Etsy Sales Intelligence

The Etsy seller intelligence estimator. Etsy hides per-listing sales counts; this actor reverse-engineers them from public signals — and bills you $0.05 per listing analysed. No setup. No external accounts. No infrastructure.

💸 $0.05 per listing. All inclusive. Bright Data anti-bot bypass, Claude Haiku translation, multilingual extraction, proxies, retries, compute — every cost is baked in. You pay only when a listing record is successfully delivered to your dataset.

Pricing fine print. The headline rate is $0.05 per listing (event listing-scraped). Apify auto-adds two tiny platform events: $0.00005 once per run start (first 5 seconds of compute waived) and $0.00001 per dataset record. These add ~$0.00006 extra per listing — a 0.1% uplift, effectively rounding error. The tables below use the headline $0.05.

TL;DR (2026)

• What it does. Estimates lifetime units sold and revenue for any Etsy listing, shop, or keyword search.
• How. Reverse-engineers per-listing sales from listingReviews / effectiveReviewRate with price-tier and niche calibration, plus an adaptive ±25%/±35%/±50% confidence band.
• What it costs. $0.05 per successfully analysed listing. No subscription, no setup fee, failed scrapes are free.
• Who it replaces. A pay-per-use alternative to subscription tools like eRank, EtsyHunt, Marmalade, and Sale Samurai.
• How you call it. Apify Console UI, REST API, Apify Integrations (Zapier, Make, n8n), or as an MCP tool from Claude Desktop, Claude Code, Cursor, Cline, and Continue.
• Accuracy. Unvalidated. The estimate is a documented calculation, not a measurement, and no ground-truth corpus exists to score it against (see Limitations). confidenceNote states the inputs and the band on every record so you can judge each one.

What is an Etsy sales estimator?

An Etsy sales estimator is a tool that calculates the number of items a listing or shop has sold even though Etsy itself hides per-listing sales counts. It does this by combining three public signals: the number of reviews on the listing, the shop's lifetime sales-to-reviews ratio, and the price tier of the product. Etsy Sales Intelligence is an Apify Actor that performs this calculation deterministically, adds adaptive confidence bands, and returns structured JSON or CSV so the data can drop straight into a spreadsheet, BI dashboard, or LLM workflow.

Worked example

A listing has 5 reviews. The shop reports 1,200 sales / 257 reviews (a 21% review rate). Estimated lifetime units sold = 5 / 0.21 ≈ 23. At $24.50 USD that is roughly $564 lifetime revenue with a low/high band of $282 – $846. Every record carries a confidenceNote explaining the math, plus pre-formatted display strings (estimatedRevenueDisplay: "$564") for spreadsheets and reports.

Built for

• Etsy sellers benchmarking competitors before a price/photo refresh
• Niche researchers hunting hidden winners ahead of the crowd
• Print-on-demand operators sizing demand before paying for a sample run
• Dropshippers validating product ideas with real revenue numbers
• E-commerce analysts feeding raw JSON into BI tools, LLMs, or dashboards

Subscription tools (eRank, EtsyHunt, Sale Samurai) charge $20–$30 per month for the same reverse-engineered estimate locked behind a UI. This actor returns raw structured data into your Apify dataset on demand — pay only for what you scrape.

Just run it

1. Open the actor in Apify Console
2. Choose a mode:
   • listing — paste one or more Etsy listing URLs to analyse specific products
   • shop — paste an Etsy shop URL to audit all listings in that shop
   • search — type a keyword to research a niche (top results)
3. Set maxResults (default 50)
4. Click Run

That's it. No API keys, no Bright Data signup, no Anthropic key. The actor handles every layer of infrastructure for you.

Use it from Claude, Cursor, and other MCP clients

Etsy Sales Intelligence is a native MCP server (Model Context Protocol). The same actor runs in two modes against the same scrapers, estimator, and $0.05 PPE billing:

1. Classic run mode — Apify Console UI, REST API, Apify Integrations (Zapier, Make, n8n).
2. MCP standby mode — long-lived HTTP server exposing three MCP tools at a persistent URL. Scale-to-zero: the container is only running when an MCP client calls it.

MCP tools exposed

• estimate_listing — analyse one or more specific Etsy listing URLs.
• audit_shop — discover and audit every listing in a shop.
• scan_niche — search Etsy for a keyword and analyse top-ranking listings.

Each tool returns a compact JSON object with per-listing estimates and a shop/niche-level aggregate, ready for the LLM to summarise.

Direct MCP endpoint (recommended)

The actor's persistent MCP endpoint:

https://marielise-dev--etsy-sales-intelligence.apify.actor/mcp

Configure once in your MCP client. The endpoint speaks Streamable HTTP. Authentication uses your Apify token.

Claude Desktop / Claude Code

Add to claude_desktop_config.json (or run claude mcp add in Claude Code):

{
  "mcpServers": {
    "etsy-sales-intelligence": {
      "url": "https://marielise-dev--etsy-sales-intelligence.apify.actor/mcp",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Then in chat: "Use estimate_listing on https://www.etsy.com/listing/1234567890/example. Cap at 5 listings." Claude picks the right tool, calls the endpoint, returns the structured estimate.

Cursor / Cline / Continue / Windsurf

All accept the same URL + token config via their MCP settings panel. The three tools (estimate_listing, audit_shop, scan_niche) appear as first-class callable tools.

Alternative — discover through the Apify MCP aggregator

You can also reach this actor through Apify's generic MCP server (which lists every public actor on Apify Store). Useful if you already have Apify MCP configured for other actors:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": ["-y", "@apify/actors-mcp-server"],
      "env": { "APIFY_TOKEN": "YOUR_APIFY_TOKEN" }
    }
  }
}

The LLM uses search-actors + call-actor to find and invoke marielise.dev/etsy-sales-intelligence. Slightly more tool-call hops, but you keep one MCP server for all your actors.

Cost safety when calling from an LLM

LLMs sometimes overshoot. The MCP tools enforce stricter caps than classic run mode:

• maxResults is hard-capped at 50 in MCP mode (vs 500 in classic run mode).
• Default maxResults when the LLM omits it: 10.
• Filters (minReviews, minEstimatedRevenue) skip listings without billing them.
• Tell the LLM a budget in your brief: "…with at most 20 listings".
• Set a Max total charge on the actor from the Apify Console for a hard spend ceiling per session.

Scale-to-zero

The MCP server runs in Apify Standby mode with scale-to-zero enabled. The container spins up on the first MCP request and turns down after the configured idle timeout (default 5 minutes). You pay $0.05 per analysed listing — there is no idle-time charge.

Pricing

Run size	You pay	Time at default concurrency=5
1 listing	$0.05	~30 s
10 listings	$0.50	~1 min
50 listings	$2.50	~5 min
100 listings	$5.00	~10 min
500 listings	$25.00	~50 min
1,000 listings	$50.00	~1.7 h
5,000 listings	$250.00	~8 h

Linear per-listing — no subscription, no setup fee, no minimum. Your maxResults input directly caps your spend.

What's NOT charged

• Failed scrapes (target page 404, target down) → free
• Listings filtered out by your minReviews / minEstimatedRevenue thresholds → free
• Listings cancelled by maxResults cap → free
• Empty preview runs → free

You pay only for records successfully pushed to your dataset.

How it compares to eRank, EtsyHunt, Marmalade, and Sale Samurai

Capability	Etsy Sales Intelligence	eRank	EtsyHunt	Marmalade	Sale Samurai
Pricing model	Pay-per-listing ($0.05)	$5.99–$29.99 / month	$19.99 / month	$19 / month	$9.99–$29.99 / month
Minimum commitment	None	Monthly	Monthly	Monthly	Monthly
Raw structured JSON output	Yes (dataset)	No (dashboard only)	Limited CSV	Limited CSV	Limited CSV
Per-listing sales estimate	Yes (with confidence band)	Yes	Yes	Yes	Yes
Shop-level lifetime audit	Yes	Partial	Yes	Limited	Yes
Keyword/niche scan	Yes	Yes	Yes	Yes	Yes
Non-English Etsy locales	Auto-translated (Haiku)	English only	English only	English only	English only
Confidence band per record	Yes (±25/35/50%)	No	No	No	No
Trending / dormant flags	Yes	No	Limited	No	Limited
Programmatic API	Yes (REST + Apify SDK)	No public API	No	No	No
Callable from Claude / MCP	Yes	No	No	No	No
Zapier / Make / n8n integration	Yes (Apify Integrations)	No	No	No	No
Failed-scrape billing	Free	n/a	n/a	n/a	n/a

Breakeven: subscription tools cost $10–$30/month. At $0.05/listing you pay less unless you audit more than 200–600 listings every month. Most sellers and researchers run far fewer one-off audits, so pay-per-listing comes out cheaper and leaves you owning the raw data.

Output

One record per analysed listing pushed to your dataset, plus a RUN_SUMMARY.json aggregate written to the run's key-value store.

Units (read this once)

• All price* and *Revenue*USD numbers are whole US dollars, NOT cents, rounded to 2 decimals. estimatedRevenueUSD: 84656 means $84,656.00.
• Every numeric field has a pre-formatted *Display sibling for table UIs and reports (estimatedRevenueDisplay: "$84,656").
• shopReviewRate is a 0..1 ratio. shopReviewRatePct is the same value as integer percentage 0..100.
• Counts are integers. Timestamps are ISO 8601 UTC.

Per-listing fields

Field	Type	Unit	Description
listingId	string	—	Etsy numeric listing ID
listingUrl	string	URL	Canonical listing URL
status	string	—	"ok", "blocked", or "failed". Only ok records carry an estimate and only ok records are billed. Filter on this field.
failureKind	string|null	—	On non-ok records: unblocker-credential, anti-bot, parse, or network
failureReason	string|null	—	On non-ok records: plain-English cause
title	string	—	Listing title (auto-translated to English when Etsy serves a non-English variant)
price	number|null	original currency	Whole units, e.g. 24.50
priceDisplay	string|null	—	"€24.50"
priceUSD	number|null	USD whole dollars	Live FX-converted, 2 dp
priceUSDDisplay	string|null	—	"$24.50"
currency	string|null	ISO 4217	Original currency code
category	string|null	—	Listing category breadcrumb
tags	string[]	—	Listing tags. Best-effort: often empty on English-locale renders (Etsy lazy-loads them via XHR). See Limitations.
images	string[]	URLs	Listing image URLs
listingReviews	integer	count	Reviews on this specific listing
viewsLast24h	integer|null	count	When Etsy exposes a counter
inCarts	integer|null	count	When Etsy shows the cart-count badge
badges	string[]	—	["Bestseller", "Star Seller", "Quick Replies"] etc
reviewDates	string[]	ISO datetime	Recent listing review timestamps
trending	boolean	—	True when 6+ of the last 10 extracted review dates fall within 90 days, and at least 5 dates were extracted
dormant	boolean	—	True when no review in the last 180 days
shopName	string	—	Etsy shop slug
shopUrl	string	URL	Etsy shop URL
shopTotalSales	integer|null	count	Lifetime sales reported by the shop page
shopTotalReviews	integer|null	count	Aggregated shop reviews
shopReviewRate	number|null	ratio 0..1	shopTotalReviews / shopTotalSales, clamped 0.05–0.50
shopReviewRatePct	integer|null	percent 0..100	Same value × 100 for table views
effectiveReviewRate	number	ratio 0..1	Shop rate after niche blend AND price-tier adjustment — the value actually used in estimation
effectiveReviewRatePct	integer	percent 0..100	Same × 100
nicheBlendApplied	boolean	—	True when the shop rate was blended with the niche median (run had 3+ distinct shops AND shop totals were measured)
observedReviewRate	number|null	ratio 0..1	Bayesian observed shop rate from 4+ trackOverTime snapshots. Null until accumulated.
observedReviewRatePct	integer|null	percent 0..100	Same × 100
observedRateSampleCount	integer	count	How many snapshots produced the observed rate
plausibility	enum|null	"plausible" / "suspicious" / null	AI sanity-check flag; null when disabled or check failed
plausibilityReason	string|null	—	Short explanation when plausibility = "suspicious"
salesPerYearShop	integer|null	count/yr	Shop average annual sales (lifetime ÷ age)
salesPerYearShopDisplay	string|null	—	"427 / yr"
listingAgeYears	number|null	years	Listing age inferred from oldest review date
listingSalesPerYearEstimate	integer|null	count/yr	Per-listing velocity = estimatedUnitsSold / listingAgeYears
listingSalesPerYearDisplay	string|null	—	"35 / yr"
shopAge	string|null	—	"6 years"
shopLocation	string|null	—	Free-form location of the shop, e.g. "Miami, Florida"
shipsFromCountry	string|null	ISO-3166-1 alpha-2	Country the listing ships from, e.g. "US"
shipsFromRegion	string|null	—	Region/state, e.g. "FL"
estimatedUnitsSold	integer	count	Central lifetime estimate
estimatedUnitsSoldDisplay	string	—	"1,480"
estimatedSalesLow / estimatedSalesHigh	integer	count	Adaptive ±25–50% band (see "How the estimate works")
estimatedSalesRangeDisplay	string	—	"740 – 2,220"
estimatedRevenueUSD	number	USD whole dollars	Central × priceUSD
estimatedRevenueDisplay	string	—	"$84,656"
estimatedRevenueLowUSD / estimatedRevenueHighUSD	number	USD whole dollars	Same adaptive band, applied to revenue
estimatedRevenueRangeDisplay	string	—	"$42,328 – $126,984"
confidence	enum	"high" / "medium" / "low"	Sortable confidence tier mirroring the band
confidenceNote	string	—	Plain-English explanation including price-tier adjustment, trending uplift, and band tier
competingListings	integer|null	count	Search-mode only
scrapedAt	string	ISO datetime	Record timestamp
delta	object|null	—	Populated only when trackOverTime is on and a previous snapshot exists

Run aggregate (KV RUN_SUMMARY.json)

A single object summarising the run, written every time:

{
  "mode": "search",
  "scrapedAt": "2026-04-30T08:00:00.000Z",
  "totalCandidates": 50,
  "successfullyScraped": 48,
  "pushedToDataset": 42,
  "skippedFilters": 6,
  "failedScrapes": 2,
  "totalEstimatedRevenueUSD": 1284500,
  "totalEstimatedRevenueDisplay": "$1,284,500",
  "totalEstimatedUnitsSold": 36420,
  "totalEstimatedUnitsSoldDisplay": "36,420",
  "averagePriceUSD": 28.40,
  "averagePriceDisplay": "$28",
  "trendingCount": 14,
  "dormantCount": 7,
  "topByRevenue": [
    {
      "listingId": "1570282475",
      "title": "Custom Hand-Painted Pet Portrait Leather Keyring",
      "shopName": "BeanieBaeArt",
      "estimatedRevenueUSD": 84656,
      "estimatedRevenueDisplay": "$84,656",
      "listingUrl": "https://www.etsy.com/listing/1570282475/..."
    }
  ],
  "topByUnits": [
    {
      "listingId": "1234567890",
      "title": "Sticker Pack — 10 Pieces",
      "shopName": "StickerWorld",
      "estimatedUnitsSold": 12500,
      "estimatedUnitsSoldDisplay": "12,500",
      "listingUrl": "https://www.etsy.com/listing/1234567890/..."
    }
  ],
  "shipsFromBreakdown": [
    { "country": "US", "listings": 28, "share": 0.667, "sharePct": 67, "sharePctDisplay": "67%", "totalEstimatedRevenueUSD": 856200, "totalEstimatedRevenueDisplay": "$856,200" },
    { "country": "GB", "listings": 9,  "share": 0.214, "sharePct": 21, "sharePctDisplay": "21%", "totalEstimatedRevenueUSD": 312800, "totalEstimatedRevenueDisplay": "$312,800" },
    { "country": "CA", "listings": 5,  "share": 0.119, "sharePct": 12, "sharePctDisplay": "12%", "totalEstimatedRevenueUSD": 115500, "totalEstimatedRevenueDisplay": "$115,500" }
  ]
}

Caveat on shipsFromBreakdown. This is a seller-side distribution: which countries the listings ship from. It is not a buyer-country / sales-destination split. Etsy does not expose buyer-country sales mix on public pages; only the shop owner can see that via Etsy Stats. The breakdown is still useful for niche research (e.g. "is this niche dominated by US sellers or international?") — just don't market it as buyer demographics.

Input examples

One specific listing

{
  "mode": "listing",
  "listingUrls": [
    { "url": "https://www.etsy.com/listing/1234567890/example-product" }
  ],
  "maxResults": 1
}

Cost: $0.05.

Audit a competitor's full shop

{
  "mode": "shop",
  "shopUrl": "https://www.etsy.com/shop/CompetitorShopName",
  "maxResults": 50,
  "minReviews": 1
}

Cost: up to $2.50 (50 × $0.05).

Niche research

{
  "mode": "search",
  "searchQuery": "minimalist wall art",
  "minReviews": 5,
  "minEstimatedRevenue": 500,
  "maxResults": 100
}

Cost: up to $5.00. Filters drop low-signal listings before they reach the dataset, so actual charge is usually lower.

Track a shop weekly

{
  "mode": "shop",
  "shopUrl": "https://www.etsy.com/shop/MyShopToTrack",
  "trackOverTime": true,
  "maxResults": 50
}

Run on a weekly schedule. Each record gains a delta block: reviewsDelta, estimatedSalesDelta, salesPerDay, daysSinceLast. First run establishes the baseline; subsequent runs compute growth.

How the estimate works

1. Fetch the listing page → extract listingReviews, badges, recent review timestamps, ships-from country.

2. Fetch the shop page (cached once per run per shop) → extract shopTotalSales, shopTotalReviews, shopAge, shopLocation.

3. shopReviewRate = shopTotalReviews / shopTotalSales, clamped to [0.05, 0.50] to suppress wild estimates from tiny samples. Falls back to 0.20 (Etsy-wide observed midpoint) when shop totals are unavailable.

4. Price-tier adjustment. Cheap items get fewer reviews per sale than expensive ones, even within the same shop. The shop rate is multiplied by a price-tier coefficient:

   Listing price (USD)	Coefficient	Why
   < $20	× 0.70	Impulse buyers leave fewer reviews
   $20 – $100	× 1.00	Baseline
   $100 – $300	× 1.20	More engaged buyers
   > $300	× 1.40	High-ticket reviewers most diligent

   Result is effectiveReviewRate, also clamped to [0.05, 0.50].

   These four coefficients are unvalidated heuristics, seeded from anecdotal seller reports rather than measured data. They are published here so you can see exactly what was applied and back it out if you disagree: effectiveReviewRatePct and shopReviewRatePct are both on every record, so estimatedUnitsSold × effectiveReviewRate ÷ shopReviewRate recovers the un-adjusted estimate.

5. estimatedUnitsSold = round(listingReviews / effectiveReviewRate).

6. estimatedRevenueUSD = estimatedUnitsSold × priceUSD.

7. Adaptive confidence band:

   • ±25 % when shop totals are real (measured) AND listing has 50+ reviews → confidence: "high"
   • ±35 % when shop totals are real but listing has 10–49 reviews → confidence: "medium"
   • ±50 % when shop totals are assumed (20 % default) OR listing has < 10 reviews → confidence: "low"

   The confidence field is sortable for filtering; confidenceNote explains the math in plain English.

8. Trending uplift. When the listing's review velocity flags it as trending = true (6+ of the last 10 reviews within 90 days), the band is shifted asymmetrically: the low edge tightens by × 0.95 and the high edge widens by × 1.15. Reflects the empirical observation that trending listings tend to over-perform their historical-average estimate.

9. Velocity signals. Two derived fields turn lifetime numbers into per-year flows:

   • salesPerYearShop = shopTotalSales / parseShopAgeYears — average annual shop pace.
   • listingSalesPerYearEstimate = estimatedUnitsSold / listingAgeYears, where listingAgeYears is derived from the oldest extracted review date. This is a floor on the listing's age, not its real age — Etsy shows only recent reviews, so a listing with thousands of sales can surface a handful of dates spanning weeks. Both fields are null unless at least 5 review dates were extracted, rather than reporting an age the sample cannot support.

10. Niche calibration (two-pass estimation). When a run touches 3+ distinct shops, the actor switches to a two-pass estimator:

    • Pass 1 scrapes every listing + shop in parallel and stashes the raw data — no estimates yet.
    • Between passes, it computes nicheReviewRate = median shop review rate across distinct shops in the run.
    • Pass 2 estimates each listing with the shop rate blended toward the niche: blendedShopRate = 0.7 × shopRate + 0.3 × nicheRate. Outlier shops (e.g. one at 8 % in a niche where median is 22 %) are pulled toward the niche norm, reducing per-listing estimation error.
    • The blend is only applied when shop totals are measured (not assumed). Per-listing records flag nicheBlendApplied: true and the confidenceNote describes the blend.
    • With fewer than 3 distinct shops (typical for listing mode with one URL), the blend is skipped — rate stays as-is.

11. Bayesian observed rate (only with trackOverTime: true, kicks in after 4+ weekly snapshots per shop). Each run appends a per-shop snapshot of (shopTotalSales, shopTotalReviews) to KV. Once 4+ snapshots exist, the actor computes observedRate = (lastReviews − firstReviews) / (lastSales − firstSales) — i.e. the actual review rate during the tracked period, not the lifetime average. This observed rate is then blended with the lifetime rate as posteriorShopRate = 0.6 × observed + 0.4 × lifetime and used as the shop's effective rate for the rest of the pipeline. Most accurate signal we have. Per-record fields observedReviewRate, observedReviewRatePct, and observedRateSampleCount expose what fired.

12. Plausibility check. Every record passes through deterministic rules that catch math impossibilities (listingReviews > shopTotalReviews, estimatedUnitsSold > shopTotalSales, priceUSD ≤ 0, placeholder titles). Setting plausibilityCheck: true additionally runs a Claude Haiku pass for softer outliers; this is off by default because the model occasionally flags legitimate single-hit shops. Output fields plausibility and plausibilityReason flag records for review without removing them from the dataset.

13. trending = true when 6+ of the last 10 extracted review dates fall within 90 days — and at least 5 dates were extracted. Etsy exposes only a handful of recent reviews in the page, and those are recent by definition, so on a 2-date sample "most reviews are recent" is arithmetic, not a trend. Below the threshold the flag stays false.

14. dormant = true when no extracted review date falls within 180 days. When no dates could be extracted at all, dormant is false (unknown) rather than true — except on listings with zero reviews, where it is genuinely dormant.

What we deliberately did NOT build (and why)

Two improvements were considered for v2 but skipped on engineering principle, not laziness:

• Calibration corpus. Validating the algorithm's coefficients against a curated set of Etsy shops with publicly disclosed lifetime sales would let us tune coefficients with statistical confidence. Skipped because the realistic public corpus is ~10–15 shops (interviews, podcasts, indie-maker tweets) — too small for confident tuning, and self-reported numbers are noisy. The right way to do this is to collect anonymised real customer run outputs over months, backtest against any sellers who later publicly disclose, and refine quarterly. Pre-launch this is impossible.
• Category-specific default rates. Same data problem: without ground truth or large samples, hard-coding per-category rates would just be more wrong-confidence numbers stacked on existing ones. Price-tier adjustment (already implemented) captures most of what category would, more reliably.

If you're a researcher with verified shop sales data and would like to help calibrate, the source is open on Apify — open an issue.

Limitations

• Estimates, not facts. Etsy never reveals true per-listing sales. Treat output as directional intelligence with a stated confidenceNote.
• Mixed-price shops skew per-listing estimates. A shop selling one $5 sticker and one $500 sculpture distorts both estimates toward the average review rate. The clamp [0.05, 0.50] cushions this but does not eliminate it.
• Currency rates refresh once per run and are persisted to KV CURRENCY_RATES.json for transparency.
• tags is best-effort and frequently empty. Etsy serves tag data inline only on some locale variants. On the English (US) variant tags are lazy-loaded via XHR after page render, and the underlying scrape captures pre-hydration HTML — so the tags array is empty in that case. The translator never invents tags from the title or description; what you see is what Etsy actually shipped in the initial HTML. Estimates do not depend on tags.
• listingFavourites is no longer exposed by Etsy publicly (deprecated by Etsy ~2024). The field has been removed from the actor.
• Buyer-country distribution is not available. shipsFromBreakdown reports where listings ship from, not where buyers are. Etsy only exposes buyer-country sales mix to the shop owner via Etsy Stats.

FAQ

How accurate is the estimate?

There is no measured accuracy figure, and any tool that quotes you one for Etsy is guessing. Etsy never publishes per-listing sales, so no public ground truth exists to score an estimator against.

What the actor does give you is a stated method and a stated uncertainty. The band is adaptive: ±25% when both shop totals are measured and the listing has 50+ reviews (confidence: high), ±35% when the listing has 10–49 reviews (medium), and ±50% when shop totals are assumed or the listing has fewer than 10 reviews (low). Those tiers rank records against each other by how much real signal went in — they are not calibrated error rates.

The estimate is most trustworthy when shopTotalSales and shopTotalReviews are both non-null (the review rate is then measured for that specific shop rather than assumed) and the listing has a large review count. It is weakest on shops whose totals could not be read, where a flat 20% rate is assumed for every shop alike. confidenceNote spells out which case each record is.

Why doesn't Etsy show sales counts directly?

Etsy removed per-listing sales counts to discourage clones. Public reviews, shop totals, and badges remain — exactly the signals this actor consumes.

Do I need a Bright Data account or Anthropic key?

No. Everything is included in the $0.05 per-listing price. The publisher absorbs all infrastructure (anti-bot bypass, translation, proxies, compute). You just run.

Why "$0.05" and not a tiny number with a subscription?

The actor uses Apify's Pay-Per-Event model — you pay only when a complete listing record lands in your dataset. No subscription, no setup fee, no compute charges. A failed scrape, a filtered-out listing, or a run that hits its maxResults cap costs you nothing. Compared with subscription tools like eRank ($10/mo) or EtsyHunt ($20/mo), this actor breaks even at ~200-400 listings per month — and gives you raw structured data instead of a locked-in dashboard.

Can I run this against thousands of listings?

Yes. A 5,000-listing run costs $250 (5,000 × $0.05). Increase the concurrency input (default 5, max 20) to speed up — each step adds parallel scraping requests. Split into batches if a single Apify run exceeds memory or time limits.

Does the actor scrape in parallel?

Yes. Listings are processed in batches of concurrency (default 5) using Promise.allSettled, with shared shop-fetch deduping (concurrent listings from the same shop reuse a single in-flight shop request). At default concurrency a 50-listing run finishes in roughly 4–6 minutes versus ~25 minutes sequentially.

Can the actor tell me which countries are buying most?

No — Etsy does not expose buyer-country sales splits on public pages. Only the shop owner sees that via Etsy Stats. The shipsFromBreakdown aggregate in RUN_SUMMARY.json is a seller-side distribution (where the listings ship from), not a buyer-country mix.

Why is tags sometimes empty?

Etsy A/B tests its render path. On the English (US) variant tags are lazy-loaded via XHR after the page loads; the scrape captures the pre-hydration HTML and tags are missing. On other locale variants Etsy inlines tags. The actor does not invent tags from the title — what you see is what Etsy shipped in the HTML. Estimates do not depend on tags.

How do I track changes over time?

Set trackOverTime: true and re-run with the same input on a schedule. Each record gains a delta block once a previous snapshot exists.

What if a scrape fails?

The $0.05 listing charge fires only for records with status: "ok". A blocked or failed listing never triggers it, so a run that scrapes nothing costs you nothing beyond Apify's own $0.00005 run-start event and $0.00001 per dataset row — fractions of a cent.

A failure still appears in your dataset as a row with status: "blocked" or "failed", a failureKind, and a plain-English failureReason. That is deliberate: a run that scraped nothing and a run that legitimately found nothing produce very different rows, and you should not have to read the log to tell them apart. Filter to status = "ok" for the estimates, and check the other rows when a run returns less than you expected.

Can I export to Google Sheets / Airtable / a webhook?

Yes — every Apify dataset can be exported via the Console UI or piped through Apify Integrations to Google Sheets, Airtable, Slack, Make, Zapier, or a custom webhook. See Apify Integrations docs.

How this is built

The actor stack — fully managed, you don't pay or configure any of it:

• Bright Data Web Unlocker for anti-bot bypass on Etsy listing and shop pages. Etsy fronts its pages with DataDome, which hard-blocks plain HTTP clients and headless browsers alike — including real Chrome over a residential IP — so a managed unblocker is not optional here.
• Claude Haiku 4.5 for translating cosmetic fields (title, category, tags, badges) when Etsy serves a non-English variant. Numeric fields are never sent to the LLM.
• Multilingual regex in the shop scraper extracts numeric stats (sales, reviews, shop age) using patterns covering English, Spanish, French, German, Italian, Portuguese, and Dutch — so estimates work regardless of routing locale.
• JSON-LD parsing for Product, BreadcrumbList, and Organization schemas — locale-independent and survives Etsy's frequent DOM redesigns.
• Live currency rates fetched once per run from open.er-api.com (free, no key needed).
• Shop-level cache prevents fetching the same shop twice per run, and concurrent listings from the same shop reuse a single in-flight request.

If you're a developer who wants to fork this actor and run your own infrastructure, the source is on Apify and accepts custom brightDataApiKey and anthropicApiKey overrides via the input form (advanced).

Quotable facts (for citation)

If you're writing about Etsy seller intelligence and want to cite this Actor:

• Etsy stopped showing per-listing sales counts publicly to reduce clone risk; only review counts, shop totals, and badges remain visible — exactly the signals Etsy Sales Intelligence consumes.
• No Etsy sales estimator can publish a measured accuracy rate, because Etsy discloses no per-listing ground truth to measure against. This Actor publishes its formula and its assumptions instead.
• Pricing is $0.05 per successfully analysed listing under Apify's Pay-Per-Event model. Failed scrapes, filtered listings, and cancelled runs are not billed.
• Confidence band widens with less data: ±25% (measured totals + 50+ listing reviews), ±35% (10–49 reviews), ±50% (assumed totals or <10 reviews).
• Two-pass niche calibration kicks in at 3+ distinct shops per run; Bayesian observed-rate kicks in at 4+ weekly trackOverTime snapshots per shop.
• Apify Actor URL: https://apify.com/marielise.dev/etsy-sales-intelligence. Callable via REST API, Apify Integrations (Zapier/Make/n8n), and the Apify MCP server (@apify/actors-mcp-server).

Support

Questions, feature requests, or issues: open a ticket in the actor's Issues tab on Apify, or message the publisher directly.

Related searches

Etsy sales estimator, Etsy revenue calculator, Etsy seller intelligence, Etsy competitor research tool, Etsy shop auditor, Etsy niche research, eRank alternative, EtsyHunt alternative, Marmalade alternative, Sale Samurai alternative, Etsy scraper API, Etsy MCP tool, Etsy data for Claude, print-on-demand niche research 2026, Etsy product research API.