datagrit › Data › Substack Newsletter Sponsorship Prospect Finder

Substack Newsletter Sponsorship Prospect Finder

Substack publications ranked for sponsorship: subscriber count, paid-tier size, plan prices, posting cadence and engagement per 1,000 subscribers.

Substack Newsletter Sponsorship Prospect Finder turns Substack category rankings, a list of newsletters you name, or the recommendation links of a newsletter into one analyzed row per publication. Each row combines the public profile (subscriber count, paid-subscriber label, monthly, annual and founding prices) with numbers measured from the publication's own post archive: posts in the last 30 and 90 days, posts per week, the share of paid-only posts, median reactions and comments per post, reactions per 1,000 subscribers, and how many newsletters started recommending it in the last 30 days.

Export the result as JSON, CSV or Excel, call it through the Apify API, or plug it into n8n, Make and AI agents through MCP.

Why use Substack Newsletter Sponsorship Prospect Finder?

• Build a sponsorship shortlist. Rank newsletters in a niche by audience size, engagement per 1,000 subscribers and posting consistency instead of guessing from subscriber counts alone.
• Find growing newsletters early. The recommendation count of the last 30 days shows which publications are gaining readers through other writers, before they reach the top of a ranking.
• Qualify outreach lists. Filter by subscriber range, paid plan, monthly price, language, days since the last post and posts per month, so you contact only active publications that fit your budget.
• Expand from newsletters you already like. Give one or more publications and get the newsletters they recommend, each with the same metrics.
• Track the market over time. Schedule the same input weekly and compare ranks, subscriber counts and cadence between runs.

Output fields

Each record contains the profile fields (publicationId, name, url, language, authorName, subscribers, paidSubscribersLabel, paidSubscribersAtLeast, paidSubscribersSource, bestsellerTier, paymentsState, plan prices), the origin of the row (foundVia, category, rank, lookalikeOf), the measured activity fields (lastPostAt, daysSinceLastPost, posts30d, posts90d, postsPerWeek, paidPostShare90d, medianReactionsPerPost, medianCommentsPerPost, reactionsPer1kSubscribers), the recommendation fields (recommendedBy30d, recommendedBy30dCapped, recommendedByLatestAt; recommendedBy30dCapped is true when the 30-day window may extend beyond the recommendations Substack returned, so read the count as a lower bound), dataGaps, sourceUrl and scrapedAt.

Substack exposes the paid-subscriber count only as a band such as "Thousands of paid subscribers", so paidSubscribersAtLeast is a lower bound. The band comes from the publication's own record: its label when Substack shows one (paidSubscribersSource = ranking-label), or the numeric order of magnitude in the same record when the label is not shown (ranking-order-of-magnitude). Only when the publication has no band of its own does the author's bestseller badge fill in (author-bestseller-tier); the badge is set per author and can sit one step above or below the publication, so it never overrides the publication's band and is always returned separately as bestsellerTier. Check paidSubscribersSource before relying on a figure. The Actor does not report who sponsors a newsletter.

Publications that hide their subscriber count have subscribers empty. When the recommendations of one publication cannot be read, its recommendation fields stay empty and dataGaps names what is missing; a publication whose post archive cannot be read is skipped without billing. If the archive or the recommendations cannot be read for most publications, the run fails instead of returning empty columns.

Rows with a status instead of data (found: false) are never billed: a requested address that does not exist or is not a Substack publication, an address that could not be reached or checked in this run (the note says it is unknown whether it is a Substack publication), a publication confirmed to exist whose profile could not be read, and a publication whose post archive could not be read, because the activity metrics are what a row is billed for. The note of each row and the run summary give the reason, and a run that returns nothing because the source could not be read says so instead of blaming your filters.

Is it legal to scrape this data?

The Actor reads only publicly available information and does not log in or bypass access controls. You are responsible for using the data in line with applicable laws

(including data protection rules) and the source's terms. If you find an issue, open it in the Issues tab; problems are answered within one business day.

Output fields

Every result is one flat record, so it drops straight into a spreadsheet, a database or a CRM.

Sample record

{
  "found": true,
  "input": "no-such-newsletter.substack.com",
  "note": "No Substack publication was found at no-such-newsletter.substack.com.",
  "publicationId": 6349492,
  "name": "SemiAnalysis",
  "subdomain": "semianalysis",
  "customDomain": "newsletter.semianalysis.com",
  "url": "https://newsletter.semianalysis.com",
  "description": "Bridging the gap between the world's most important industry, semiconductors, and business.",
  "language": "en",
  "authorName": "Dylan Patel",
  "authorHandle": "semianalysis",
  "authorBio": "Bridging the gap between business and the worlds most important industry.",
  "authorUrl": "https://substack.com/@semianalysis",
  "subscribers": 318000,
  "paidSubscribersLabel": "Thousands of paid subscribers",
  "paidSubscribersAtLeast": 1000,
  "paidSubscribersSource": "ranking-label",
  "bestsellerTier": 1000,
  "paymentsState": "enabled",
  "monthlyPriceUsd": 50,
  "annualPriceUsd": 500,
  "foundingPriceUsd": 1000,
  "annualDiscountPercent": 17,
  "firstPostAt": "2020-05-22T21:26:00.000Z",
  "hasPodcast": false,
  "hasCommunity": true,
  "foundVia": "leaderboard",
  "category": "technology",
  "rank": 1,
  "lookalikeOf": "Lenny's Newsletter",
  "lastPostAt": "2026-09-29T14:02:11.000Z",
  "daysSinceLastPost": 2,
  "posts30d": 9,
  "posts90d": 31,
  "postsPerWeek": 2.4,
  "paidPostShare90d": 0.35,
  "medianReactionsPerPost": 412,
  "medianCommentsPerPost": 38,
  "reactionsPer1kSubscribers": 1.3,
  "recommendedBy30d": 4,
  "recommendedBy30dCapped": false,
  "recommendedByLatestAt": "2026-09-27T08:15:00.000Z",
  "dataGaps": [],
  "sourceUrl": "https://substack.com/api/v1/category/public/4/all?page=0",
  "scrapedAt": "2026-10-01T09:30:00.000Z"
}

Input

Call it from your code

Run the Actor and get the results in one request. Replace YOUR_APIFY_TOKEN with the token from your Apify account settings.

curl -X POST "https://api.apify.com/v2/acts/datagrit~substack-sponsor-prospector/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"source":"leaderboard","categories":["technology","business"],"maxPerCategory":10,"maxItems":20}'

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('datagrit/substack-sponsor-prospector').call({
  "source": "leaderboard",
  "categories": [
    "technology",
    "business"
  ],
  "maxPerCategory": 10,
  "maxItems": 20
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.length, items[0]);

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("datagrit/substack-sponsor-prospector").call(run_input={
  "source": "leaderboard",
  "categories": [
    "technology",
    "business"
  ],
  "maxPerCategory": 10,
  "maxItems": 20
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(len(items), items[0] if items else None)

Frequently asked questions

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