Marketplace Event Schema: The Complete Reference

A complete reference for marketplace analytics. All events — from listing creation to completed transaction — with parameters, data types, and data layer examples you can copy into your tracking plan.

Marketplace tracking is fundamentally different from standard e-commerce. You have two user types — buyers and sellers — with separate funnels that need to converge at a transaction. Most analytics setups treat marketplaces like regular online stores and miss half the picture. This reference gives you a structured event schema that covers both sides of the marketplace equation.

I’ve applied this schema across property rental platforms, freelance marketplaces, and B2B procurement sites in Europe. The structure works for any two-sided platform — whether you’re matching supply and demand for apartments, services, or products.

Marketplace Funnel Overview

Marketplace analytics tracks two parallel funnels — supply (sellers/providers) and demand (buyers/seekers) — that meet at the transaction point.

Search & Discovery Events

Search is the primary conversion driver in any marketplace. Tracking what buyers search for, how they filter results, and what they click reveals both demand patterns and supply gaps.

The search_performed event with results_count = 0 is critical for marketplace health. Zero-result searches directly indicate unmet demand — if people search for “dog walker Berlin Mitte” and get nothing, you know exactly where to focus supply acquisition.

Track filter_applied to understand what attributes matter most to buyers. When I built analytics for a European rental marketplace, filter data showed that 78% of users filtered by price range before anything else, followed by location radius. That insight shifted the product team’s priority from building more filter options to improving the price and map UX.

Data Layer Example: Search Performed

window.dataLayer.push({
  "event": "search_performed",
  "search_term": "web developer",
  "results_count": 34,
  "search_category": "freelancers",
  "location": "munich"
});

Listing Interaction Events

Once a buyer finds a listing, these events track the evaluation journey — from first view to saving, comparing, and contacting the seller. This is where most marketplace drop-offs happen.

The source parameter on listing_viewed tells you where the buyer came from: search_results, category_browse, recommendation, saved_list, direct_link, or email_alert. This is essential for understanding which discovery channels produce the highest view-to-booking rates.

Track photo_viewed carefully — listings where users view 3+ photos convert at significantly higher rates than those where users view only the thumbnail. When I analyzed this for a vacation rental platform, the correlation was striking: listings with 8+ photos and where users viewed at least half of them had a 3.2x higher booking rate than the platform average.

Messaging & Inquiry Events

In many marketplaces, the path to transaction goes through messaging. Tracking conversations between buyers and sellers reveals trust-building patterns and conversion bottlenecks.

The response_time parameter (in minutes) on inquiry_replied is one of the most important marketplace health metrics. Sellers who respond within 1 hour typically see 2-3x higher conversion rates than those who respond after 24 hours. Track this server-side and use it to build seller quality scores.

Use sender_type values of buyer or seller on message_sent. The message count per conversation before transaction is a key metric — too many messages suggests the listing lacks information; too few might indicate the platform’s trust mechanisms (reviews, verification badges) are working well.

Booking & Transaction Events

These events track the actual conversion — when browsing becomes buying. For marketplaces, this often involves multiple steps: request, confirmation, payment, and fulfillment.

The platform_fee parameter on payment_completed is what separates marketplace analytics from standard e-commerce. Your actual revenue is the fee, not the gross merchandise value (GMV). Always track both so your dashboards can show GMV for market size and platform fee for actual business performance.

For marketplaces with negotiation (classifieds, freelance, B2B), the offer_made and offer_accepted events reveal pricing dynamics. Track the ratio between offer_amount and listing_price to understand how much buyers expect to negotiate — and negotiation_rounds to see how quickly deals close.

Data Layer Example: Payment Completed

window.dataLayer.push({
  "event": "payment_completed",
  "booking_id": "BK-2026-00451",
  "transaction_value": 850.00,
  "currency": "EUR",
  "payment_method": "card",
  "platform_fee": 127.50
});

Listing Management Events (Supply Side)

The supply side is where many marketplace analytics setups fall short. Tracking how sellers create, manage, and optimize their listings tells you about marketplace health and supply quality.

Track photo_count and description_length on listing_created to measure listing quality at creation time. These are strong predictors of conversion — listings with fewer than 3 photos or descriptions under 100 characters almost always underperform. Use this data to build smart onboarding prompts that nudge sellers toward complete listings.

The listing_promoted event is a revenue line for many marketplaces. Track promotion_type values like featured, top_of_search, highlighted, or category_spotlight. Combine promotion spend with booking data to calculate promoted-listing ROI for sellers — a metric that drives upsell conversations.

Review & Trust Events

Trust is the currency of marketplaces. Reviews, ratings, and verification events build the social proof that makes strangers comfortable transacting with each other.

The days_after_booking parameter on review_submitted reveals the optimal review request timing. Most marketplaces find that reviews submitted within 3 days are longer, more detailed, and have higher ratings than those submitted after a week. Use this data to optimize your review reminder emails.

Track dispute_type values like not_as_described, no_show, cancellation, quality_issue, or payment_dispute. Dispute patterns often reveal systemic issues — a spike in “not as described” disputes in a specific category signals a listing quality problem that better photo requirements or descriptions could solve.

Complete Event Map

All marketplace events at a glance, organized by user type and funnel stage.

GTM Implementation Guide

Here’s how to set up the core marketplace events in Google Tag Manager. Supply-side and trust events are mostly server-side — focus your GTM implementation on demand-side interactions.

Marketplace-Specific Metrics

Standard web analytics metrics don’t tell the full marketplace story. These calculated metrics — derived from the events above — are what actually matter for marketplace health.

Validation Checklist

FAQ

Further Reading

• GA4 Recommended Events Reference — Google Analytics
• GA4 Measurement Protocol — Google Analytics
• Marketplace Metrics That Matter — Andreessen Horowitz
• Marketplace Liquidity — NFX

Lukas Meier

Product Analytics Specialist based in Munich. Helping teams implement clean, actionable analytics since 2015.