia Post, and New Zealand reuses it.
Four categories have no published rate of their own. Footwear, home goods, jewellery and accessories, and department stores fall back to the all-retail average of 16.5%.
The returns figure comes from merchants above US$500M revenue. Directionally sound, but not a mid-market sample.
We treat repeat probability as constant. In reality it climbs with every order (the same research found active buyers place 57.6% more orders than new ones). So this model understates lifetime orders for well-retained brands. We’d rather be conservative on a page someone takes to their finance team.
Contribution here is before customer acquisition cost. We found no credible published acquisition cost benchmark for mid-market retail, so rather than invent one the calculator stops at gross contribution. Your true margin-adjusted lifetime value is lower again.