Affiliate Program Commission Finder

Compare affiliate programs by commission model, payout rate, cookie window, network, cadence, approval rules, and partner fit.

Affiliate Program Commission Finder is built for one focused job: turning public source pages or pasted research snippets into usable affiliate program records. It does not try to be a generic webpage scraper. The parser, fields, examples, and dataset schema are shaped around this actor's specific business question.

Use it when you already have public pages, listings, announcements, reviews, directories, or copied snippets and want a repeatable way to extract the information that matters without manually filling a spreadsheet.

Who Uses It

Affiliate marketers, publishers, partner managers, media buyers, and agencies comparing monetization opportunities.

What It Finds

The actor looks for signals connected to compare affiliate programs by commission model, payout rate, cookie window, network, cadence, approval rules, and partner fit. It keeps the output focused on fields a buyer can review, filter, enrich, or send into a CRM, spreadsheet, product backlog, procurement workflow, or research database.

The extraction method is specific to this actor:

Affiliate-terms extraction with commission model detection, cookie-window parsing, payout/network recognition, and partner-fit classification.

How It Works

1. The actor accepts public URLs or pasted snippets in affiliateProgramSources.
2. Public URLs are fetched when possible and converted into visible text.
3. Pasted snippets are processed directly, which is useful for pages that block automated fetching or require manual browsing.
4. Actor-specific parsing logic extracts only the fields listed below.
5. Empty or low-information inputs are skipped safely, with clean logs instead of noisy failed rows.

Input

Use affiliateProgramSources for the sources you want to process. Each item can be a public URL or a focused text snippet.

{
    "affiliateProgramSources": [
        "TaskFlow affiliate program pays 30% recurring commission with a 60-day cookie, monthly PayPal payouts, and approval for SaaS content partners.",
        "FitGear ambassador program offers 12% commission, 30-day cookie, Impact network tracking, and requires active fitness audiences.",
        "SecureLayer partner program pays $250 bounty per qualified demo for cybersecurity publishers after manual approval."
    ],
    "maxResults": 3
}

Output Fields

• programName - Affiliate, ambassador, or partner program name.
• brandName - Brand offering the promotion or affiliate program.
• commissionModel - Commission model such as recurring, percentage, or bounty.
• commissionRate - Commission rate or payout value.
• cookieWindow - Affiliate cookie window.
• payoutCadence - Payment frequency or payout method.
• affiliateNetwork - Affiliate network or tracking platform.
• approvalRequirement - Partner approval, audience, or eligibility requirement.
• bestFitPartner - Publisher or partner type most likely to fit the program.
• programUrl - Affiliate program URL.

Example Output

{
    "programName": "TaskFlow affiliate program",
    "brandName": "FitGear",
    "commissionModel": "recurring",
    "commissionRate": "30% recurring commission",
    "cookieWindow": "60-day cookie",
    "payoutCadence": "monthly PayPal payouts",
    "affiliateNetwork": "PartnerStack",
    "approvalRequirement": "SaaS content partners",
    "bestFitPartner": "SaaS content partner",
    "programUrl": "https://example.com/affiliate"
}

Best Use Cases

• Build a focused spreadsheet from repeated source pages.
• Review public signals before sales, partnership, product, procurement, or market research work.
• Monitor a category over time using the same output fields.
• Turn copied notes from newsletters, communities, directories, review pages, or search results into a structured dataset.

Tips For Better Results

• Use source pages that visibly contain the signal you want this actor to extract.
• For hard-to-fetch sites, paste the relevant public text snippet directly into the input.
• Keep each input item focused on one page, listing, announcement, review, or profile.
• Review extracted rows before making high-stakes business, legal, procurement, or security decisions.

Limitations

• The actor does not bypass logins, paywalls, private systems, CAPTCHA, or anti-bot walls.
• The actor extracts from visible text and may miss information hidden behind scripts or user interactions.
• Some fields may be empty when the source does not contain that information.
• Output quality depends on source quality, so focused source text produces the strongest rows.