How Expert Coaches Build Confidence: 19,337 Labeled Calls

Data Product Features

• Technique-to-outcome pairs: Each technique is linked to the student's emotional change.
• Breakthrough moments: Marked, with the turns that led to them.
• Hot-seat structure: See one student's full arc inside a group session.
• Emotional arcs: Every speaker scored from -3 to +3 across the call, with notes on each shift.
• Conversation flow: Check-in, exploration, teaching, problem statement, reframe, commitment or close.
• Tone on every segment: A tone label and a strength of low, medium or high.
• Voice measurements: Speaking speed, pitch, loudness and pauses.

Distribution

• Size: About [X] GB of audio (about 43 GB per 1,000 hours), plus about [X] MB of text.
• Data Volume: About [X] hours; 19,337 calls; about 220 transcript segments per hour; 11 fields per segment, plus call-level data.

Usage

• AI coaches and tutors: Ask better questions and respond with care.
• Empathetic response models: Learn what helps a person who is stuck.
• Reward models: Use the before-and-after scores as a signal for supportive dialogue.
• Coaching quality scoring: Rate coaches and AI coaches against expert patterns.

Coverage

• Geographic Coverage: Global; most participants are in the United States, Canada, the United Kingdom and Australia.
• Time Range: 2015 - 2026.
• Demographics: Adult entrepreneurs and small-business owners, talking with expert business coaches. Language: English.

License

AI Training Rights

• Use the Data Product to train, fine-tune, and evaluate machine learning models, including large language models.
• Incorporate Data Product content into models and commercialize resulting model outputs.
• Create derivative works (model weights, embeddings, etc.) for any lawful purpose.

• The Data Product itself may not be sold, redistributed, or shared outside of licensed usage.
• Licensee may not attempt to identify any person in the data.
• Licensee must comply with all applicable laws, including data protection and privacy regulations.

Who Can Use It

• AI coaching apps: For coaches that ask better questions.
• Edtech and tutoring platforms: For AI tutors.
• HR, learning and development platforms: For workplace coaching tools.
• Conversational AI researchers: For studies of supportive dialogue.

Data Dictionary

| Column Name | Data Type | Description | Possible Values/Notes |
|-------------|-----------|-------------|-----------------------|
| call_id | string | Random ID for the call | e.g. CC-WYKCVAP5 |
| collection | string | Which collection the call belongs to | coaching_calls |
| call_type | string | Kind of session | e.g. coaching_session |
| duration | number | Call length in seconds | e.g. 4289.34 |
| recording_period.year, .quarter | integer | When the call was recorded | Year and quarter only, for privacy |
| language | string | Language code | en |
| audio_quality.snr_db, .clipping, .channels | number | Signal-to-noise ratio, clipping share and channel count | |
| source_format | object | Original recording format | Container, codec, bitrate, sample rate, channels |
| asr_engine, asr_mean_confidence | string, number | Speech recognizer used and its average confidence | Confidence from 0 to 1 |
| participants[] | array | Everyone who spoke: placeholder, pseudonym, role, how the role was assigned, and confidence | Roles: coach, student, participant |
| primary_theme, secondary_themes | string, array | Main and supporting topics | e.g. mindset_confidence, goal_setting, offer_design |
| summary | string | Short summary of the whole call | |
| stages[] | array | Titled stages of the call, with start and end times, segment range and summary | |
| blocks[] | array | Session formats within the call, with the student in focus | hot_seat, qa, teaching_block |
| segments[].id | integer | Order of the segment in the call | |
| segments[].start, .end | number | Start and end time, in seconds | |
| segments[].speaker | string | Placeholder for the speaker | e.g. [Coach A], [Student S-9831] |
| segments[].text | string | What was said, with identifying details replaced | e.g. [Person 3], [Business 2], [Product 1], [Location 1] |
| segments[].tones[] | array | Tone and strength | Tone e.g. encouraging, frustrated, curious, empathetic, realization, relieved; strength: low, medium, high |
| segments[].flow_role | string | What the segment does in the conversation | e.g. check_in, exploration, teaching, problem_statement, reframe, commitment, close |
| segments[].behavior | string | Coaching technique used, if any | e.g. diagnostic_question, normalize, give_example, reframe, celebrate_win, reflect_back, commitment_confirm |
| segments[].moment | string | Key moment, if any | e.g. insight, breakthrough, resistance |
| segments[].overlap | boolean | Whether speakers talked over each other | |
| segments[].acoustics | object | Words per minute, average pitch, pitch variation, relative loudness, pause before | Can be empty for very short segments |
| emotion_notes[] | array | Emotion shifts: speaker, from tone, to tone, the segment that triggered it, and a note | |
| transitions[] | array | Turning points in the conversation, with a note | e.g. coach_redirect, topic_shift, breakthrough, student_pushback |
| emotional_arcs[] | array | Each speaker's emotional path through the call, point by point | Score from -3 to +3 |
| behavior_effects[] | array | How each technique landed: the student, their emotional score before and after, and the direction | Direction: up, flat, down, no_response |
| redaction_summary | object | Count of each kind of detail replaced | e.g. person, business, product, location, date, amount, contact |
| qc | object | Privacy and consistency checks run on the call, and their results | |
| human_checked | boolean | Whether a person has reviewed the call | Currently false for all calls |
| consent_status, consent_terms_version | string | Consent record for the call | |
| schema_version, taxonomy_version, pipeline_version | string | Versions of the file format, label set and pipeline | |