GTM Signals Aggregator

📡 What can GTM Signals Aggregator do?

Give it a company domain and it returns both GTM buying signals in one flat row: hiring intent and tech stack, merged, plus a composite score from 0 to 100, a recommended action and a plain-English summary.

It runs GTM Hiring Signal Scraper and Tech Stack Signal Detector in parallel, merges both results, and adds the composite layer on top. No nested JSON, no second lookup, no glue code.

📦 What you get	⚙️ Features and integrations
💯 composite_score, 0 to 100 from both signal sides 🚦 recommended_action, prioritize, nurture or deprioritize 📝 Plain-English gtm_signal_summary 🧾 51 flat fields, both signals in one row	⚡ Both sources run in parallel 🛟 Partial results by design, one side failing keeps the other 🔍 Published score model, rebuild it yourself ⬇️ Export to JSON, CSV, Excel, HTML or XML

Bought by teams who want one qualification number per account rather than two rows to merge and a formula column to maintain.

🚫 This actor does no fetching of its own. It is orchestration only: it calls the two sub-actors over the Apify API and merges what they return. That is also why there is no proxy setting here; the sub-actors handle their own network access.

💡 Why use GTM Signals Aggregator?

Running the two actors separately gives you two rows per company that you merge yourself. This gives you one row with everything merged, plus a composite score, a recommended next action and a summary the standalone actors do not produce.

If you want	Read these fields
One number to sort your list by	composite_score
Three bands instead of a number	composite_signal, recommended_action
A sentence for a rep or an agent	gtm_signal_summary
The hiring side	gtm_hiring_signal, gtm_role_count, signal_strength, top_gtm_role
The tech stack side	crm_detected, seq_tool_detected, gtm_tool_count, the uses_* flags
Whether the row is complete	actor1_error, actor2_error

💯 How the composite score works, in full

composite_score is an integer from 0 to 100, built by adding points from both sides:

Contribution	Points
GTM hiring signal present	20
Hiring signal strength	20 for high, 10 for medium, 5 for low
Open GTM roles	5 per role, capped at 20
CRM detected	15
Sequencer detected	10
GTM tool count	3 per tool, capped at 15

The total is capped at 100. composite_signal and recommended_action then fall out of it:

Score	composite_signal	recommended_action
60 to 100	high	priority outreach
30 to 59	medium	nurture
0 to 29	low	deprioritize

Filter on composite_score when you want your own cutoff, or on composite_signal when the three bands are enough.

▶️ Ready-to-run examples

Example	What it does
Score GTM Buying Signals 0 to 100	Blend both signals into one composite score
One-Line Outreach Recommendation	Fast triage: prioritize, nurture or deprioritize
Generate an AI Lead Briefing	A 3 to 5 sentence agent-ready briefing
Combined Hiring and Tech Stack Enrichment	Both signals merged into one row
Fast Score Without the Summary	Scoring only, for high-volume passes

📋 What data can GTM Signals Aggregator extract?

51 fields per company, from three layers plus the run and status fields.

Layer	Fields
Composite	composite_score, composite_signal, recommended_action, gtm_signal_summary
Hiring	gtm_hiring_signal, signal_strength, gtm_role_count, top_gtm_role, gtm_roles_detected, most_recent_posting_date, ats_platform, career_page_url
Tech stack	crm_detected, seq_tool_detected, marketing_automation_detected, gtm_tool_count, tech_stack_signal, and the uses_* flags for HubSpot, Salesforce, Clay, Apollo, Outreach, Gong, ZoomInfo, Drift, Intercom and Marketo
Run	company_domain, actor1_error, actor2_error, run_time_ms, run_date

⚠️ Partial results are expected behavior, not a fault. If one sub-actor fails, the other side still populates and the matching actor1_error or actor2_error flag is set. Check those flags first if a block of fields is null, because a failed side drags the composite score down exactly as if the signal were absent.

⚠️ false and null mean different things on every row. false is a measurement: the actor looked and the answer was no. null is the absence of one: the actor could not look. When something it needed fails, the row comes back with degraded: true, a plain-language degradation_reason, and every field it could not measure set to null rather than to a confident false or 0. Filter on degraded before you trust a negative.

🛠️ How to score a company's buying signals

1. Open the Input tab and put a bare domain in company_domain.
2. Leave include_summary on unless you are running high volume and do not need the sentence.
3. Click Start.
4. Sort by composite_score, or filter on recommended_action.
5. Check actor1_error and actor2_error before trusting a low score.

🧪 Using it in Clay

Add an Apify enrichment column and map your domain column to company_domain. Every field is a top-level key.

There is a prebuilt Clay table with the enrichment column and formula columns for composite score, recommended action and signal summary: open the template.

⚡ Running at volume

Set include_summary to false for a lighter, faster pass when you only need the score. Turn on explain_mode when you want to see why a score came out where it did.

💵 How much does it cost to score buying signals?

You are charged once per company scored.

Plan	Per company	Per 1,000 companies
Free	$0.0550	$55.00
Bronze	$0.0495	$49.50
Silver	$0.0440	$44.00
Gold	$0.0385	$38.50

There is also an Actor start event at $0.00005, charged once per run per GB of memory.

Free Apify plans get 50 results per calendar month, resetting on the 1st. Paid plans have no monthly cap.

🛑 Your spending limit is respected. Set a maximum cost per run in Apify, or max_charge_usd on the Input tab, and the run stops before it would go over, keeping every result already charged. Each row's billable_events_json lists the events that row was charged for.

💳 This is an orchestrator, so the sub-actors bill you as well. The price above is for this actor's merge and scoring layer. Each source you select in sources runs one sub-actor, and each sub-actor charges its own per-domain event against the token that started the run, which is yours. Budget for every source you select, not just this one.

🧾 What a run really costs, measured end to end

A real run rather than an estimate. Measured 2026-08-12 on a single company.

Charged to your Apify account	Cost
This actor, 1 company plus the start event	$0.0550
gtm-hiring-signal-scraper, $0.05 per company	$0.0501
gtm-tech-stack-signal-scraper, $0.015 per company	$0.0150
Total for that run	$0.1202

The two sub-actors were 54% of that bill. The effective price was $0.120 per company, not the $0.0550 in the table above.

Price cut on 2026-08-23, from $0.0900 to $0.0550. This actor had been priced above the combined cost of the two sub-actors it runs, which meant assembling the two by hand was cheaper than buying the bundle. It is not any more.

That measurement used the default sources, hiring and tech_stack. Every extra source adds one sub-actor run per company at that sub-actor's own Free plan list price:

Source in sources	Sub-actor	List price per company, Free plan
hiring (default)	gtm-hiring-signal-scraper	$0.05
tech_stack (default)	gtm-tech-stack-signal-scraper	$0.015
funding	funding-press-signal-scanner	$0.03
events	event-presence-index	$0.005
workplace	workplace-program-detector	$0.008

Turn a source off by leaving it out of sources, and its sub-actor does not run.

⌨️ Input

Everything is on the Input tab.

Field	Type	Default	What it does
company_domain	string	required	Bare company domain.
sources	array	hiring, tech_stack	Which signals to aggregate. See below.
include_summary	boolean	true	Produce the plain-English summary. Off is faster at volume.
explain_mode	boolean	false	Show how the composite score was arrived at.
max_charge_usd	number	none	Optional. The run stops before the next result that would take this run's charges over this amount, and keeps every result already charged. Leave empty to rely only on the maximum cost per run you set in Apify.

🎛️ Choosing your signal sources

sources decides which signals the composite is built from. Five are available:

Source	What it measures	Max weight
hiring	Open GTM roles, signal strength, role count	60
tech_stack	CRM, sequencer, total GTM tool count	40
funding	A raise in the last 365 days, plus press event volume	30
events	Conference appearances, and whether the company runs its own	20
workplace	Formal workplace programs, an HR maturity and headcount proxy	20

The default is ["hiring", "tech_stack"], which is what this actor has always run. An existing task, saved input or schedule therefore costs exactly what it cost before and scores exactly what it scored before.

The score is normalized over the sources you selected, not over all five. That is what makes the setting safe to use. Without it, adding a third source would drag every two source score toward the floor, and a funding only run would be scored out of 100 on a 30 point scale and always read "deprioritize". With it, a maxed out funding only run is a 100, and the historical pair still sums to 100 out of 100.

Each extra source is one more sub actor run on your own Apify account, so sources is the cost dial as well as the depth dial. Start with the pair, add funding for timing, add the rest when you are working a short list rather than a long one.

Two honesty rules the row follows:

• A source you did not select reports null on every one of its fields, never false. false would claim the actor looked.
• A source you selected that could not be reached is named in sources_failed and scored out of the set, not scored as a zero. If every selected source fails, the composite is null rather than a confident zero.

📤 Output

One flat row per company, exportable as JSON, CSV, Excel, HTML or XML.

{
  "company_domain": "example.com",
  "composite_score": 72,
  "composite_signal": "high",
  "recommended_action": "priority outreach",
  "gtm_signal_summary": "Example is hiring three AEs and an SDR, and runs HubSpot with Outreach. Strong pipeline investment underway.",
  "gtm_hiring_signal": true,
  "signal_strength": "high",
  "gtm_role_count": 4,
  "top_gtm_role": "Enterprise Account Executive",
  "ats_platform": "greenhouse",
  "career_page_url": "https://boards.greenhouse.io/example",
  "crm_detected": true,
  "seq_tool_detected": true,
  "uses_hubspot": true,
  "uses_outreach": true,
  "gtm_tool_count": 3,
  "tech_stack_signal": "high",
  "actor1_error": false,
  "actor2_error": false,
  "run_time_ms": 18420,
  "run_date": "2026-06-18T16:21:40.382Z"
}

🧾 More fields on every row

Field	Type	What it holds
uses_hubspot	boolean	True if HubSpot was detected, or null.
uses_salesforce	boolean	True if Salesforce was detected, or null.
uses_clay	boolean	True if Clay was detected, or null.
uses_apollo	boolean	True if Apollo was detected, or null.
uses_outreach	boolean	True if Outreach was detected, or null.
uses_gong	boolean	True if Gong was detected, or null.
uses_zoominfo	boolean	True if ZoomInfo was detected, or null.
uses_drift	boolean	True if Drift was detected, or null.
uses_intercom	boolean	True if Intercom was detected, or null.
uses_marketo	boolean	True if Marketo was detected, defaults to false, or null.
has_recent_funding	boolean	True when a funding round was found in the last 365 days. Null when the funding source was not selected.
funding_event_count	integer	Number of press events found for the company. Null when the funding source was not selected.
latest_funding_event_date	string	Publication date of the most recent press event. Null when the funding source was not selected.
most_recent_press_headline	string	Headline of the most recent press event. Null when the funding source was not selected.
event_count	integer	Number of conferences and industry events the company appeared at. Null when the events source was not selected.
most_recent_event_name	string	Name of the most recent event. Null when the events source was not selected.
most_recent_event_year	integer	Year of the most recent event. Null when the events source was not selected.
runs_own_event	boolean	True when the company runs a conference of its own. Null when the events source was not selected.
workplace_program_count	integer	Number of formal workplace programs detected. Null when the workplace source was not selected.
has_dei_program	boolean	Null when the workplace source was not selected.
has_erg	boolean	Null when the workplace source was not selected.
has_learning_program	boolean	Null when the workplace source was not selected.
sources_requested	string	Comma separated list of the signal sources this row was built from.
row_status	string	ok, degraded, error, or notice (a notice row carries a message, not a result). Present on every row.
error_reason	string	Why the row is degraded or an error, in plain words. Null on an ok row. Present on every row.
billable_events_json	string	JSON list of the pricing events this row was charged for, for example [{"event":"apify-default-dataset-item","count":1}]. Lets you reconcile a run from the dataset alone. The once per run start event is not repeated on rows.

💡 Tips

• Sort by composite_score rather than filtering on composite_signal when your list is large. The bands are coarse by design.
• Always check actor1_error and actor2_error before writing off a low scorer. A failed side looks identical to an absent signal in the score.
• Drop include_summary for bulk passes and turn it back on for the shortlist.
• Use sources: ["hiring", "tech_stack"] for the wide pass and add funding and events only on the shortlist. Sub actor runs are the cost.
• The score model is published above. If your ICP weights hiring more heavily than tooling, rebuild it from the raw fields, which are all on the row.

⚠️ Known limits

Hiring detection covers Greenhouse, Lever, Ashby, Workable, SmartRecruiters, and Personio. Companies on other platforms return no hiring data, which reads as an absent signal rather than an error.

Tech stack detection reads public-facing scripts and network calls. Tools loaded server-side or behind an auth wall may not appear, and backend-only tools such as Clay leave no detectable signal at all.

A failed sub-actor lowers the composite score. The error flags exist so you can tell that apart from a genuinely quiet company. They are not decorative.

Sub-actor cold starts affect run time. Most runs finish in well under a minute, but a cold container can add a few seconds.

No proxy configuration here. This actor does no fetching of its own.

❓ FAQ

Why should I use this instead of running both actors myself?

One row instead of two, plus a composite score, a recommended action and a summary the standalone actors do not produce. No column merging and no formula columns to maintain.

Why is a block of fields null?

One of the two sub-actors failed. actor1_error and actor2_error tell you which.

Can I change the scoring weights?

Not inside the actor, but the full model is published above and every raw field is on the row, so you can compute your own score downstream.

Does it cost more than running the two actors separately?

It costs this actor's fee plus both sub-actors' fees. What you buy for the extra is the merge, the score and the summary.

Is there a proxy setting?

No, and there is nothing this actor could use one for. The sub-actors handle their own network access.

🧩 Want other GTM data?

Mamba Labs builds a fleet of enrichment actors that share one flat output convention:

Actor	Actor ID
Account Hiring Watch	gRAZA5WBi1r6L0gy7
Agent Accessibility Auditor	anxbRv0lKrpQ1pnua
Agent to Brokerage Resolver	ePRRGLxOGyaVHO4dI
AI Answer Visibility Tracker	RJZvzKniYK4nswcMg
AI List Extractor for Any Website	D5hOdIez45M0NX5jD
AI Tooling Detector	EwkHhmqiuJgRoVEbE
Amazon Product, Price, and Stock Tracker	MtJPZoyjJ3IeqTFyo
API Deprecation and Breaking Change Monitor	pHsbS3xXUK8Jqutz5
App Store and Google Play Review Monitor	MaCMEhGrbchxQZ92G
B2B Reddit Conversation Monitor	SoDryzzsjmY3SsiyG
Bluesky Brand Presence Mapper	eLpxzP4IuXznlFVND
Bluesky Posts Scraper	KldSjS7wjoeZ7vQGm
Brand Sponsor Finder	PPgK6xrgOmvL7Nrys
Bulk WHOIS and RDAP Domain Age Lookup	MTrkG5FLU6SjqLdJY
Career Change Signal Engine	vX2Yga1a6wGaXaP14
Company Careers Page Jobs, 17 ATS Boards	izP0rfIgf5YaRZfIh
Company Change Event Feed	oX44rS0fkEJ3rXLWe
Company Discovery List Builder	jch0HdaZnpbMqlcGS
Company Firmographic Enricher	YlUtLWjfPpqykmB8g
Company Funding Record	OFS4Mt1gyYNtGfUbE
Company Headcount Estimator	aHC4GROnNt6kfZe5r
Company Identity Resolver	lr8fTRAmZCBZmuwwh
Company Role Email, Phone and Address Finder	4mMncKaJykiq94grz
Company Social Presence Mapper	4k6CCemkgBDz18m2h
Competitor Price, Stock, and Changelog Monitor	yxdgCbUQ4PYQWwRNU
Contact Classifier	0lGSeYJmniXhGANnO
Contractor License Search, CA, TX, WA, OR, LA	WBYK3CPI2kdeq4bP1
CRM Contact Job Change Checker	L8UbEmMJC1XYPSZOb
Domain Deliverability Checker	0tVgxI7A6o9jMlxmc
Domain to LinkedIn URL Resolver	3HtnSaqPHOg1Qg5gx
Ecommerce Platform Profiler	5G2NeYjBbQTjaTn9L
Ecommerce Store Discovery	PeHu4sLyFvQHXUUrX
Email List Verifier, Bring Your Own Key	CzVJ4NlaCKk80xpgE
Event Presence Index	WLhMy8fMDgsxdYxv5
Expert Witness Finder, US and Australia	LIpwlgfE3LaQBaXd4
Facebook Posts Scraper	T6jxIeq4ua0SLXnqG
Funding and Press Signal Scanner	FS13X6dhQVgX3XOM6
GDPR Cookie Banner and Consent Checker	TrKyOS0KBoGC35nbr
GitHub Followers and Following Export	SGNxDmFb9v3tRDaFI
GitHub Organization Signal Scanner	ahF8RRpv5mzPao5CN
Google Ads Transparency Center Scraper by Domain	1eOwxhBVxzvOXAm4s
Google Maps Places Scraper	SuvHCNJ7pIuw6gn54
Google Trends Scraper	OeDzaACKmBYJXzHZk
GTM Hiring Signal Scraper	D7O1SA2EqwHGsGr1P
GTM Signals Aggregator	xKdRfnfFNkdMpFuNs
Healthcare Provider NPI Enricher	MrgbHzcpDq3mOv9Df
ICP Fit Scorer	W161DT8W4kW55dMFh
Influencer Change Monitor	d2VVgahNL6UmcLkhg
Influencer Lead List Builder	KnmByszcv135yM30G
Influencer Profile Scraper	EqEnklDVMIcB1V499
Influencer Search Across TikTok, Instagram and YouTube	tpkedmloIIWtXx6sg
Influencer Talent Agency Lookup	zCuX4Mgyg6JvXgGzd
Instagram Facebook Brand Presence Mapper	HXh65I0JBjPsunbY8
Integration and Marketplace Presence Mapper	V0guNuvM85Q61f2gj
Job Board Keyword Signal Scanner	4DvqpvhMR74NLcDDY
Job Posting Monitor	QCfICD9kOPiOdj5iI
Journalist and Media List Builder	mAfizsZC8qRoR0ecS
Launch Directory List Builder	cRq3bmvEZxZtioQ7U
Lead File Import Checker	RwlGVXTf46LqZdUgt
Legal Entity Resolver	KHFyPCDIx7CyqULYm
Link in Bio Scraper and Newsletter Detector	OorucdheTIgu7RFzK
LinkedIn Company Page Mapper	zOPm6A9JEhUGcIdFW
LinkedIn Jobs Feed	SH6N0d8cz46BhCC8B
LinkedIn Post Tracker	oiGLNPuaf5BRaz9K5
Local Business Lead Builder	qMN97aoHhsAS5bznV
Local Rank and Map Pack Tracker	gv0kyHGDIg8XscclB
Lookalike Companies from Seed Domains	e2O426WBAbLLQL4Z2
Manufacturer and Supplier Finder	Qcsdmd0lUfPzVkonh
MCP Server Change and Health Monitor	IeqjWF9U0BdDQFY08
Media URL Transcriber	Syfr7Ya3EsHgWfrB6
Meta Ad Library Ads by Company, Graph API	J1GWlSXfSGU3u8Wng
Mobile Phone Waterfall	6OOgPktf7RIa5XCOP
New Hire Finder	cKuc38p4nYHNF3IW8
Newly Registered Companies, UK, Norway, Finland, France	g2WfZhWvCtc57g2Jo
News and Media Mention Monitor	gol1PPUKrsIk4QLBd
Newsletter Sponsor Tracker	qSwpqxD1TiPZpLQHv
Open Tender and RFP Finder	kPTGz6t6TFoUUyfaO
Outbound Stack Detector	v43UJC8r7qW7cBSTG
Page Finder and Extractor	TpurgcOZbVnknlaiC
Partner and Member Directory Extractor	ZX0XJPPUvnFJxeC1a
People Finder & Email Verifier	LEgloG7tWgQl4o6TD
Person LinkedIn URL Resolver	ssuyPV8LnEsL8qbnb
Person Social Footprint Mapper	jfQXa2zW9FHCJot9K
Personal Email to Company Resolver	AefT5Ye6YNPFQwJBf
Pinterest Brand Presence Mapper	d1HvjZfKhgvWsoEwn
Podcast Guest and Host Finder	o2MGSvgbSLeeXOsIo
Product Hunt Daily Launches with Maker Domains	O4VX63drWK9Y9x641
Prospect Engine	q9rT6v64nUJdPexOQ
Public Feature Board and Roadmap Monitor	rcHF5gj5r2Vqnry35
Publishing Frequency Tracker	TbLwaUUATdYb6wp4N
Recent Funding Rounds Feed	KueVp9MOz7iOoAgDp
Reddit Brand Presence Mapper	pKQZmDaT70SEb54bZ
Reddit Search and Thread Scraper	4Gsth5cP2Xt9V2xDN
Reporting Window Finder	uINxR7a1IW8qUTRUX
Research Topic Institutions and Authors	OH8ANqt7bCKbo72Gk
Sequencer Event Poller	S6s8Rjd61tGdrMRk4
Sequencer Lead Push	0Jv27VeWM5tSZQs9x
Shopify App Store and Chrome Web Store Listings	gaY0BzkLyN77mD3wI
Site Crawler for Links and Tables	lLLJ6JxZ4XqN8ULz0
Substack Newsletter and Posts Scraper	UEFsdwiPLmPNVy9xd
Team Page People Extractor	j8a9zX0k4mNbvDbWK
Tech Stack Signal Detector	qyd7nNyqFPelQViBx
Tennis Match Data	v3OX6zxXHZOf2yurO
Threads Posts by Keyword, Exact Match	2OT8K8RiEEWtaj2MW
TikTok Brand Presence Mapper	fco8FvoJcSafbeyd1
Trustpilot Reputation Enricher	3lIqfCt61j4hqE1xz
US and UK Contract Award Winners by Company	zhEtllASykOcx9hJ8
US Import Records Buyer Finder	VIMafpKF5ggOw0COU
VC and Accelerator Portfolio List Builder	chH8hrfuoqqolW06m
Website Accessibility Checker, WCAG 2.2 AA	P3jBj3FRghKEhX52z
Website Phrase and Business Model Classifier	CBuZ9k7sRwBeiKiCS
Website Traffic Rank Estimator	GnOFUzlNVIoXgeJjH
Work Email Waterfall	OT6xqTFThC0Rjf7Zj
Workplace Program Detector	Li6pPDhqbdzFz3h7G
X Brand Presence Mapper	oLOgadhHUkNAyDA3w
YouTube Channel from Company Domain	6hSpD3kd2s2IQ4VSs
Zillow Listing Details	yG4zM0Enfsw2NefJ1

Every actor in the suite takes a domain or a company and returns one flat row, so they stack in the same Clay table without reshaping anything.

🆘 Support

Found a bug or want a field added? Open an issue on the Issues tab. Response time is typically within 48 hours.

ℹ️ Sourcing and legal. This actor fetches nothing itself. Hiring data comes from the public job board APIs of Greenhouse, Lever, Ashby, Workable, SmartRecruiters, and Personio; tech stack data comes from rendering the company's own public web pages. No personal data is collected: the output describes a company's hiring posture and its website's software, not people. You are responsible for how you use the output.

Built by Mamba Labs.