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ScrapingBuddy
Illustrative contemporary theater with pale balconies and burgundy seats
All use cases

TICKET INTELLIGENCE / MANAGED DATA

Vivid Seats
ticket data.

Separate the seat, the offer and the fees.

We collect Vivid Seats ticket offers with section and zone details, quantities, prices and listing perks. Add available checkout components to support deeper price research.

Discuss your project Explore data points

THE SIGNAL BEHIND THE LISTING

Understand what sits behind each offer.

ScrapingBuddy connects ticket prices with seating zones, listing conditions and purchase options. Use structured records to research inventory, compare offers and investigate price composition.

01

Research seat-level pricing

Compare observed offers by event, section, zone and row. Keep venue context attached to each asking price.

02

Compare offer attributes

Bring notes, perks, tags and purchase quantities into your analysis. Understand which attributes accompany different price levels.

03

Analyze price composition

Study available checkout prices, fees and tax as distinct components. Scope the required checkout detail separately from listing collection.

WHAT WE COLLECT

The data behind
your next decision.

Choose the Vivid Seats data points your workflow needs. We keep related details together and agree the final schema around your use case.

01

Event & listing identity

Connect every offer to its marketplace and event. Collection timestamps help your team follow the same listing across deliveries.

View field names
  • marketplace
  • event_id
  • listing_id
  • cache_time
02

Sections, zones & rows

Group ticket offers by source section and seating zone. Preserve original labels to support venue-specific mapping and comparison.

View field names
  • section_id
  • section_name
  • section_name_raw
  • zone_id
  • zone_name
  • row_name
03

Ticket quantities

Separate the number of tickets in a listing from the quantities a buyer can purchase. Compare singles, pairs and larger groups accurately.

View field names
  • ticket_quantity
  • ticket_quantity_lots
04

Listing prices

Retain displayed and all-in pre-checkout prices separately, so your analysis can identify the stage at which an amount was observed.

View field names
  • display_price_pre_checkout
  • all_in_price_pre_checkout
05

Notes, perks & tags

Keep source-described perks and listing annotations with each offer. Use these attributes to segment inventory and contextualize price differences.

View field names
  • listing_notes
  • perks
  • tags
06

Checkout components

Where available and included in your scope, collect checkout price, buyer fees, other fees, tax and total as separate amounts.

View field names
  • display_price_checkout
  • buyer_fee_checkout
  • other_fee_checkout
  • sales_tax_checkout
  • all_in_price_checkout

Field availability depends on the source and agreed collection scope. Need a different schema or additional enrichment?

Share your requirements

BUILT FOR USEFUL COMPARISONS

A clearer view of price composition.

Listing prices and checkout amounts answer different questions. We keep their stages distinct, so your team can analyze fees without blending unlike values.

Separate price stages
Displayed listing prices and observed checkout totals retain their own fields.
Itemized components
Available buyer fees, other fees and tax can be scoped alongside checkout prices.
Explicit missing values
An unavailable fee or tax stays unknown; it is not silently converted to zero.

A COLLECTION BUILT AROUND YOUR BRIEF

From event scope
to a maintained feed.

Recurring ticket intelligence needs more than a page response. We scope access, records and quality checks around your research.

  1. 01 / DEFINE

    Start with your events

    Share target events, required fields and how you plan to use the data. We agree a collection scope, refresh schedule and delivery format.

  2. 02 / VALIDATE

    Maintain the collection

    Source-specific access engineering, parsing and schema checks. Monitor missing fields, failures and freshness separately from inventory changes.

  3. 03 / DELIVER

    Fit your data stack

    Review a sample, confirm the schema and connect the delivery to your pipeline. Choose Parquet, CSV, JSON or a scoped API integration.

Researching a few public pages instead?

Our self-service Scraping API collects accessible public content with optional AI analysis. It does not bypass CAPTCHA or login, and does not automatically return this ticket schema.

Explore Scraping API

BEFORE YOU BUILD ON THE DATA

The questions
that affect your model.

Can checkout fees and tax be included for every listing?

Availability depends on the source flow and collection scope. We validate checkout access and the required fields before committing coverage. Missing amounts remain empty; they are not estimated from other listings.

Is an all-in listing price the same as a checkout total?

Not necessarily. Price labels can refer to different stages and tax treatment. We retain distinct pre-checkout and checkout fields and agree currency, price unit and tax interpretation before building comparisons.

Can zones be used as exact seat locations?

No. A zone groups seating; it does not identify an individual seat. Section, zone and row details support location-based analysis without inventing exact seat positions.

How do you set refresh frequency and project pricing?

We scope the event set, listing volume, required fields, collection frequency and delivery method together. Checkout detail or additional enrichment can change the collection work. We validate a sample and agree the scope and price before starting a recurring feed.

What happens when the source changes or a collection fails?

Our managed workflow includes source-specific parsing and validation. We agree freshness checks and how failed or incomplete collections are reported, so a collection failure is not mistaken for empty inventory. Monitoring and maintenance responsibilities are defined in the delivery agreement.

Can the data feed connect to our existing pipeline?

We can scope Parquet, CSV or JSON delivery, or a project-specific API integration. Before delivery, we agree identifiers, field types, missing-value handling, currency, price units and timestamp conventions. This managed feed is separate from the self-service Scraping API.

EXTEND YOUR MARKET VIEW

Researching more than one marketplace? We can scope event matching and field normalization as part of your project.

YOUR EVENTS. YOUR DATA REQUIREMENTS.

Build your
Vivid Seats
data feed.

Share your target events and required fields. We will review access and scope a sample before agreeing scale and delivery.

  • Platforms and event coverage
  • Fields, currency and collection context
  • Refresh schedule and delivery format
Talk through your project

Up to 5,000 characters.

Projects are accepted individually, subject to feasibility and available capacity. An enquiry does not reserve a solution or confirm delivery. Pricing and terms are agreed directly with our team.

A real person reviews your brief and follows up about scope and delivery.

ScrapingBuddy is an independent data service, not affiliated with or endorsed by Vivid Seats. We provide data collection, not ticket sales. Venue artwork is illustrative. Access and delivery commitments are agreed per project.