finding-saas-company-leads-twitter

Finds SaaS companies and software startup leads from Twitter/X using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find SaaS companies on Twitter for partnership or sales outreach, build a list of software startups by vertical, find B2B SaaS founders or decision-makers on X, identify early-stage software companies by their Twitter activity, discover SaaS products in a specific niche from Twitter, find software vendors posting about product launches or fundraising, or compile a SaaS company contact list from social media. Returns company handle, founder name (from bio), follower count, product description, and recent tweets. Ideal for SaaS integration partners, investor outreach, B2B sales teams, and startup press.

Finding SaaS Company Leads on Twitter

Discovers SaaS companies and software product accounts via Twitter signals — product launches, feature announcements, founder activity, and niche-specific hashtags. Twitter is where early-stage B2B SaaS companies are most active before establishing a formal web presence.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
startUrls	array	Optional	[]	Twitter profile or tweet URLs
twitterHandles	array	Optional	[]	Twitter usernames (without @)
twitterUserIds	array	Optional	[]	Twitter user IDs
getFollowers	boolean	Optional	false	Extract follower lists
getFollowing	boolean	Optional	false	Extract following lists
getRetweeters	boolean	Optional	false	Extract retweeters of a tweet URL
includeUnavailableUsers	boolean	Optional	false	Include unavailable/suspended users
maxItems	number	Optional	Unlimited	Maximum users to return
customMapFunction	string	Optional	—	JavaScript function to transform each output object

Workflow

text

Progress:
- [ ] Step 1: Build keyword and hashtag search list for vertical
- [ ] Step 2: Run tweet-scraper to find active companies
- [ ] Step 3: Extract unique company handles
- [ ] Step 4: Enrich via twitter-user-scraper
- [ ] Step 5: Score and classify by stage
- [ ] Step 6: Deliver lead list

Step 1: Search Keywords

Build from vertical. Example for "project management SaaS":

text

Keywords: ["project management software", "PM tool", "#pmtools", "task management SaaS",
           "launched a product", "we built", "try our tool", "project management app"]

Standard SaaS signal phrases (always include):

text

["just launched", "we built", "our product", "sign up free", "#buildinpublic",
 "new feature", "we're hiring", "Series A", "product update", "[vertical] tool"]

Step 2: Run tweet-scraper

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

bash

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

text

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["[VERTICAL] software", "[VERTICAL] SaaS", "[VERTICAL] tool launch", "we built [VERTICAL]"],
  "maxItems": 300,
  "tweetLanguage": "en"
}

REST API fallback:

bash

curl -X POST   "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{
    "searchTerms": ["HR tech SaaS", "HR software launch", "we built HR tool"],
    "maxItems": 300
  }'

Collect unique author.username values.

Step 3: Enrich Profiles

If Apify MCP is available:

text

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "usernames": ["[username1]", "[username2]", "...up to 100 usernames"]
}

REST API fallback:

bash

curl -X POST   "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{"usernames": ["handle1", "handle2"]}'

Step 4: Filter and Classify

Is a SaaS company? Keep if bio contains:

• Product keywords: "software", "SaaS", "platform", "app", "tool", "API", "dashboard"
• Launch signals: "try", "sign up", "free trial", "beta"
• Funding signals: "backed by", "YC", "Techstars", "seed", "Series A/B"

Stage classification from followerCount:

text

early: followerCount < 1,000
growing: 1,000–10,000
established: > 10,000

Company score:

text

lead_score = (is_saas_signal ? 1 : 0) * 0.40
           + (has_website ? 1 : 0) * 0.25
           + (tweeted_in_last_30_days ? 1 : 0) * 0.20
           + min(followerCount / 5000, 1) * 0.15

Step 5: Edge Cases

• Personal accounts return instead of company: Filter — prefer accounts where name != username and bio describes a product; deprioritize accounts with personal pronouns in bio ("I build...")
• < 20 companies found: Widen vertical keywords; try hashtags #buildinpublic, #indiehacker, #saas directly
• Duplicate company (founder + company account both found): Keep company account; link to founder handle as contact

Output Format

text

# SaaS Company Leads: [VERTICAL]
Companies found: [N] | Early: [N] | Growing: [N] | Established: [N] | Date: [DATE]

## Growing-Stage Companies (Best Outreach Window)
| Company | Handle | Product | Stage | Followers | Website | Score |
|---------|--------|---------|-------|-----------|---------|-------|
| [name] | @[handle] | [1-line description from bio] | Growing | [N] | [url] | [0.XX] |

## Early-Stage (High Receptivity)
| Company | Handle | Product | Followers | Last Active |
|---------|--------|---------|-----------|------------|

## Established (Formal Sales Cycle)
| Company | Handle | Product | Followers | Website |
|---------|--------|---------|-----------|---------|

Troubleshooting

Results are mostly personal accounts: Add "software" OR "app" OR "platform" to search and filter aggressively by bio keywords. Vertical too broad (returns 500+ companies): Narrow to a sub-vertical (e.g., "project management" → "async project management for remote teams"). Companies inactive (last tweet > 60 days): Flag as potentially dormant; cross-reference product website for active status.