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Financial Intelligence

Earnings Transcript Signals

Published Updated

48 signal subtypes from quarterly earnings calls with direct executive quotes and speaker attribution. Know exactly who said what about AI investment, cost reduction, and strategic priorities.

Read the API docs
Companies Covered
4,000+
Signal Subtypes
48
Avg Signals per Call
5-15
Delivery
REST API, GCS Push, Generate Insights API, Flat File
Format
JSONL or Parquet
Bucket
gs://autobound-earnings-transcripts/

What Are Earnings Transcript Signals?

Weekly refresh

Earnings transcripts are the records of quarterly calls where public company executives discuss last quarter's results, next quarter's expectations, investment areas, and challenges with analysts. Autobound's signals preserve speaker attribution: when a CFO says budgets are doubling, the signal captures the statement, who said it, their title, and the surrounding context.

We extract 48 signal subtypes across five categories: budget, growth, pain, strategic, and organizational signals. A typical transcript yields 5-15 distinct signals, including quantified commitments (dollar amounts, percentages, headcount), technology mentions, competitor references, and vendor discussions. The evidence field carries the exact quote from the transcript so reps can verify context before reaching out.

Example subtypes

Strategic initiativestrategicInitiative

Example from the signal catalog

Ironclad Insurance announced a three-year initiative to become the leading digital insurer for small businesses.

From an earnings call to your next move

What we detect, what you get, and what you do with it. Pick the path that fits your team.

Sales, marketing and RevOps: Slack, CRM fields and plays.

  1. 0103

    Executives say it on the call

    On quarterly calls, executives name budgets, priorities and problems. We read each transcript and keep who said what.

    1. Earnings call transcript
    Earnings call transcriptJan 10, 2026

    Allocating $500M-$750M for Foot Locker turnaround, including tech asset review.

    A massive budget has been allocated for the Foot Locker integration, explicitly including a review of existing technology assets and legacy contracts. This creates a time-sensitive opportunity for new vendors.

    Extracted
    DICK'S Sporting Goods, Inc.dickssportinggoods.comcompanyInvestment commitmentsignal_subtype
  2. 0203

    You get one clean record

    Each record names the company and its domain, labels what happened, and links back to the source.

    1. Signal record

    DICK'S Sporting Goods, Inc.

    dickssportinggoods.comSpecialty Retail

    Investment commitmentinvestmentCommitment

    Allocating $500M-$750M for Foot Locker turnaround, including tech asset review.

    “A massive budget has been allocated for the Foot Locker integration, explicitly including a review of existing technology assets and legacy contracts. This creates a time-sensitive opportunity for new vendors.”
    Period
    Q3
    Call date
    Jan 8, 2026
    Detected
    Jan 10, 2026

    signal_id 34c1d2c0…

  3. 0303

    Act on it where you work

    Send it to Slack, write it to your CRM, or give it to an AI agent.

    When
    signal_subtype = investmentCommitment with metric_dollar_millions set
    Do
    Exec email that quotes the CEO's line from the call
    1. Your team acts
    or your own code
    #account-signals

    Signal alert from your workflow

    DICK'S Sporting Goods, Inc.· Investment commitment

    Allocating $500M-$750M for Foot Locker turnaround, including tech asset review.

    Play: Exec email that quotes the CEO's line from the call

    Start the playOpen the source

    Illustration. The record fields are real.

Try it on your own accounts

1,000 free credits. Query earnings-transcript in the Signal API or from Claude.

How we build this signal

How we build each earnings call signal

Every week a job reads the previous week's earnings calls and turns each transcript into quoted signals.

Model reads per call
2
Relevance floor
0.7
Refresh
Weekly
  1. 01

    Collection

    Each Tuesday a job pulls the full transcript of every earnings call from the previous week for companies in our public-company list, with each company's profile and website.

  2. 02

    Two focused reads

    A model reads each transcript twice: once for budget, growth and guidance, once for pain points, strategy, leadership and org changes, partnerships and competitors. Each read may only use subtypes from its closed list.

  3. 03

    Quoted evidence

    Every signal carries verbatim quotes from the call, with the speaker's name and title when the transcript identifies them, plus the stated dollar amounts, percentages and timeframe. Code drops unlisted subtypes and relevance scores under 0.7.

  4. 04

    Company check

    The domain comes from the company profile, normalized to the root domain. A call whose company has no website ships nothing. Firmographics are joined by domain.

  5. 05

    Dedup and delivery

    Each signal is fingerprinted on subtype, ticker, fiscal quarter, fiscal year and call date, so a shipped signal is never delivered again. Records are schema-checked before upload.

Earnings Transcript Signal Schema

Earnings transcript signals include speaker attribution, direct evidence quotes, fiscal period context, and quantitative metrics when mentioned.

{
  "signal_id": "34c1d2c0bd4d9d70",
  "signal_type": "earnings-transcript",
  "signal_subtype": "investmentCommitment",
  "signal_name": "DICK'S Sporting Goods allocating $500M-$750M for Foot Locker turnaround, including tech asset review",
  "association": "company",
  "detected_at": "2026-01-10T14:30:00Z",
  "company": {
    "name": "DICK'S Sporting Goods, Inc.",
    "domain": "dickssportinggoods.com",
    "linkedin_url": "linkedin.com/company/dicks-sporting-goods",
    "financial_symbol": "DKS",
    "industries": [
      "Specialty Retail"
    ],
    "sector": "Consumer Cyclical"
  },
  "data": {
    "fiscal_period": "Q3",
    "fiscal_year": 2025,
    "earnings_date": "2026-01-08T08:00:00Z",
    "signal_category": "budget",
    "sales_relevance": "Long-term commitment = sustained vendor opportunities",
    "relevance": 1.0,
    "confidence": "high",
    "sentiment": "positive",
    "headline": "Allocating $500M-$750M for Foot Locker turnaround, including tech asset review.",
    "detail": "A massive budget has been allocated for the Foot Locker integration, explicitly including a review of existing technology assets and legacy contracts. This creates a time-sensitive opportunity for new vendors.",
    "evidence": [
      "We expect these actions, along with other merger and integration costs to result in a future pretax charge of between $500 million and $750 million.",
      "We are looking deeper into the assets that we have in place, some of the technology assets, some of the legacy contracts that we will evaluate."
    ],
    "evidence_speakers": [
      {
        "speaker_name": "Navdeep Gupta",
        "speaker_title": "Chief Financial Officer"
      }
    ],
    "competitors_mentioned": [],
    "vendors_mentioned": [],
    "technologies_mentioned": [],
    "regions": [],
    "metric_dollar_millions": 750,
    "metric_pct": null,
    "metric_headcount": null
  }
}

Bulk data

Need this signal for 10,000+ companies?

Get a free sample file or a bulk feed (API, GCS push, flat file). The sample is real Earnings Transcripts records, as a CSV.

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Measured coverage

What's inside Earnings Transcript Signals

A dated snapshot counted in our production database, not a live count. Minimums, not ceilings.

Coverage for all 35 signal types →
1,250
Companies with fresh data per month
Published baseline, records through Jul 21, 2026
7,619
Records created per month
Published baseline, records through Jul 21, 2026

Top subtypes

48 total
Strategic Initiative9.8K · 7.6%
Efficiency Focus6.7K · 5.2%
Investment Commitment6.5K · 5.0%
Tech Investment6.1K · 4.7%
Market Expansion5.7K · 4.4%
Pain Point5.7K · 4.4%
Cost Pressure5.0K · 3.9%
Product Launch4.9K · 3.8%
🇺🇸United States2.4K · 70%
🇨🇦Canada91 · 2.7%
🇮🇳India65 · 1.9%
🇨🇳China38 · 1.1%
🇬🇧United Kingdom30 · 0.9%
🇮🇱Israel27 · 0.8%
🇧🇷Brazil25 · 0.7%
CA 638TX 317NY 316MA 171FL 138IL 124

Top industries

all industries →
Financial Services220 · 6.5%
Biotechnology Research184 · 5.4%
Software Development166 · 4.9%
Oil and Gas156 · 4.6%
Leasing Real Estate153 · 4.5%
Banking107 · 3.2%
IT Services and IT Consulting100 · 3.0%

Join keys

fill rate
Domain100%
LinkedIn URL92%
Company name100%
Records measured 129,537+Companies reached 3,383+

See for yourself

Check out sample data: real Earnings Transcript records with the same fields the API returns.

Read the API docs
“They provide insight and triggers about accounts we would otherwise have trouble finding.”
Rob Consoli, Chief Revenue Officer, Skuid

Frequently asked questions

Do earnings transcript signals include who said what?
Yes, speaker attribution is a core differentiator. Each signal includes the speaker's name, title, and the exact quote from the transcript. This lets sales reps reference a specific executive's priorities in their outreach.
How many signals does a typical earnings call produce?
A single earnings call typically yields 5-15 distinct signals spanning multiple subtypes, including budget commitments, technology mentions, competitive references, and strategic initiatives.
Are analyst Q&A portions included in signal extraction?
Yes. Our models process both the prepared remarks and the analyst Q&A sections. The Q&A often surfaces pain points and competitive dynamics that executives don't mention in their prepared statements.
How do earnings transcript signals differ from financial fundamentals?
Earnings transcript signals capture qualitative intelligence, specifically what executives say about strategy, priorities, and challenges. Financial fundamentals track quantitative metrics like revenue growth, margins, and earnings surprises. Together they give you the full picture.
Can I track mentions of specific technologies or competitors?
Yes. Our signals include a technologies_mentioned field and can detect competitor references. You can filter for signals mentioning specific technology categories like AI, Kubernetes, or cloud migration.