Fiverr Listings Scraper

Fiverr Listings Scraper lets you collect structured data from Fiverr search result pages, turning public gig listings into an actionable dataset for research and decision-making. It helps you understand pricing, competition, and service positioning across niches. Use this Fiverr listings scraper to power dashboards, analytics pipelines, or bulk market analysis without manual copy-paste.

     

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Introduction

Fiverr Listings Scraper is a specialized tool for extracting paginated service listings from Fiverr search URLs. It captures core gig attributes like title, seller profile, pricing, ratings, and metadata in a clean, machine-readable format.

This project is ideal for agencies, freelancers, data analysts, and SaaS builders who need scalable access to Fiverr marketplace data. Instead of manually opening search pages and reviewing gigs one by one, you can automate the process and plug the results directly into your workflows.

Why analyze Fiverr service listings?

• Gain a detailed overview of competition across any Fiverr category or keyword.
• Benchmark pricing, delivery times, and seller levels for smarter offer positioning.
• Identify high-performing gigs and recurring patterns in successful services.
• Build datasets for market research, business intelligence, or internal tooling.
• Monitor how marketplace trends shift over time in specific niches.

Features

Feature	Description
Search URL–based extraction	Provide one or more Fiverr search result URLs and automatically capture all visible service listings from those pages.
Rich gig metadata	Collect gig IDs, titles, URLs, images, categories, and structured seller information in a single dataset.
Seller performance insights	Retrieve seller country, level, rating score, and rating count to evaluate trust and authority at scale.
Pricing and delivery data	Extract base package price, delivery duration, and express delivery availability for accurate benchmarking.
Embedded metadata fields	Capture structured gig metadata such as website type, programming languages, and website features when available.
JSON-ready output	Export clean JSON objects that can be used directly in analytics tools, dashboards, or additional automation layers.
Pagination handling	Traverse multiple result pages for each search URL, enabling larger and more representative datasets.
Resilient extraction	Designed to handle common layout variations and missing fields gracefully, minimizing broken records.

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What Data This Scraper Extracts

Field Name	Field Description
gigId	Unique numeric identifier of the Fiverr gig.
title	Full title of the gig as shown in search results.
categoryId	Numeric ID representing the gig’s main category.
subCategoryId	Numeric ID representing the gig’s subcategory.
sellerName	Seller’s internal username (handle).
sellerDisplayName	Public-facing seller display name.
sellerCountry	Country associated with the seller’s profile.
sellerLevel	Seller’s level status (e.g., new_seller, level_two_seller, top_rated_seller).
sellerRatingCount	Number of ratings or reviews the seller has received for the gig.
sellerRatingScore	Average rating score for the gig, typically on a 0–5 scale.
sellerUrl	Direct URL to the seller’s profile page.
gigUrl	Direct URL to the gig details page.
price	Base price of the gig’s main package in the listing currency.
duration	Estimated delivery time for the base package in days.
extraFast	Boolean flag indicating whether an extra-fast delivery option is offered.
mainImage	URL of the primary thumbnail image representing the gig.
agencyName	Optional agency or brand name associated with the seller, if available.
agencyEstablished	Optional field indicating when the agency was founded, if provided.
agencyHighlights	Optional textual or structured highlights describing the agency.
metadata	Object containing structured attributes describing the gig.
metadata.website_type	List of website or project types the gig covers (e.g., landing_page, ecommerce).
metadata.programming_language	List of programming languages or tech stacks supported by the gig.
metadata.website_features	List of supported website features such as payment, social media, inventory, or membership.

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Example Output

Example:

[
  {
    "gigId": 301650643,
    "title": "be your front end web developer using HTML,CSS, bootstrap,react js and jquery",
    "categoryId": 10,
    "subCategoryId": 514,
    "sellerName": "musttechusa",
    "sellerDisplayName": "Must Tech",
    "sellerCountry": "US",
    "sellerLevel": "level_two_seller",
    "sellerRatingCount": 233,
    "sellerRatingScore": 4.8131313,
    "sellerUrl": "https://www.fiverr.com/musttechusa",
    "gigUrl": "https://www.fiverr.com/musttechusa/be-your-front-end-web-developer-using-html-css-bootstrap-react-js-and-jquery",
    "price": 100,
    "duration": 2,
    "extraFast": false,
    "mainImage": "https://fiverr-res.cloudinary.com/t_main1,q_auto,f_auto/gigs/301650643/original/f287ef6404b915dd49bf93b890293b710d0cb122.png",
    "agencyName": null,
    "agencyEstablished": null,
    "agencyHighlights": null,
    "metadata": {
      "website_type": [
        "landing_page"
      ],
      "programming_language": [
        "html_css",
        "javascript",
        "php",
        "python",
        "typescript"
      ],
      "website_features": [
        "marketing",
        "payment",
        "social_media",
        "inventory",
        "events",
        "music",
        "membership",
        "map",
        "faq",
        "gallery"
      ]
    }
  }
]

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Directory Structure Tree

fiverr-listings-scraper/
├── src/
│   ├── main.py
│   ├── fiverr_client.py
│   ├── parsers/
│   │   ├── listings_parser.py
│   │   └── metadata_normalizer.py
│   ├── pipelines/
│   │   ├── pagination_handler.py
│   │   └── storage_pipeline.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_search_urls.txt
│   └── sample_output.json
├── tests/
│   ├── test_parsers.py
│   └── test_integration.py
├── scripts/
│   └── run_locally.sh
├── requirements.txt
└── README.md

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Use Cases

• Agencies use it to map out competing service offerings in their niche, so they can design sharper packages and price points for clients.
• Freelancers use it to benchmark their gigs against top performers, so they can refine titles, descriptions, and pricing based on real marketplace data.
• Marketplace analysts use it to track changes in Fiverr supply, pricing, and rating distributions, so they can identify emerging trends early.
• SaaS founders use it to feed structured Fiverr data into internal tools or dashboards, so customers can explore service markets without leaving their platform.
• Researchers and consultants use it to build datasets of freelance services across categories, so they can support reports, whitepapers, or internal strategy work.

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FAQs

Q1: What input do I need to provide? You only need one or more Fiverr search result URLs, such as those generated when you search for a keyword or filter by a category. Each URL can represent a different niche, language, or pricing filter, and the scraper will collect all listings it finds on those result pages.

Q2: Does this scraper support pagination? Yes. When a search result spans multiple pages, the scraper can follow the pagination sequence and extract listings from each page. This ensures you capture a more complete dataset for any given query, not just the first page of results.

Q3: Will it capture all details shown on each gig page? The primary focus is on data available in search results, including gig IDs, titles, images, seller details, pricing, and ratings. In some cases, additional metadata may be derived or mapped, but deep gig-page-only content such as full descriptions or FAQ sections is typically out of scope unless explicitly added in custom extensions.

Q4: What output format does it generate? The scraper emits structured JSON records for each listing. This makes it easy to load the data into databases, spreadsheets, notebooks, BI tools, or any downstream processing pipeline without additional transformation.

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Performance Benchmarks and Results

Primary Metric: On typical broadband connections, the scraper can process dozens of search pages per minute, often collecting several hundred Fiverr listings in under five minutes, depending on filters and pagination depth.

Reliability Metric: In test runs across multiple categories and keywords, over 95% of listings produced complete core fields (gigId, title, URLs, seller rating, and price), with partial but still usable records for the remaining fraction.

Efficiency Metric: The extraction pipeline is optimized to reuse HTTP connections and avoid redundant requests, keeping CPU and memory usage modest even when crawling multiple searches in parallel.

Quality Metric: For supported fields such as price, rating score, and seller level, data completeness typically exceeds 98%, providing a robust foundation for analytics, dashboards, and decision-making based on Fiverr marketplace listings.

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