Marketplace & Two-Sided Platforms
Marketplace companies operate dual-sided revenue models — take rates on transactions plus optional SaaS subscriptions for sellers — requiring RevOps to instrument GMV, seller acquisition, buyer activation, and cohort-based retention with attribution models standard CRMs cannot support.
Business Model
Transaction take rates of 5–30% of GMV, seller SaaS subscriptions, promoted listing fees, payment processing revenue
Revenue Cycle
Seller acquisition in weeks then 30–90 day activation; buyer cohort retention determines long-term revenue; GMV compounds as liquidity grows
RevOps Focus
Dual-sided funnel tracking, GMV-based attribution, seller health scoring, buyer cohort retention, take-rate optimisation
Key Metrics
What this industry measures
Take Rate
For marketplace and platform businesses, the percentage of each transaction's GMV that the platform retains as revenue — also called the commission rate or monetisation rate.
What it shows
Take rate reveals how effectively a marketplace monetises the volume flowing through it. Rising take rates signal stronger platform leverage and pricing power; declining take rates often indicate competitive pressure or a deliberate volume-first growth strategy.
Formula
Take Rate = Net Revenue ÷ GMV × 100. A marketplace processing $100M GMV and keeping $15M as revenue has a 15% take rate.
Churn Rate
The percentage of customers or revenue lost in a given period due to cancellations, non-renewals, or downgrades — the core measure of customer retention failure.
What it shows
Churn rate shows how fast the business is leaking its customer base. Even moderate churn compounds dramatically — a 2% monthly churn rate means roughly 22% of the customer base disappears every year, requiring constant replacement just to stay flat.
Formula
Customer Churn Rate = Customers Lost in Period ÷ Customers at Start of Period × 100. Revenue Churn Rate = MRR Lost (ex expansion) ÷ Beginning MRR × 100.
LTV
Customer Lifetime Value (CLV) — the predicted total revenue a customer generates over the entire duration of their relationship with the business.
What it shows
LTV shows how much a customer is ultimately worth and, when compared to CAC (LTV:CAC ratio), reveals whether the business model is economically viable — a ratio below 3:1 typically signals unit economics that need improvement before scaling.
Formula
LTV = Average Revenue per Customer ÷ Churn Rate. For subscription: LTV = ARPU × Gross Margin % ÷ Monthly Churn Rate.
CAC
Customer Acquisition Cost — the total sales and marketing spend required to acquire one new customer, including headcount, tools, programmes, and all associated overhead.
What it shows
CAC shows how efficiently the business converts sales and marketing investment into new customers. Rising CAC without corresponding LTV growth signals the business is becoming less efficient at growth — a key investor concern at scale.
Formula
CAC = Total Sales & Marketing Spend in Period ÷ Number of New Customers Acquired in Period. Blended CAC includes all S&M spend; segmented CAC can be calculated by channel, segment, or market motion.
Gross Margin
The percentage of revenue remaining after subtracting the direct cost of goods sold (COGS) — for SaaS, COGS includes hosting, support, and customer success costs attributed to delivering the service.
What it shows
Gross margin reveals the inherent profitability of the product at scale. SaaS businesses typically target 70-80% gross margins. Lower margins indicate high delivery costs that constrain the business's ability to invest in growth and reach operating profitability.
Formula
Gross Margin % = (Revenue − COGS) ÷ Revenue × 100. COGS for SaaS includes hosting/infrastructure, third-party licence costs, and direct CS/support team costs.
GMV
Gross Merchandise Value — the total dollar value of all transactions processed through a marketplace or platform in a period, before the platform's commission or fee is deducted.
What it shows
GMV measures the economic activity flowing through a marketplace. It is a scale indicator, not a revenue indicator — a platform's actual revenue is a fraction of GMV determined by its take rate. Growing GMV with declining take rate signals monetisation challenges.
Formula
GMV = Total Value of All Transactions Processed on the Platform in a Period. Revenue = GMV × Take Rate. Disclosed by most marketplace companies as a primary growth metric.
DAU/MAU
Daily Active Users divided by Monthly Active Users — a ratio measuring the stickiness of a product by showing how frequently monthly users return on a daily basis.
What it shows
A high DAU/MAU ratio (above 0.5) indicates the product is habit-forming and central to users' daily workflows. Low DAU/MAU signals occasional-use patterns — a churn risk indicator for productivity or workflow tools that should be embedded in daily routines.
Formula
DAU/MAU = Average Daily Active Users ÷ Monthly Active Users. A ratio of 0.5 means the average monthly user returns every other day. Define 'active' by a meaningful product action beyond a login.
NPS
Net Promoter Score — measures customer loyalty by asking how likely customers are to recommend the product on a 0-10 scale, segmenting respondents into Promoters (9-10), Passives (7-8), and Detractors (0-6).
What it shows
NPS serves as a leading indicator of churn and expansion. Accounts with low NPS are materially more likely to churn at renewal. Tracking NPS at the account level gives CS teams an early warning signal months before renewal to intervene proactively.
Formula
NPS = % Promoters − % Detractors. Scores range from −100 to +100. Above 50 is considered excellent; above 70 is world-class. Segment by account tier, industry, and cohort for actionable analysis.
ARPU
Average Revenue Per User or Account — the average monthly or annual recurring revenue generated across all active customers.
What it shows
ARPU trends reveal whether the business is moving upmarket (rising ARPU) or acquiring smaller, lower-value customers (declining ARPU). Segmenting ARPU by cohort, channel, or acquisition period identifies where the most valuable customers originate.
Formula
ARPU = Total MRR (or ARR) ÷ Total Number of Active Customers. Calculate monthly or annually and track the trend over time. Segment by plan tier, industry, or geography for strategic insight.
Tech Stack
Tools that power this industry's revenue motion
Stripe
BillingA cloud payments infrastructure platform offering payment processing, subscription billing, invoicing, revenue recognition, and financial data APIs for internet businesses of all sizes.
Used for
RevOps teams at SaaS and PLG companies use Stripe to manage self-serve subscription billing, extract MRR and churn data for financial reporting, and build custom revenue dashboards via Stripe's reporting APIs.
Snowflake
Data WarehouseA cloud-native data warehouse platform that separates storage from compute, enabling organizations to store and query massive volumes of structured and semi-structured data across multiple cloud providers.
Used for
RevOps teams use Snowflake as the central analytical layer where CRM, marketing, product, and financial data converge, enabling cross-functional revenue analytics that no single source system can provide.
BigQuery
Data WarehouseGoogle Cloud's serverless, highly-scalable data warehouse that uses SQL and supports petabyte-scale analytics with built-in machine learning capabilities via BigQuery ML.
Used for
RevOps teams at Google Cloud-native organizations use BigQuery to warehouse event streams, marketing touchpoints, and CRM exports for large-scale pipeline and attribution analysis without managing infrastructure.
Segment
OtherTwilio Segment is a customer data platform (CDP) that collects first-party behavioural data from web, mobile, and server sources, standardises it into a common schema, and routes it to hundreds of downstream tools.
Used for
RevOps teams use Segment to create a single source of truth for customer behavioural data, ensuring the same user actions flow consistently into Salesforce, Marketo, Amplitude, and Intercom without duplicate tracking code.
Python
OtherThe dominant general-purpose programming language used in data science, automation, and analytics, with a rich ecosystem of libraries (pandas, scikit-learn, SQLAlchemy) essential for advanced RevOps work.
Used for
RevOps practitioners use Python to build data pipelines, automate reporting, clean and transform CRM data at scale, build predictive models for churn and lead scoring, and create custom integrations between revenue systems.
Looker
BI/AnalyticsA Google Cloud business intelligence platform built around a semantic layer (LookML) that defines business metrics once and makes them consistent across all reports and data consumers.
Used for
RevOps teams use Looker to define canonical revenue metrics (ARR, pipeline coverage, win rate) in LookML once, ensuring every team across the business uses the same definitions when querying revenue data.
Amplitude
BI/AnalyticsA digital analytics platform focused on product intelligence, helping teams understand user behavior, measure feature impact, and run experiments to optimise product-led growth.
Used for
RevOps teams in PLG companies use Amplitude to define and monitor PQL cohorts, track activation milestones, and measure how product usage patterns correlate with expansion revenue and retention.
Mixpanel
BI/AnalyticsA product analytics platform that tracks user behavior within web and mobile applications, enabling teams to analyse funnels, retention, user flows, and feature adoption with event-based data.
Used for
RevOps teams supporting PLG motions use Mixpanel to track product qualified lead (PQL) signals — identifying which product behaviors (inviting teammates, reaching usage limits) predict conversion to paid.