Apify Actor ROI Ranking: 20 Projects for Brian’s Store Experiment
Build narrow, boring, commercially legible Actors that turn public web pages into lead lists, audit packets, or recurring monitoring feeds. The best ROI is not the flashiest scraper; it is an Actor with obvious search demand on Apify, low login/ban exposure, and a buyer who can immediately use the output in sales, SEO, reputation, or competitive-intelligence workflows.
The most attractive cluster is public business data + contact/enrichment + audit output. Apify’s own Store currently advertises 48K+ Actors, creator payouts, AI-agent use cases, anti-blocking/proxy infrastructure, and Store monetization through pay-per-event / pay-per-usage. The visible Store homepage also shows very large usage counts for broad categories: Google Maps Scraper around 491K users/runs and 1,572 reviews, Instagram around 317K, TikTok around 210K, Google Search Results around 147K, Website Content Crawler around 164K, YouTube around 92K, and Contact Details Scraper around 53K in the marketing collection. This means demand is concentrated in leads, social, search, ecommerce, reviews, and AI/RAG crawling, but competition is brutal in generic versions.
Brian’s edge should be packaging and workflow specificity: not “a scraper,” but “give me the exact prospect/audit/review CSV an agency or founder already knows how to use tomorrow.”
1. Local Business Lead Enrichment Pack — Google Maps/query input → business + website + contact/social + missing-digital-presence flags. Highest blend of demand, buyer clarity, and synergy with Brian’s existing contact/social extractor.
2. Shopify Store Intelligence / Lead Builder — Shopify domain/category input → catalog, tech hints, contact, social, pricing, apps/marketing signals. Less platform-hostile than LinkedIn, high B2B/GTM value, and under-served relative to generic ecommerce scrapers.
3. SERP + PAA + AI Overview Content-Gap Pack — keyword list → organic results, PAA, related searches, AI Overview presence, content gap CSV. SEO agencies already buy SERP data, and a packaged research output is easier to monetize than raw SERP rows.
Scores are 1–10. Maintenance means low maintenance risk: higher is better. ROI is a weighted judgment, not a pure average, with extra weight for Brian-fit, build speed, distribution, and low legal/platform risk.
| Rank | Actor project | ROI | Demand | WTP | Build | Maintenance | Competition gap | Distribution | Short verdict |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Local Business Lead Enrichment Pack | 8.6 | 9 | 8 | 8 | 7 | 6 | 9 | Build first; improve existing extractor into a visible lead workflow. |
| 2 | Shopify Store Intelligence / Lead Builder | 8.2 | 8 | 8 | 8 | 8 | 7 | 8 | Strong GTM wedge; less hostile than social/login targets. |
| 3 | SERP + PAA + AI Overview Content-Gap Pack | 8.0 | 8 | 8 | 8 | 8 | 6 | 8 | Package raw Google Search results into agency-ready briefs. |
| 4 | Google Business Profile Audit Actor | 7.9 | 8 | 8 | 9 | 7 | 6 | 8 | Simple, high-value local SEO/reporting wedge. |
| 5 | Subreddit Pain Miner for Validation/Outreach | 7.7 | 8 | 7 | 8 | 8 | 6 | 8 | Brian-fit is high; sell as founder/agency research input. |
| 6 | Website Contact/Social Link Extractor v2 | 7.6 | 7 | 7 | 9 | 9 | 6 | 7 | Already built; make it public/listed and reposition as a component. |
| 7 | App Store + Google Play Review Export/Theme Miner | 7.5 | 7 | 7 | 8 | 8 | 7 | 6 | Good niche for product teams; moderate distribution friction. |
| 8 | Local Citation / NAP Consistency Scanner | 7.4 | 7 | 8 | 8 | 8 | 7 | 6 | Boring but agency-friendly; avoids fragile login targets. |
| 9 | Hiring Intent / Job Posting Signal Extractor | 7.3 | 8 | 7 | 8 | 7 | 6 | 8 | Sell to sales/recruiting/market-intel users; avoid LinkedIn-first MVP. |
| 10 | Review Miner for Trustpilot/G2/Amazon/App Stores | 7.2 | 8 | 8 | 7 | 6 | 7 | 7 | Strong demand, but target choice determines ban/maintenance burden. |
| 11 | GitHub Repo / Maintainer / Company Signal Extractor | 7.1 | 7 | 7 | 8 | 8 | 7 | 6 | Great for devtools leads; smaller but clean and durable. |
| 12 | Newsletter/RSS Author + Source Contact Extractor | 7.0 | 6 | 6 | 9 | 9 | 7 | 6 | Low risk, easy build, but demand is less proven. |
| 13 | Product Hunt Launch + Enrichment Monitor | 6.9 | 6 | 6 | 8 | 9 | 7 | 6 | Useful but smaller TAM; good fast-follow experiment. |
| 14 | Ecommerce Price/Catalog Monitor for Shopify/WooCommerce | 6.8 | 7 | 8 | 7 | 7 | 6 | 6 | Valid, but crowded and needs careful anti-blocking. |
| 15 | G2/Capterra/SaaS Review Competitor Miner | 6.7 | 7 | 8 | 6 | 6 | 7 | 6 | High WTP; maintenance and source fragility are the issue. |
| 16 | Restaurant/Menu/Local Competitor Snapshot | 6.5 | 6 | 7 | 7 | 6 | 7 | 6 | Good local-agency angle; source consistency may be messy. |
| 17 | LinkedIn Ads Library Competitive Creative Scraper | 6.3 | 7 | 7 | 6 | 6 | 6 | 6 | Useful but not first; platform changes can break it. |
| 18 | Amazon Review/Q&A/Listings Miner | 6.0 | 8 | 7 | 5 | 4 | 5 | 7 | Demand is real, but platform friction and competition are severe. |
| 19 | Airbnb/STR Comps + Review Tracker | 5.7 | 7 | 7 | 5 | 4 | 5 | 6 | Attractive buyers, but high brittleness and ToS/anti-bot risk. |
| 20 | Zillow/Realtor Agent/Listing Change Monitor | 5.2 | 7 | 7 | 4 | 3 | 5 | 6 | Avoid early; likely high maintenance/legal/platform arms race. |
Apify marketplace demand. Apify’s homepage/store positions the platform as a marketplace for real-time web data, competitor tracking, lead generation, social monitoring, and AI-agent data. The Store page advertises 48K+ Actors and visible popular Actors: Google Maps, Instagram, TikTok, Google Search Results, Website Content Crawler, LinkedIn search/profile tools, Reddit, YouTube, Contact Details Scraper, and ecommerce/review tools. That pattern strongly suggests demand exists in broad categories, but also that generic copies are hard to rank unless they improve packaging, price, quality, or a neglected workflow.
Monetization surface. Apify docs say Store Actors can be pay-per-event, pay-per-usage, or rental, with rental being sunset in 2026. Store publishing terms say creators receive 80% of pay-per-result/pay-per-event fees, potentially reduced by platform usage depending on configuration. This favors Actors with clean, countable outputs: per 1K leads, per audited domain, per review exported, per SERP/keyword pack, or per completed report.
Comparable pricing and WTP. Competitors validate budget: PhantomBuster plans are positioned around outbound/lead workflows; Outscraper sells Google Maps and review/contact extraction pay-as-you-go; DataForSEO sells SERP, Reviews, Business Data, Google Play/App Store, Trustpilot, Amazon, and Google Business APIs; BuiltWith lists lead generation/market analysis features and shows Basic/Pro plans around $295/$495 per month. These are not direct substitutes for every Actor, but they prove that buyers already pay for extracted web data when it plugs into sales, SEO, reputation, ecommerce, or competitive intelligence.
Risk screen. I penalized Actors that rely on logged-in social accounts, aggressive profile scraping, or high-defensibility platforms. LinkedIn profile scraping, Amazon, Airbnb, Zillow/Realtor, X/Twitter, and some social-media ideas have demand but can become maintenance traps. Brian’s first ROI experiments should avoid that unless the Actor has a narrow, low-volume, clearly lawful/public-data use case.
Thesis: Given a niche + location or Google Maps URLs, return a prospect CSV with business identity, website, phone, email/contact-page/social links, review count/rating, missing-digital-presence flags, and suggested outreach angle.
ICP / buyer: Local SEO agencies, web-design shops, niche lead-gen operators, appointment setters, home-service marketers, SMB growth consultants.
Data sources: Google Maps / Google Business public listings, business websites, contact pages, social profile links, optional SERP/domain checks.
Why demand likely exists: Google Maps Scraper is the largest visible Apify category signal; Apify Store snippets show Google Maps lead and contact Actors, Outscraper sells similar Maps/contact/review extraction, and Reddit/search snippets show users wiring Maps scrapers into n8n/outbound workflows. Generic Maps scraping is crowded, but enriched “ready to contact” output is the paid workflow.
Competition/substitutes: Apify Google Maps Scraper, Google Maps Business Leads Scraper, Contact Details Scraper, Outscraper, Scrap.io, manual VAs, Clay/Apollo enrichment. The gap is a bundled, agency-ready lead pack rather than raw places data.
MVP scope / build notes: Start with keyword/location or Google Maps results from existing Actors/API-compatible pages; crawl listed websites; extract email, phone, form URL, socials, schema.org, FB/IG/LinkedIn links; compute flags such as no website, no HTTPS, missing contact page, low review response, outdated copyright. Output CSV + JSON + summary counts.
Pricing angle: Pay per 1K enriched businesses, or tiered PPE: base place row + contact enrichment + audit flags. Premium sample datasets by vertical/city could help Store conversion.
Main risks: Google Maps dependency, email extraction quality, spam-adjacent positioning. Avoid “cold email spam tool” language; sell as public business data/audit enrichment.
Thesis: Convert Shopify storefront domains or category searches into a merchant intelligence row: products, price bands, tech hints, apps/scripts, contact/socials, email capture, shipping/return clues, ad pixels, and B2B pitch triggers.
ICP / buyer: Ecommerce agencies, Shopify app vendors, DTC consultants, email/SMS agencies, CRO agencies, B2B vendors selling to merchants.
Data sources: Shopify storefront public pages, /products.json where available, sitemap, collection pages, theme assets, app/script tags, contact/social pages.
Why demand likely exists: Apify has Shopify product/price scrapers and ecommerce categories; BuiltWith/Wappalyzer validate that technographic/ecommerce lead intelligence commands monthly subscription prices; Shopify merchants are a reachable buyer segment for agencies and app vendors.
Competition/substitutes: BuiltWith, Wappalyzer, Store Leads, Apify Shopify scrapers, manual scraping. Gap: narrow Shopify-specific output with contact + pitch trigger + product/category context, not just tech fingerprinting.
MVP scope / build notes: Input domains or SERP-discovered Shopify sites; detect Shopify; pull public product catalog if exposed; count SKUs; extract categories, pricing range, sold-out rate, social/contact, popups/newsletter tools when visible. Avoid login or cart actions.
Pricing angle: Per domain audited or per 1K domains enriched. Agencies will pay for CSVs that feed outbound and vertical campaign selection.
Main risks: Shopify endpoint access varies; large catalogs can be compute-heavy; app detection may be noisy.
Thesis: Given keywords and a target domain, return organic results, People Also Ask, related searches, AI Overview/AI Mode presence when available, competitor URL clusters, and an “article/landing page gaps” CSV.
ICP / buyer: SEO agencies, affiliate/content teams, local SEO shops, AI-search optimization consultants, small publishers.
Data sources: Google Search public SERPs via Apify Google Search Results Scraper / own SERP Actor, PAA, related searches, snippets, ads, result URLs.
Why demand likely exists: Apify’s Google Search Results Scraper is visibly popular; DataForSEO sells SERP and keyword APIs; Apify Store snippets mention SERP, AI Mode, AI overviews, ads, PAA, prices, and reviews. The buyer wants packaged research, not raw SERP JSON.
Competition/substitutes: Apify Google Search Results Scraper, DataForSEO, SerpApi, SEMrush/Ahrefs, manual SEO analysts. Gap: cheaper, single-purpose content-gap packs for small agencies and AI workflows.
MVP scope / build notes: Input keywords + target domain; run SERP extraction; group competitors; pull titles/snippets/PAA; output topic clusters, missing pages, pages to update, and evidence links. No need to crawl protected sites.
Pricing angle: Pay per keyword/SERP pack, e.g. per 100 keywords. Make outputs LLM-ready and agency report-ready.
Main risks: SERP volatility, Google anti-bot cost, differentiation versus existing SERP API.
Thesis: Given a business name/location, GBP URL, or Maps result, generate a local SEO audit: categories, rating/review count, photos, hours, phone/site, review recency, owner responses, Q&A, competitor comparison, and missing-field checklist.
ICP / buyer: Local SEO agencies, franchise marketers, web designers selling GBP cleanup, reputation agencies.
Data sources: Google Maps/Business public listing data, reviews, photos count/metadata where available, competitor results for same category/location.
Why demand likely exists: Google Maps data dominates Apify demand, and local SEO agencies repeatedly use GBP audits as lead magnets. The Actor can convert a known high-demand scraper into a higher-value report.
Competition/substitutes: Google Maps Scraper, Local Falcon/BrightLocal/Whitespark, manual agency audits. Gap: cheap API-first audit packet inside Apify.
MVP scope / build notes: Start with one business or top 10 competitors; produce CSV + Markdown/HTML audit with missing fields and benchmarks. Use existing Maps/reviews extraction rather than rebuilding everything.
Pricing angle: Per audit or per 100 businesses audited; upsell to batch prospecting.
Main risks: Data availability changes, report quality must be credible enough for agencies to resell.
Thesis: Search Reddit posts/comments by subreddit/query/date, extract repeated pain clauses, buyer vocabulary, competitor mentions, urgency, and reply/comment drafts for validation.
ICP / buyer: Indie founders, agencies doing niche research, Lurkbot-style opportunity researchers, product marketers, content teams.
Data sources: Reddit public posts/comments/search pages, subreddit feeds, old Reddit/JSON where available, optional Google indexed Reddit results.
Why demand likely exists: Apify Store shows Reddit Scraper Lite around 31K and high ratings in marketing/social collections; Brian already uses Reddit pain discovery in Lurkbot; founders constantly need “find complaints, not ideas.”
Competition/substitutes: Reddit Scraper Lite, generic Reddit scrapers, GummySearch, F5Bot, manual search, Lurkbot internal tools. Gap: output is not raw posts; it is pain taxonomy + prospect/comment queue.
MVP scope / build notes: Input subreddits/queries/date window; fetch posts/comments; dedupe; score by pain intensity, buyer fit, recency; emit rows with pain quote, source URL, suggested validation question, and soft reply draft.
Pricing angle: Per 1K posts/comments analyzed or per completed research pack. Could also feed Brian’s own workflows.
Main risks: Reddit access/403/rate limits; social scraping sensitivity; must avoid spammy outreach framing.
Thesis: Turn Brian’s existing private Actor into a listed, polished enrichment primitive: bulk URLs → emails, phones, contact forms, social profiles, schema, team/about links, and confidence scores.
ICP / buyer: Lead-gen operators, agencies, researchers, local-business prospectors, Apify users chaining outputs from Maps/SERP/Shopify Actors.
Data sources: Public websites, contact/about/team/legal pages, schema.org, meta tags, footer/header links, sitemap/robots where helpful.
Why demand likely exists: Apify’s marketing collection shows Contact Details Scraper around 53K and many lead-generation Actors depend on contact enrichment. The existing Actor already sits in a high-demand category, but private/unlisted status blocks ROI.
Competition/substitutes: Contact Details Scraper, Hunter/Clearbit/Apollo/Dropcontact, email verifier Actors, manual VA scraping. Gap: transparent, cheap, bulk URL enrichment with no proprietary database promise.
MVP scope / build notes: Make the Actor public/listed; add strong README examples; support CSV input; crawl shallow pages only; include source URL for every extracted field; expose maxPagesPerDomain, includeSocials, includeForms, dedupe, confidence.
Pricing angle: Pay per domain processed or per successfully enriched domain. Bundle with a free sample run.
Main risks: Generic competition, email quality disputes, spam adjacency. Position as “public contact/social discovery for research and CRM hygiene.”
Thesis: Given app IDs, export reviews by country/version/date and summarize top complaints, feature requests, sentiment shifts, and competitor comparisons.
ICP / buyer: Indie app founders, mobile PMs, app agencies, ASO consultants, game studios.
Data sources: Apple App Store public pages/RSS/API-like endpoints, Google Play public review pages where accessible, app metadata.
Why demand likely exists: DataForSEO sells App Store and Google Play review/data APIs; AppFollow/Appfigures/Appbot are paid categories; search results show recurring demand for review exports and AI review analysis. Apify has app/review adjacent categories but there is room for a narrow “export + themes” Actor.
Competition/substitutes: AppFollow, Appfigures, Appbot, DataForSEO App APIs, ad hoc Python libraries, Apify review Actors. Gap: quick low-cost export with LLM-ready theme summaries.
MVP scope / build notes: Start with Apple App Store reviews because public endpoints are more stable; then Google Play. Output raw reviews + themes + changelog/version grouping.
Pricing angle: Per app-country-version review batch or per 1K reviews.
Main risks: Google Play review access may be brittle; historical depth differs by country/source.
Thesis: Given a business name/phone/domain, scan public directories/search results for name-address-phone consistency, duplicate listings, wrong URLs, and missing profiles.
ICP / buyer: Local SEO agencies, SMB web shops, reputation/citation cleanup vendors.
Data sources: Google SERP, Maps, Yelp, Yellow Pages, BBB, chamber/directories, business website schema.
Why demand likely exists: Local SEO tools sell citation audits; Apify has Maps, Yelp, SERP, directory, and contact scrapers. This avoids building a large database by running a live audit for one business/prospect.
Competition/substitutes: BrightLocal, Whitespark, Yext, Semrush local, manual audits. Gap: API-first, cheap per-audit output for agencies and automation flows.
MVP scope / build notes: Input canonical NAP; query top directory patterns; normalize phone/address; flag mismatches; include source URL/evidence snippet.
Pricing angle: Per business audit or per 100 citations checked.
Main risks: Directory parsing variability, address normalization edge cases.
Thesis: Extract job postings for companies/queries and convert them into buyer-intent signals: department hiring, tools mentioned, pain keywords, remote/location, and lead routing.
ICP / buyer: B2B sales teams, recruiting agencies, market-intel analysts, vertical SaaS vendors.
Data sources: Indeed, Google Jobs SERP, company career pages, RemoteOK/Wellfound/Greenhouse/Lever public boards, optional LinkedIn Jobs via safer/public paths.
Why demand likely exists: Apify has a jobs category and Indeed/LinkedIn/Google Jobs scrapers; job posts are common public intent signals for sales and market research.
Competition/substitutes: Apify Indeed/LinkedIn Jobs Actors, Clay, BuiltWith-like sales intelligence, manual job-board searches. Gap: transform raw jobs into “which companies are likely buying X” lists.
MVP scope / build notes: Start with company career pages and Google Jobs/Indeed; extract title, company, location, description, tool keywords; score intent by keywords.
Pricing angle: Per 1K jobs extracted/analyzed or per monitored company list.
Main risks: Indeed/LinkedIn fragility; company career pages vary.
Thesis: Extract customer reviews from one selected source and output complaint themes, competitor comparisons, and quotable pains for product/marketing teams.
ICP / buyer: SaaS PMs, ecommerce brands, Amazon sellers, reputation agencies, product marketers.
Data sources: Start with Trustpilot or app stores; later add G2/Amazon as separate Actors or modules.
Why demand likely exists: DataForSEO explicitly sells Reviews APIs across Google, Trustpilot, Tripadvisor, Google Play, App Store, and Amazon; Apify marketing collection includes Amazon Reviews and Trustpilot scrapers, but some have weak ratings, suggesting room for reliability/UX improvements.
Competition/substitutes: DataForSEO Reviews APIs, Apify Trustpilot/Amazon/G2 Actors, AppFollow, ScraperAPI/Oxylabs tutorials. Gap: source-specific high-quality extraction plus LLM-ready pain taxonomy.
MVP scope / build notes: Do not build “all reviews everywhere” first. Pick Trustpilot or app stores; support URL input, date/rating filters, pagination, source links, themes.
Pricing angle: Per 1K reviews or per report pack.
Main risks: Review sites change often; G2 and Amazon are more defensive than app stores/Trustpilot.
Thesis: Given GitHub orgs/repos/topics, extract repo metadata, stars/forks/issues velocity, maintainer/company links, package/site URLs, and commercialization/lead clues.
ICP / buyer: Devtool founders, open-source intelligence teams, recruiting/sales teams, investors, agent builders.
Data sources: GitHub public web/API, repo READMEs, package manifests, websites linked from repos.
Why demand likely exists: GitHub data is high-signal for devtool GTM and lower risk than login-gated social scraping. Many teams want “who is using/maintaining X” lists.
Competition/substitutes: GitHub API, OSS Insight, libraries.io, Sourcegraph, manual GitHub search, Clay enrichments. Gap: Apify-ready CSV combining repo signal + contacts/domains.
MVP scope / build notes: Use public API where possible; enrich linked domains with existing contact extractor; output repo stats, topics, license, last commit, issue labels, website, social links.
Pricing angle: Per repo/org processed; premium for contact enrichment.
Main risks: GitHub API rate limits; mapping repos to commercial buyers can be fuzzy.
Thesis: Given RSS feeds, Substack/newsletter/blog URLs, or search queries, extract recent posts, author names, emails/socials, topics, sponsors/ads, and outbound links.
ICP / buyer: PR agencies, link builders, podcast/newsletter advertisers, content marketers, indie founders doing outreach.
Data sources: RSS/Atom feeds, public blogs, Substack public pages, Beehiiv/Ghost blogs, author/about pages.
Why demand likely exists: Content/outreach buyers need source/contact lists, and this is much less brittle than big social platforms. Demand is weaker than Maps/SEO, but buildability is excellent.
Competition/substitutes: SparkToro, BuzzStream, Hunter, manual research, generic crawlers. Gap: source-specific, fresh, exportable author/outreach rows.
MVP scope / build notes: Input feed/domain/query; parse feed and recent posts; crawl about/contact; extract author/contact/social; classify topics.
Pricing angle: Per feed/domain; batch lists for PR/outreach.
Main risks: Smaller Apify search demand; Substack anti-scraping may vary.
Thesis: Monitor Product Hunt launches by category/date and enrich makers, websites, pricing pages, tech/contact/social links, and launch performance.
ICP / buyer: Agencies, startup scouts, VC/angel analysts, B2B vendors selling to new startups, founders tracking competitors.
Data sources: Product Hunt public pages, maker/product websites, social links, GitHub links where present.
Why demand likely exists: Product Hunt is an obvious startup-intent source and easy to explain, but Apify search signals were weaker than Maps/SEO/ecommerce. Treat as a fast-follow, not core revenue bet.
Competition/substitutes: Product Hunt APIs/tools, manual browsing, startup databases. Gap: fresh daily enriched CSV.
MVP scope / build notes: Daily top launches + keyword/category search; enrich website/contact/social/pricing; output company row and maker row.
Pricing angle: Per daily feed or monthly subscription-like task, though Apify rental sunset favors PPE/run pricing.
Main risks: Smaller demand; official API availability may reduce need.
Thesis: Given competitor storefront URLs, schedule recurring catalog/price inventory snapshots and alert on new products, price changes, sold-out/restock, and markdowns.
ICP / buyer: DTC brands, ecommerce agencies, Amazon/Shopify sellers, pricing analysts.
Data sources: Shopify/WooCommerce public catalog pages, sitemaps, product JSON where exposed, structured data.
Why demand likely exists: Apify’s ecommerce category and price-scraper pages show Amazon/Shopify/Walmart price use cases; price intelligence is a known paid workflow.
Competition/substitutes: Price2Spy, Prisync, Minderest, Apify ecommerce/price Actors, Octoparse. Gap: lightweight store-specific monitors for long-tail Shopify competitors.
MVP scope / build notes: Start Shopify-only; snapshot product handle/title/variant/price/availability; compare to previous dataset; output delta rows.
Pricing angle: Per 1K products scanned or per store snapshot.
Main risks: Crowded, changing frontend themes, need recurring-task UX.
Thesis: Extract reviews for SaaS products/categories and produce buyer objections, feature requests, competitor comparisons, and quotable customer language.
ICP / buyer: SaaS product marketers, founders, PMs, competitive-intel agencies.
Data sources: G2, Capterra/GetApp/Software Advice public pages where accessible; company websites.
Why demand likely exists: Apify search results show G2 Product Reviews Scraper; DataForSEO and review-intel tools validate WTP for review data. B2B SaaS buyers pay for competitive insights.
Competition/substitutes: G2/Capterra native access, Klue/Crayon, DataForSEO Reviews APIs, existing Apify G2 scrapers. Gap: low-cost thematic extraction and competitor battlecard output.
MVP scope / build notes: Start with one source; scrape product URL reviews; support rating/date filters; summarize themes; include source URL and review metadata.
Pricing angle: Per 1K reviews or per competitor pack.
Main risks: Site defenses and terms; extraction can break.
Thesis: For restaurants in a city/category, return menu URLs/items/prices where visible, cuisine tags, rating/review counts, reservation/delivery links, and competitor positioning.
ICP / buyer: Restaurant marketers, local agencies, franchise operators, delivery consultants.
Data sources: Google Maps, restaurant websites, OpenTable, Yelp snippets/reviews, menu pages, schema.org menu data.
Why demand likely exists: Apify travel/marketing collections show Yelp/OpenTable/restaurant-related Actors; restaurants are a large local vertical with visible review/menu data.
Competition/substitutes: Manual audits, menu aggregators, Yelp/OpenTable scrapers, agency research. Gap: local competitive snapshot tied to outreach/audit.
MVP scope / build notes: Start with Google Maps + website menu extraction; avoid scraping delivery platforms deeply. Output menu link, top dishes/prices if simple, and rating/review context.
Pricing angle: Per market/category snapshot.
Main risks: Menus are messy PDFs/images; prices change; restaurant WTP for data alone may be low.
Thesis: Given company names/domains, extract public LinkedIn Ad Library creative/copy/CTA metadata and summarize positioning changes.
ICP / buyer: B2B marketers, competitive-intel teams, agencies running paid social.
Data sources: LinkedIn Ad Library public pages and advertiser pages, company domains.
Why demand likely exists: Apify marketing collection includes LinkedIn Ads Scraper; ad intelligence tools have established budgets. But platform changes and LinkedIn sensitivity keep this outside the top tier.
Competition/substitutes: Apify LinkedIn Ads Scraper, Semrush/Adbeat/Foreplay-like libraries, manual ad library review.
MVP scope / build notes: URL/company input; extract active ads metadata, copy, CTA, landing page, images/video URLs where public; summarize themes.
Pricing angle: Per advertiser or per ad extracted.
Main risks: LinkedIn anti-bot changes, legal/ToS sensitivity, media extraction fragility.
Thesis: Given ASINs/keywords, extract listing details, reviews, questions/answers, rating distribution, and customer complaint themes for product research.
ICP / buyer: Amazon sellers, ecommerce agencies, product researchers, DTC brands.
Data sources: Amazon public product/review pages, search/listing pages, seller pages where available.
Why demand likely exists: Apify Store shows Amazon price/product/review scrapers; ScraperAPI/Oxylabs/SerpApi publish Amazon scraping products/tutorials; ecommerce teams clearly pay for this data.
Competition/substitutes: JungleScout/Helium10/Keepa, ScraperAPI/Oxylabs/SerpApi, existing Apify Amazon Actors. Gap only exists if Brian picks a narrow underserved output, e.g. “review pain themes by ASIN list.”
MVP scope / build notes: If built, do one narrow function: review/Q&A complaint miner from supplied ASIN URLs, not full Amazon search. Use retries/proxies conservatively.
Pricing angle: Per 1K reviews/Q&A rows or per ASIN report.
Main risks: High anti-bot pressure, existing competition, Amazon ToS sensitivity, expensive maintenance.
Thesis: Given a market/search URL, extract public short-term rental listings, prices/fees/ratings/reviews where visible, and competitor deltas over time.
ICP / buyer: STR operators, property managers, market analysts, real estate investors.
Data sources: Airbnb public listings/search pages, review pages, host/listing details.
Why demand likely exists: Apify Store has Airbnb price/location/review scrapers; STR analytics is a paid category. But high platform brittleness and stronger incumbents lower ROI.
Competition/substitutes: AirDNA, PriceLabs, Apify Airbnb Actors, manual comp checks.
MVP scope / build notes: If built, target supplied listing URLs and public review extraction first; avoid broad search-scale scraping.
Pricing angle: Per listing/market snapshot.
Main risks: Login walls, anti-bot, legal/ToS risk, data completeness.
Thesis: Monitor property listings/agent pages for changes, contact data, price drops, and market activity.
ICP / buyer: Realtors, investors, wholesalers, real estate data shops.
Data sources: Zillow/Realtor public listing pages, agent pages, search pages.
Why demand likely exists: Apify has a real-estate category and Zillow/Realtor Actors; real estate data has clear WTP. But this is one of the worst ROI fits for Brian’s quick Actor experiment because it invites a platform arms race.
Competition/substitutes: Zillow/Realtor APIs/feeds/paid data, Apify Zillow/Realtor scrapers, MLS tools, PropStream, BatchLeads.
MVP scope / build notes: Avoid as an early project. If forced, narrow to user-supplied URLs and low-frequency monitoring.
Pricing angle: Per listing/agent row or monitoring run.
Main risks: Very high anti-bot/ToS/legal risk, incomplete data, strong incumbents, support burden.
1. Website Contact/Social Link Extractor v2 — publish/list the existing Actor immediately; it becomes a reusable enrichment primitive for several higher-ROI Actors.
2. App Store Review Export/Theme Miner — low-ish platform risk and useful to product teams.
3. Subreddit Pain Miner — high Brian-fit and reusable for Lurkbot/opportunity validation.
4. Google Business Profile Audit Actor — easy to sell as agency lead magnet/report output.
5. Hiring Intent Extractor — strong B2B signal if started from Google Jobs/company career pages instead of LinkedIn-heavy scraping.
The existing private/unlisted Actor, website-contact-social-link-extractor (Qq9zAnXeQhro75u4W), ranks around #6 as a standalone project but #1 as enabling infrastructure. It should not be left as “just another contact scraper.” The ROI move is to make it the enrichment layer behind Maps, Shopify, SERP, GitHub, newsletter, and Product Hunt Actors.
Recommended listing improvements:
Week 1: Publish/list Contact/Social Extractor v2; add README, schema, examples, pricing, and two sample datasets.
Week 2: Build Local Business Lead Enrichment Pack using Maps results + Contact/Social v2. This should be the first serious paid Actor because it rides the strongest marketplace demand.
Week 3: Build Shopify Store Intelligence Pack. Reuse Contact/Social v2 and add product/catalog/tech signals.
Week 4: Build SERP/PAA Content-Gap Pack. This gives Brian an SEO/AI-search lane that does not depend on social-login scraping.
Validation before coding each Actor: Search Apify Store for the exact target phrase, read the top 10 competing Actor READMEs/reviews, run three sample buyer workflows manually, then publish a narrow MVP with a clear result schema and example dataset.