Customer LTV on Marketplaces: Estimating What You Cannot Directly See

Lifetime value is the number that decides how much you are allowed to spend to win a customer. On your own store you can calculate it almost cleanly. You know who bought, how often they came back, and what each order was worth. On Amazon or Flipkart you know none of that at the person level. The platform owns the buyer and hands you aggregates. So most marketplace brands set LTV aside as a D2C luxury, cap their acquisition cost at the first order, and quietly underspend on the categories that would have paid them back twice over.

That is a mistake, and it is the expensive kind, because it is invisible. You never see the second order you failed to fund. The honest position is not that marketplace LTV is unknowable. It is that you cannot measure it directly, so you have to estimate it deliberately, from signals the platform leaks, and treat the estimate as a range you can defend rather than a number you can prove.

Why LTV is worth estimating even when it is fuzzy

The point of LTV was never precision. It was a spending ceiling. If a buyer is worth eight hundred rupees over their life with you, you can rationally pay more to acquire them than if they are worth two hundred. That decision does not need a perfect number. It needs a defensible band and the discipline to act on it.

The brands that refuse to estimate LTV do not avoid the decision. They make it implicitly, by capping acquisition cost at the first-order margin. That cap is a hidden LTV assumption of exactly one order. For a consumable that reorders every month, that assumption is wrong by a wide margin, and the competitor who modelled it correctly will outbid you on every keyword that matters.

Refusing to estimate LTV is not caution. It is choosing the worst possible estimate, one order, and pretending you did not choose.

The signals that proxy lifetime value

You will never get a customer ID you can build a clean LTV model on. You will get enough to build a coarse one. Three signals carry most of the weight for marketplace brands in India.

• Subscribe-and-save enrolment and churn. If you sell anything repeatable, this is the closest thing to a real retention curve a marketplace gives you. Enrolments and how many survive three and six months later are a near-direct read on how long a buyer stays. Treat the subscribe cohort as your highest-confidence LTV input.
• Repeat-purchase rate at the SKU level. Amazon and, less generously, Flipkart report repeat behaviour in aggregate. The repeat rate is the probability of a next order. Stack a few of those and you have an expected order count, which is the spine of any lifetime-value estimate.
• New-to-brand share against total orders. This tells you how much of each period is fresh acquisition versus returning demand. A buyer base that keeps returning is a base with real lifetime value. One that is all new-to-brand every month has none, no matter how good the topline looks.

None of these names a single customer. Together they describe the curve, and the curve is what LTV actually is. The work of turning these into a number is cohort work, which is why the two disciplines are inseparable. We lay out the proxy method in full in building cohorts without first-party data, and an LTV estimate is the natural output of those cohorts rather than a separate exercise.

A method you can defend in a meeting

Keep the model crude on purpose. A crude model you can explain beats a precise one nobody trusts. The estimate has three inputs and one honest caveat.

1. Expected number of orders. Start from the SKU repeat-purchase rate. If roughly a fifth of buyers place a second order and a smaller share a third, you can build an expected-orders figure without ever knowing who those buyers were. For subscribe SKUs, use the survival curve instead, it is cleaner.
2. Contribution per order. Use contribution margin, not revenue. Revenue-based LTV flatters you into overspending. Net of fees, ad cost, returns, and shipping is the only version that maps to what you can actually afford. This is the same discipline we apply when reading profitability one SKU at a time, and LTV inherits its honesty from that per-SKU margin work.
3. A decay assumption. Buyers churn. Apply a falling probability to each successive order rather than assuming a flat repeat rate forever. A simple decay is enough. The goal is a band, low and high, not a single confident point.

Multiply expected orders by contribution per order, discount the later orders, and you have a lifetime-value range per SKU or per category. State it as a range every time. The honesty of the range is what makes leadership trust the rest of the analysis.

Where the estimate breaks, say so first

The model has known weaknesses, and naming them yourself is what keeps it credible. Repeat rates from a promo-heavy period overstate loyalty, because deep-discount buyers repeat worse than full-price ones. A short observation window understates LTV for slow-reorder categories. And aggregate repeat data cannot separate one devoted buyer placing five orders from five buyers placing one each. Flag each of these in the room before someone else does, and the estimate survives the scrutiny it deserves.

What the estimate actually changes

An LTV band is useless if it does not move money. It should change three decisions directly.

• Your acquisition ceiling. A SKU with a strong subscribe curve and a rising repeat rate can justify a first-order acquisition cost that looks reckless in isolation, because the second and third orders repay it. A SKU that never repeats has to win on the first order or be cut.
• Where you scale spend. Two SKUs with identical first-order economics are not equal businesses if one has triple the expected order count. LTV is what separates them, and without it you fund both as though they were the same.
• Who you chase back. The cohorts with the highest estimated value are the audiences worth re-engaging. That points budget straight at the demand most likely to return, which is the entire logic of retargeting marketplace shoppers when you do not own their data. You spend re-engagement money where the lifetime value is, not evenly.

Notice that every one of these is an acquisition decision. That is the point of the angle. You estimate LTV not to admire it but to know how aggressively you are allowed to spend at the top of the funnel. Subscribe-and-save and repeat signals proxy that value well enough to set the ceiling, and a deliberately rough estimate beats the silent assumption of one order every time.

What changed recently

The platforms have started handing you a cleaner subscribe signal than they used to, and the quick-commerce players have turned subscription into the explicit retention lever. That is good news for anyone estimating LTV, because it makes the highest-confidence input bigger and more legible.

The clearest proof is the subscription membership data itself. When Zepto piloted its paid membership, subscribers spent more than 30 percent more on the app and their monthly retention improved by roughly 10 percent, with the pass crossing a million members inside a week of launch, per YourStory. Read that as an LTV statement, not a marketing one. A membership cohort that spends a third more and churns less is a directly observable retention curve, the exact thing marketplace aggregates usually deny you. If you sell through a platform with a membership tier, the subscriber cohort is now your highest-confidence band, and you should be modelling it separately from walk-up demand.

The strategic backdrop reinforces the same point. Inc42’s 2026 quick-commerce outlook argues there is still Inc42 virtually no loyalty in the category, with shoppers keeping several apps and switching on availability and discount, and it expects growth to come from getting existing customers to order more often rather than from new cities, with subscriptions and private labels as the main stickiness levers. For a brand, that is a direct instruction. The platforms are now optimising for order frequency, which means the repeat and subscribe signals you build LTV on are about to carry more of the platform’s own attention, and the brands that already have a defensible LTV band will set acquisition ceilings the switchers cannot.

Make the number live somewhere leadership will look

An LTV band that lives in one analyst’s spreadsheet changes nothing. It has to sit beside acquisition cost and per-SKU margin in the same view, so the spending decision is obvious at a glance. The relationship between what a buyer costs and what a buyer is worth is the single most important comparison on a marketplace P&L, and it deserves a permanent line, not a quarterly slide. Getting it there without drowning the reader is its own craft, the one we mean by a reporting dashboard leadership will actually read.

Update the estimate as the cohorts mature. Early repeat data is noisy, and an LTV band built on two months of signal should widen, not pretend to certainty. As subscribe curves lengthen and repeat rates stabilise, the band tightens, and your acquisition ceiling earns more confidence with it. On quick commerce specifically, the same logic governs unit economics once platform fees are netted out, which is why we treat LTV and quick-commerce unit economics after platform fees as two reads on the same P&L.

The short version

You cannot see a marketplace customer twice. You can still estimate what they are worth, from subscribe survival, repeat-purchase rates, and new-to-brand share, assembled into a contribution-based lifetime band with an honest decay. The estimate will be a range, and that is correct. It is still enough to set an acquisition ceiling that beats the silent one-order assumption most brands spend under. And as platforms lean harder into membership and frequency, the subscribe signal at the heart of that band is only getting stronger.

Our Analytics & Reporting work exists to turn those scattered platform signals into an LTV band a brand can spend against, and our Marketplace Performance teams use it to decide where acquisition money actually compounds. The customer is hidden. Their value is not. Estimate it, defend the range, and spend like you mean it.