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

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":

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):

["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:

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:

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:

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

Company score:

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

# 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.

finding-saas-company-leads-twitter

NPX Install

Tags

SKILL.md Content

Finding SaaS Company Leads on Twitter

Prerequisites

Inputs

Workflow

Step 1: Search Keywords

Step 2: Run tweet-scraper

Step 3: Enrich Profiles

Step 4: Filter and Classify

Step 5: Edge Cases

Output Format

Troubleshooting