🛒 eBay Sold Listings & Seller Intelligence

Scrape eBay sold prices, active listings, product photos, seller feedback reviews, and full seller intelligence across 10 marketplaces — no browser, captcha-resilient, pay only for what you get.

Perfect for resale comps · product research · competitor tracking · seller lead qualification.

flowchart LR
    A["🔎 Keywords<br/>👤 Seller / store / item URLs<br/>📄 Input dataset"] --> B{"Mode"}
    B -->|KEYWORD_SEARCH| C["🧾 Listings + prices"]
    B -->|SELLER_INTELLIGENCE| D["📊 Seller intelligence"]
    C --> E["🖼️ Photos + item details"]
    D --> F["⭐ Reviews + 💰 GMV + reputation"]

---

💡 Why this Actor

• 🧠 Listings and seller intelligence — one Actor covers sold-price comps and deep seller reputation, GMV, sell-through, and tier scoring, not just raw rows
• 🌍 10 marketplaces, one schema — search US, UK, DE, FR, IT, ES, CA, AU, IE, and NL and get the same typed listing, seller, and review records back
• ⚡ No browser, captcha-resilient — a request-only curl_cffi engine with retries and optional proxy rotation runs faster and lighter than headless-browser scrapers
• 💸 Pay only for the depth you receive — failed sub-fetches, diagnostics, review rows, and filtered records are never charged
• 🔗 Paste anything that points at a seller — bare username, /usr/, /fdbk/, /str/ store, ?_ssn= search, or any /itm/ item URL resolves to the real seller automatically
• 🩺 Runs stay green — empty searches or partial failures return a readable diagnostic row instead of crashing the run

📸 Sample output

A real run's output — exportable to JSON, CSV, or Excel.

✨ What you get

🧾 Listing search (KEYWORD_SEARCH)

• Sold, completed, or active eBay listing rows
• Title, subtitle, listing ID, item URL, keyword, marketplace
• Price text, numeric price, currency, and sold date when visible
• Main product photo from the search card
• Optional item-detail enrichment: seller, listing sold count, extra photo URLs, product rating summary

📊 Seller intelligence (SELLER_INTELLIGENCE)

• Username, profile URL, feedback score, positive feedback %
• 🏅 Top Rated · 🏬 Store · 💼 Business-seller signals
• Active listing count + listing samples
• Sold count, average sold price, GMV estimates, sell-through, repeat-buyer signals
• Recent feedback reviews as separate rows (text, rating, item, buyer, price)
• Coarse tier label (SOLO → SIDE_HUSTLE → GROWING → POWER_SELLER → ENTERPRISE → MEGA) for fast lead scoring

---

🌍 Supported marketplaces

Input	Marketplace		Input	Marketplace
EBAY_US	ebay.com		EBAY_ES	ebay.es
EBAY_UK	ebay.co.uk		EBAY_CA	ebay.ca
EBAY_DE	ebay.de		EBAY_AU	ebay.com.au
EBAY_FR	ebay.fr		EBAY_IE	ebay.ie
EBAY_IT	ebay.it		EBAY_NL	ebay.nl

---

🚀 Quick start

1️⃣ Sold listings with photos (default flow)

{
  "mode": "KEYWORD_SEARCH",
  "keywords": ["iphone 15 pro max"],
  "listingType": "SOLD",
  "count": 50,
  "daysToScrape": 30,
  "ebaySite": "EBAY_US",
  "sortOrder": "endedRecently",
  "includeProductPhotos": true
}

2️⃣ Full item details + gallery photos

{
  "mode": "KEYWORD_SEARCH",
  "keywords": ["sony wh-1000xm5"],
  "listingType": "ACTIVE",
  "count": 25,
  "ebaySite": "EBAY_UK",
  "condition": "USED",
  "includeProductPhotos": true,
  "detailedSearch": true
}

3️⃣ Seller intelligence + review rows

{
  "mode": "SELLER_INTELLIGENCE",
  "profileUrls": [
    { "url": "https://www.ebay.com/usr/musicmagpie" },
    { "url": "https://www.ebay.com/itm/123456789012" }
  ],
  "ebaySite": "EBAY_US",
  "includeActiveListings": true,
  "includeSoldListings": true,
  "includeRecentFeedback": true,
  "saveReviewRows": true,
  "maxReviewRowsPerSeller": 25
}

💡 In SELLER_INTELLIGENCE you can paste anything that points at a seller: bare username, /usr/, /fdbk/, /str/ store, ?_ssn= seller-search, or any /itm/ item URL — the actor resolves the real username for you.

---

💻 Command line

$apify call trakk/ebay-seller-intelligence --input-file input.json

Minimal input.json:

{
  "mode": "KEYWORD_SEARCH",
  "keywords": ["iphone 15 pro max"],
  "listingType": "SOLD",
  "count": 50
}

---

🧭 Input guide

Field	Use it for	Default
mode	KEYWORD_SEARCH (listings) or SELLER_INTELLIGENCE (sellers)	KEYWORD_SEARCH
keywords	Product names, models, SKUs, brands, phrases	["iphone 15 pro max"]
listingType	SOLD, ACTIVE, or COMPLETED	SOLD
count	Max listing rows per keyword	50
ebaySite	Marketplace to search	EBAY_US
daysToScrape	Sold/completed date window (≤ 90 days)	30
minPrice / maxPrice	Price bounds (marketplace currency)	—
condition	New, used, refurbished, open box, parts	ANY
sortOrder	Result ordering	endedRecently
includeProductPhotos	Add product photos to rows	true
detailedSearch	Open item pages for extra photos, seller, sold count, ratings	false
profileUrls / usernames	Seller/store/item inputs (seller mode)	—
includeRecentFeedback	Add recent feedback reviews	true
saveReviewRows	Save reviews as separate rows	true
maxReviewRowsPerSeller	Cap review rows per seller	25
sampleListings	Active/sold sample size on seller rows	10
maxConcurrency	Parallel seller workflows	3

Post-fetch seller filters (all optional, off by default): minFeedbackScore, minPositivePercent, countries, mustBeTopRated, mustHaveStore, mustBeBusinessSeller, minMonthlyGmvEstimate, minActiveListings, minSoldLast90Days, tiers.

---

📤 Output views

flowchart TD
    A["🗂️ Default dataset"] --> B["🧾 Listings"]
    A --> C["💲 Pricing research"]
    A --> D["🖼️ Photos"]
    A --> E["📊 Seller intelligence"]
    A --> F["⭐ Reviews"]
    A --> G["🩺 Diagnostics"]

View	What's in it
Listings	Clean listing rows with prices and photos
Pricing research	Price-first table for sold comps
Photos	Product images + gallery URLs
Seller intelligence	Reputation, velocity, GMV
Reviews	One row per seller feedback review
Diagnostics	Clear notes for empty searches / invalid input

🧾 Example listing row

{
  "recordType": "listing",
  "keyword": "iphone 15 pro max",
  "ebaySite": "EBAY_US",
  "listingType": "SOLD",
  "listingId": "123456789012",
  "title": "Apple iPhone 15 Pro Max 256GB",
  "priceText": "$899.99",
  "priceAmount": 899.99,
  "currency": "USD",
  "soldDate": "Jun 12, 2026",
  "imageUrl": "https://i.ebayimg.com/images/g/example/s-l500.webp",
  "itemUrl": "https://www.ebay.com/itm/123456789012"
}

📊 Example seller row

{
  "recordType": "seller",
  "username": "musicmagpie",
  "ebaySite": "EBAY_UK",
  "feedbackScore": 1234567,
  "positiveFeedbackPercent": 98.8,
  "isTopRated": true,
  "hasStore": true,
  "activeListingsCount": 250000,
  "soldLast90DaysCount": 10000,
  "monthlyGmvEstimate": 750000,
  "sellThroughRate": 0.42,
  "tier": "MEGA",
  "currency": "GBP"
}

⭐ Example review row

{
  "recordType": "review",
  "username": "musicmagpie",
  "rating": "positive",
  "reviewText": "Fast delivery and item exactly as described.",
  "itemTitle": "Apple iPhone 15 Pro Max 256GB",
  "priceText": "$899.99",
  "buyer": "private buyer",
  "verifiedPurchase": true,
  "itemUrl": "https://www.ebay.com/itm/123456789012"
}

---

🏗️ How it works

The actor is a single async pipeline. A run flows through input parsing, an eBay fetch layer (retries + optional proxy rotation), an HTML parser, an intelligence layer that derives GMV / tiers, and finally typed rows written to the dataset.

flowchart TB
    subgraph IN["📥 Input"]
        K["🔤 Keywords"]
        S["👤 Sellers / URLs / dataset"]
    end

    IN --> DISP{"🎯 Mode"}

    subgraph FETCH["🌐 Fetch layer"]
        direction LR
        HTTP["curl_cffi async client<br/>🔁 retry + backoff"] --- PX["🔌 direct / 🌍 Apify proxy"]
    end

    DISP -->|KEYWORD_SEARCH| FETCH
    DISP -->|SELLER_INTELLIGENCE| FETCH

    FETCH --> PARSE["🧩 HTML parser<br/>listings · feedback · sold"]
    PARSE --> INTEL["🧠 Intelligence<br/>💰 GMV · sell-through · 🎖️ tier"]
    INTEL --> FILT["🧮 Post-fetch filters"]
    FILT --> OUT["🗂️ Typed dataset rows"]

    OUT --> R1["🧾 listing"]
    OUT --> R2["📊 seller"]
    OUT --> R3["⭐ review"]
    OUT --> R4["🩺 diagnostic"]

🔁 Per-seller fan-out (SELLER_INTELLIGENCE)

Each seller resolves to a username, then fans out into three concurrent fetches bounded by a shared concurrency semaphore, so RAM and proxy use stay predictable no matter how many sellers you pass in.

sequenceDiagram
    participant A as ⚙️ Actor
    participant E as 🌐 eBay
    participant D as 🗂️ Dataset
    A->>E: 👤 feedback profile (required)
    A->>E: 🟢 active listings (optional)
    A->>E: 💰 sold listings + GMV (optional)
    E-->>A: HTML pages
    A->>A: 🧠 parse → intelligence → filters
    A->>D: 📊 seller row + ⭐ review rows
    Note over A,D: Failed sub-fetch → 🩺 diagnostic row, run stays 🟢 green

---

💸 Pricing (pay-per-event)

Recommended setup — you pay only for the depth you actually receive:

Event	Fires when	Suggested price
listing_item	One listing row from keyword search	$1.00 / 1,000
seller_profile	Base seller intelligence row	$5.00 / 1,000
seller_active_summary	Active-listings add-on succeeded	$3.00 / 1,000
seller_sold_intelligence	Sold velocity + GMV add-on succeeded	$7.00 / 1,000

Failed sub-fetches, diagnostics, review rows, and filtered records are not charged. Listings are the fast, affordable workflow; seller intelligence is a premium enrichment layer.

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🎯 Use cases

• 📈 Sold-price research & resale comps
• 🔬 Product demand validation
• 📊 Marketplace pricing dashboards
• 🕵️ Competitor seller tracking
• 🎯 Power-seller lead lists
• 🤝 Wholesale, sourcing & partnership research
• 📦 Active inventory monitoring
• ⭐ Review & reputation analysis

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❓ FAQ

Can I get product photos? Yes — keep includeProductPhotos on. Turn on detailedSearch for gallery URLs from item pages.

Can I get review text? Yes, in seller-intelligence mode. Feedback reviews are saved as separate review rows when saveReviewRows is on.

Can I get product review ratings? Sometimes — if eBay exposes a rating/review summary on the item page, detailedSearch captures it.

How far back does sold data go? eBay's public sold/completed history is roughly 90 days.

Why do I see a diagnostic row? The run finished, but eBay returned no usable rows for that keyword, filter set, or seller input. The run stays green — you get a readable note instead of a failure.

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🏷️ Keywords

eBay sold listings · eBay scraper · sold prices · price research · product photos · seller reviews · seller intelligence · GMV estimate · resale comps · marketplace analytics · seller leads · eBay completed listings