Newsletter Sponsorship Rate Extractor

Extract newsletter audience, placement, rate, niche, and booking signals for sponsorship research.

This actor is built for buyers who do not want a noisy scrape. It turns simple public text, snippets, or URLs into a compact dataset with only the fields that matter for the workflow. It is intentionally limited to clean, useful rows instead of dumping page text, actor names, internal scores, or duplicate metadata.

What it is best for

• Fast marketplace-ready research runs
• Lead qualification and enrichment workflows
• Competitive intelligence and monitoring
• CSV exports for sales, marketing, product, and operations teams
• AI-agent workflows that need predictable fields

Input

The input is intentionally simple:

• items - paste snippets, page text, job posts, review text, announcements, or public URLs.
• maxResults - choose how many rows to save, from 1 to 20.

You do not need selectors, CSS rules, browser settings, login cookies, or complicated configuration.

Output

Each saved dataset item contains only these useful fields:

• publicationName - PublicationName extracted or inferred from the supplied text.
• audienceSize - AudienceSize extracted or inferred from the supplied text.
• niche - Niche extracted or inferred from the supplied text.
• adPlacement - AdPlacement extracted or inferred from the supplied text.
• publishedRate - PublishedRate extracted or inferred from the supplied text.
• bookingSignal - BookingSignal extracted or inferred from the supplied text.
• contactUrl - ContactUrl extracted or inferred from the supplied text.

Example input

{
    "items": [
        "The SaaS Brief reaches 42,000 founders and operators. Primary sponsor placement is $1,200 per issue with booking via sponsorship form.",
        "Local Eats Weekly offers featured restaurant sponsorship for $350. Audience: 18,500 food lovers in Chicago.",
        "AI Builder Digest has 75k subscribers, ad slots include top banner and dedicated send. Contact sponsors@aibuilder.example."
    ],
    "maxResults": 3
}

Example output

{
    "publicationName": "The SaaS Brief",
    "audienceSize": "42,000",
    "niche": "SaaS founders and operators",
    "adPlacement": "primary sponsor placement",
    "publishedRate": "$1,200 per issue",
    "bookingSignal": "booking form mentioned",
    "contactUrl": "manual input"
}

Why this actor is useful

Many scrapers return too much raw data. This actor is shaped for a buyer who wants a ready-to-use spreadsheet: one row per useful signal, clean columns, and no unnecessary filler fields. That makes it easier to plug into Google Sheets, Airtable, CRMs, lead scoring tools, research reports, and AI workflows.

Recommended pricing

Best model: Pay per event / per dataset item.

Recommended event: apify-default-dataset-item

Suggested price: $0.0040 per newsletter sponsor row.

This is predictable for users because every paid event maps to one visible dataset row.

Limitations

• The actor works best with public pages or pasted text.
• It does not bypass logins, paywalls, CAPTCHA, or private data restrictions.
• Fields are extracted from visible text and may need human review for high-stakes decisions.
• For very large monitoring jobs, split inputs into batches to keep results easy to inspect.

Categories

MARKETING, LEAD_GENERATION

Search keywords

newsletter sponsorship, media buying, ad rates, creator economy