How to Model LTV for a Marketplace Business
LTV for a marketplace is more complex than LTV for a SaaS business because a marketplace often has two customer sides --- buyers and suppliers --- and the value generated is a function of transaction volume, take rate, and retention on both sides. Modelling LTV correctly for a marketplace requires defining which side is the "customer" for LTV purposes, how transaction frequency evolves over time, and what gross profit is actually earned per transaction after fulfilment costs. Getting this wrong either overstates LTV significantly or misses the two-sided dynamic entirely.
The Two-Sided LTV Problem
A SaaS business has one customer type. A marketplace has two: the demand side (buyers, employers, clients) and the supply side (workers, vendors, service providers). Both sides generate value, both sides can churn, and the economics of each differ.
The first modelling decision is which side to calculate LTV for. The answer depends on which side the marketplace monetises.
Monetisation-side LTV: Calculate LTV for whichever side pays the marketplace (demand side in most marketplace models). This is the LTV that maps directly to CAC, because CAC is typically the cost of acquiring the paying side.
Supply-side value: Even if the supply side does not pay, supply-side retention drives demand-side LTV. A marketplace where workers or vendors churn frequently forces the demand side to re-onboard new supply, reducing quality and driving demand-side churn. Model supply retention as a driver of demand-side LTV even if supply-side LTV is not calculated directly.
LTV Components for a Marketplace
For a transaction-fee marketplace (e.g. staffing, freelance, logistics):
LTV = Average Transaction Value × Take Rate × Gross Margin on Take Rate ×
Average Monthly Transactions × (1 ÷ Monthly Demand-Side Churn Rate) This is equivalent to:
LTV = Average Monthly Gross Profit per Customer ÷ Monthly Churn Rate \...where "gross profit" is the marketplace's net take after payment processing and direct fulfilment costs, not the full transaction GMV. For a subscription marketplace (e.g. platforms where the buyer pays a subscription):
The LTV model is closer to SaaS: monthly subscription revenue × gross margin ÷ churn rate. But transaction data should be layered in if the subscription price is linked to usage volume.
The GMV vs. Net Revenue Distinction
The most common LTV error for marketplaces: using GMV (gross merchandise value, the full transaction value passing through the platform) as the revenue basis for LTV rather than net revenue (the take rate portion the marketplace actually keeps).
A staffing marketplace that places a worker at £15/hour and charges the employer £18/hour has:
GMV: £18/hour × hours worked
Net revenue (take): £3/hour × hours worked
Gross profit on the take: £3/hour × hours worked × gross margin on fulfilment
LTV should be calculated on gross profit from net revenue, not GMV. Using GMV as the LTV basis overstates LTV by the inverse of the take rate --- at a typical 15-20% take rate, this is a 5-7x overstatement.
Key insight: When a marketplace founder says their LTV is £X, the first clarifying question is always: is that gross profit on net revenue, or is it GMV? The answer changes the number dramatically and determines whether the LTV:CAC ratio reflects a viable business or an accounting artefact.
Modelling Transaction Frequency Over Time
One of the key differentiators in marketplace LTV is transaction frequency: how often does a demand-side customer transact, and does frequency grow or shrink over time?
Cohort analysis of transaction frequency is the most valuable input to marketplace LTV modelling:
In the first 1-3 months after acquisition, transaction frequency is often low as the customer tests the platform
In months 3-12, frequency typically increases as trust and workflow integration build
In months 12+, frequency stabilises at a steady state or continues to grow for deeply embedded customers
Building LTV as a constant (average monthly transactions × take × margin) misses this ramp-up dynamic. A more accurate model uses cohort-observed frequency curves to build a lifetime transaction schedule.
For early-stage marketplaces without 12+ months of cohort data, use a conservative frequency assumption (no ramp-up, steady-state from month one) as the base case and model the ramp-up as upside.
Supply-Side Retention as a LTV Driver
Supply-side churn creates demand-side LTV risk that does not appear in a buyer-only LTV model. If the supply side churns and quality or availability on the platform deteriorates, demand-side churn follows. For marketplace models, include a supply-side retention assumption and model the feedback loop:
LTV
The exact relationship varies by marketplace type, but acknowledging this linkage in the model notes is important for any marketplace with operational supply-demand matching.
Worked Example: Staffing Marketplace LTV
Assumptions:
Average employer (demand-side customer) places 3 workers per month Each worker placed generates £250 in net revenue (take) to the marketplace
Gross margin on fulfilment: 55%
Monthly gross profit per customer: 3 × £250 × 55% = £412.50 Monthly demand-side churn rate: 3%
LTV = £412.50 ÷ 0.03 = £13,750
If this were calculated on GMV instead (employer rate of, say, £1,500 per worker per month):
Incorrect LTV = (3 × £1,500 × 55%) ÷ 0.03 = £82,500
The GMV-based LTV is 6x the correct number. This is not an edge case --- it is the most common marketplace LTV error.
Frequently Asked Questions
Should marketplace LTV include both buyer and seller sides?
Only if you monetise both sides. If the marketplace charges workers or vendors a fee in addition to charging employers, model LTV for both sides. More commonly, two-sided LTV is calculated only for the monetised side, with supply-side retention modelled as a driver rather than a separate LTV component.
How do you handle seasonal transaction patterns in marketplace LTV?
Use annualised average transaction frequency rather than a specific month's data. If the marketplace is highly seasonal (e.g. retail staffing peaks at Christmas, summer festival staffing), the annualised average smooths the seasonality. Flag the seasonal pattern in the assumptions.
At what stage should a marketplace start tracking LTV by customer segment?
As soon as there are enough customers to create meaningful segments --- typically 50+ customers on the demand side. High-frequency, high-ACV buyers typically have very different LTV profiles from low-frequency, low-ACV buyers. Blending them into a single LTV hides the quality concentration that sophisticated investors will ask about.
Summary
Marketplace LTV is calculated on gross profit from net revenue (the take rate portion), not on GMV. Transaction frequency evolves over time and should be modelled using cohort data where available. Supply-side retention drives demand-side LTV and should be modelled as a linked assumption even if supply-side LTV is not calculated directly. The most common error --- using GMV as the LTV basis --- overstates LTV by the inverse of the take rate, which at typical marketplace take rates is a 5-7x error. Get the LTV denominator right before any investor conversation about unit economics. Use our free financial modeling tool to put this into practice.
The Most Common Financial Modeling Mistakes
The most dangerous mistake in startup financial modeling is building a model that only works in one scenario. Real businesses face unexpected churn, slower-than-expected sales cycles, competitive pricing pressure, and hiring delays. A model that only shows the plan without stress testing what happens if ARR growth is 30% lower, or if a key hire takes four months to land is not a planning tool; it is a wishful thinking exercise.
Circular references are a technical trap that undermine model credibility instantly. When an investor opens your spreadsheet and sees #REF errors or formula loops, it signals that the model has not been rigorously tested. Build revenue, cost, and cash flow on separate sheets with clear linking. Every input assumption should live in a dedicated assumptions tab so an investor can change your growth rate and see the full impact cascade through the model instantly.
Overcomplicated models are as problematic as oversimplified ones. A 40-tab model that takes 20 minutes to navigate tells an investor that the builder does not understand what drives their business. The best financial models are opinionated: they make clear which 3-5 assumptions matter most, and they are built to make sensitivity analysis on those assumptions easy.
Financial Modeling Best Practices for Fundraising
The 3-year model is the standard for Series A fundraising; 5 years is standard for later stages. Go beyond 3 years and your assumptions become fiction; stop at 18 months and you signal you have not thought through the full opportunity. Monthly granularity for Year 1, quarterly for Year 2-3 is the conventional structure.
Separate your revenue model from your headcount model and your cost model, and make them link cleanly. Revenue should drive headcount needs (more customers requires more customer success capacity), not the other way around. Build the headcount model with named roles, not just FTE counts investors will ask who these people are.
Document your key assumptions explicitly. The best models include a two-paragraph written explanation of each major assumption: why you chose the number you chose, what the range of outcomes looks like, and what early leading indicators would tell you the assumption is breaking down. This kind of rigorous documentation signals sophisticated financial thinking and dramatically reduces the back-and-forth during due diligence.
Benchmarks: What Good Actually Looks Like
SaaS benchmarks vary significantly by segment, go-to-market motion, and contract size. For SMB SaaS with monthly contracts: monthly logo churn of 2-4% is typical, below 2% is excellent. For mid-market SaaS: annual logo churn of 10-15% is normal, below 10% is strong. For enterprise: annual logo churn below 5% is expected.
Net Revenue Retention is the metric that separates good SaaS from great SaaS. Below 100% means you are shrinking your existing base even as you add new logos a structural problem. 100-110% is healthy. 120%+ is outstanding and signals genuine product stickiness with expansion opportunity. The best SaaS businesses (Snowflake, Datadog in their growth phase) have sustained NRR above 130%.
CAC payback period benchmarks: for SMB SaaS, under 12 months is excellent, 12-18 months is acceptable. For mid-market, under 18 months is strong. For enterprise, 24-36 months is normal given longer sales cycles, though enterprise LTV is correspondingly higher. The LTV:CAC ratio below 3:1 is a red flag; 4:1+ is what investors want to see, with a clear path to improvement as the business scales.
Gross margin is the foundation of all other SaaS metrics. Below 60% suggests infrastructure costs that need engineering attention. 70-75% is standard. 80%+ is excellent and gives you the unit economics to sustain aggressive growth investment without burning excessive capital. Below 50% typically indicates professional services revenue diluting the overall margin separate and report these lines clearly.
How to Present This Metric to Investors
Context matters more than the number. A 15% annual churn rate in an SMB market with a $50 ACV and 30-day cancellation windows is very different from 15% churn in an enterprise market with $50K ACVs and 12-month contracts. When you present your metrics, lead with the context that makes your number interpretable: what is your average contract value, what is your median customer tenure, and what is your go-to-market motion.
Show trends, not snapshots. A metric that was 18 months ago and is 10% today tells a powerful story about systematic improvement. A metric that was 8% 18 months ago and is 10% today raises an immediate question about what changed. Investors model trends forward; give them a trend that supports their thesis.
Segment before you present. Blended metrics almost always obscure important patterns. If your top-quartile customers have NRR of 140% and your bottom-quartile customers are churning at 30%, the blended number is misleading. Show the segmentation, explain what drives it, and articulate the plan to shift customer mix toward the higher-performing segment. This kind of analytical rigor builds confidence.
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