Why Your Contribution Margin Looks Fine Until You Factor In Repeat Purchase Rate

The Number Most Shopify Brands Are Getting Wrong

We pull up a brand's P&L and the contribution margin looks healthy. Forty percent, sometimes forty-five. The founder is proud of it, and they should be, because getting product and shipping costs under control is real work. But then we ask one question: what percentage of your revenue last month came from customers who bought more than once?

The room gets quiet.

Contribution margin calculated on a per-order basis is a useful number, but it is not the number that tells you whether your business is actually working. When you factor in what it costs to acquire a customer and then look at how many of those customers come back, the math changes dramatically. We see this pattern constantly in audits across Shopify stores doing anywhere from two million to twenty million a year. The unit economics look fine on the surface and then collapse the moment you stress test them against actual customer behavior.

What Repeat Purchase Rate Actually Does to Your Margins

Take a brand selling a consumable product, something like a supplement or a skincare item, at a sixty dollar average order value. Their blended CAC is thirty-eight dollars. That sounds fine. A twenty-two dollar contribution margin per first order is workable if customers come back.

But if only eighteen percent of customers buy again within twelve months, the math gets ugly fast. You are spending thirty-eight dollars to acquire a customer, making twenty-two dollars on the first order, and then losing most of them before they ever make a second purchase. The lifetime value never materializes. The business is essentially running a very expensive sampling program.

We have seen brands in this exact situation who were planning to scale their Meta spend because their ROAS looked acceptable. ROAS at the campaign level masked the fact that the business model was broken at the cohort level. You can find this by pulling cohort reports in Shopify analytics, or more granularly in tools like Triple Whale or Lifetimely. Most founders we work with have never looked at a cohort retention curve. They are optimizing the funnel without knowing whether the bucket has a hole in it.

Where the Disconnect Usually Lives

There are two places we see repeat purchase rate fall apart, and they have different causes and different fixes.

The first is a product-market fit problem wearing a marketing costume. The brand has found an audience willing to try the product once, but the product does not deliver enough on its promise to pull people back. No amount of post-purchase Klaviyo flows fixes this. You can send the most beautifully sequenced win-back campaign in the world and it will not move the needle if the customer's experience with the product was underwhelming. We see this most often with brands that grew fast on influencer or UGC creative because novelty drove the first purchase but the product itself was never differentiated enough to earn loyalty.

The second is an operational and experience problem. The product is actually good but the post-purchase journey is so forgettable that customers drift. They finish the product, think about reordering, and then get distracted. Three weeks pass. Then they see a competitor's ad. This is solvable, and this is where working on the email and SMS sequences, the unboxing experience, and even subscription prompts through ReCharge or Skio can genuinely move the number. We worked with one skincare brand that had a sixty-two dollar AOV and a twenty-one percent repeat purchase rate. After rebuilding their post-purchase flow in Klaviyo and adding a subscription option with a ten percent discount, repeat rate climbed to thirty-four percent over two quarters. That single change improved their effective contribution margin more than any conversion rate optimization on their product pages.

How to Stress Test Your Own Unit Economics

The process we use is not complicated but it requires being honest with the numbers you actually have rather than the numbers you wish you had.

Start by pulling your customer cohorts from Shopify analytics or your attribution tool. Look at every cohort from the last twelve months and track what percentage of first-time buyers made a second purchase within ninety days and within one year. Then calculate your real LTV at twelve months, not projected LTV based on industry benchmarks, but actual revenue per acquired customer based on what that cohort generated.

Now run your CAC against that number. If your twelve-month LTV is ninety dollars and your CAC is fifty-five dollars, you have a thirty-five dollar spread to cover all your other operating costs. That is tighter than most people realize once you factor in overhead, returns, and customer service costs.

The brands that scale well are the ones where the LTV curve is steep early. Meaning customers who buy once are buying again within sixty to ninety days at a high rate. That early repurchase behavior is the signal that the economics will hold as you put more money into acquisition. When we see a flat LTV curve, where most of the revenue comes from that first order and then trickles off, it is a strong signal to fix retention before scaling spend.

The Scaling Decision This Changes

A lot of founders come to us wanting to talk about their website conversion rate or their ad creative. Both matter. But if your repeat purchase rate is below twenty-five percent and your product is something people should logically reorder, that is the highest-leverage problem in the business. Fixing it does not require more traffic. It requires understanding why customers are not coming back and building the systems to change that behavior.

The brands we see scale cleanly from five million to twenty million and beyond are not necessarily the ones with the highest first-order conversion rates. They are the ones with retention curves that actually work. A thirty-five percent site conversion rate means nothing if you are churning customers after one purchase. A twenty-eight percent conversion rate with a forty percent ninety-day repeat purchase rate is a completely different business.

If you have not run this analysis on your own store, it is worth doing before you make any major decisions about scaling ad spend, launching new products, or investing in site optimization.

We do a full unit economics review as part of our conversion audit, looking at not just what is happening on your site but whether the business model underneath it is set up to actually grow. If you want a second set of eyes on your numbers, reach out and we can take a look.