B2B Buying Signals: Hiring + Tech Stack Intent for Clay

📡 What can B2B Buying 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 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 🧾 32 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 B2B Buying 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 B2B Buying Signals Aggregator extract?

32 fields per company, from three layers.

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	$0.09	$90.00
Bronze	$0.0855	$85.50
Silver	$0.081	$81.00
Gold	$0.0765	$76.50

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

💳 This is an orchestrator, so the two sub-actors bill you as well. The price above is for this actor's merge and scoring layer. GTM Hiring Signal Scraper and Tech Stack Detector each charge their own per-domain event against the token that started the run, which is yours. Budget for all three, not just this one. Free Apify plans get 10 results per calendar month, resetting on the 1st; paid plans are unlimited.

🧾 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	$0.0900
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	$0.1552

The two sub-actors were 42% of that bill. The effective price was $0.155 per company, not the $0.09 in the table above.

Both sub-actors run on every company, because the score needs both signal sources to compute. There is no input that turns one off, so the ratio above is stable rather than input-dependent.

⌨️ Input

Everything is on the Input tab.

Field	Type	Default	What it does
company_domain	string	required	Bare company domain.
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.

📤 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"
}

💡 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.
• 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 and Ashby. 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 custom actors for B2B go-to-market teams. The public versions of that work live here on the Store, so our users get the same tooling we build under contract.

🧑‍💼 GTM Hiring Signal Scraper	🧱 Tech Stack Detector
🔑 Job Board Keyword Scanner	🔗 Domain to LinkedIn URL Resolver
🎯 ICP Fit Scorer	📋 Job Posting Monitor
📬 Domain Deliverability Checker	🏢 Company Firmographic Enricher
🌐 Company Social Presence Mapper	🪪 Company Identity Resolver
💰 Funding and Press Signal Scanner	🔄 Company Change-Event Feed
👤 People Finder and Email Verifier	🚀 Prospect Engine
🤖 AI Tooling Detector	📮 Outbound Stack Detector
📝 Publishing Frequency Tracker	✉️ Work Email Waterfall Finder
⏩ Sequencer Lead Push	🏅 Workplace Program Detector
👥 Team Page People Extractor	🧭 Company Discovery List Builder

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.

🛠️ Need something custom built for you or your team? Tell us what you are trying to find and we will build it. Talk to Mamba Labs.

🆘 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 and Ashby; 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.