Data library
Customer support data for AI.
Quick answer
Customer support data pairs real customer problems with the steps experts took to resolve them, often with satisfaction or QA scores. It is among the most sought-after data for training and evaluating AI support agents.
What's included#
- Ticket threads with resolutions
- Chat transcripts
- Macros and canned responses
- CSAT and QA scores
Where it typically lives
How AI developers use it#
- Support agents
- Agent-assist
- Intent classification
Preparation and privacy#
- Redact names, emails, order and account numbers
- Remove attachments with personal data
- Check client ownership if outsourced
How SourceX approaches it#
Preparation requirements are agreed with you before any work begins, and the approved dataset is delivered only after an executed agreement and your explicit authorization of buyer access.
Industries with this data
Datasets built from this data
Dataset specs AI labs can request through SourceX, sourced to spec from businesses like yours.
- Customer and revenueCustomer support ticket datasetsA customer support ticket dataset is a de-identified export of real support cases: the customer's messages, agent replies, internal notes, escalations,…TextStructured records
- Customer and revenueContact center call recordings and transcriptsA contact center call recording dataset is a collection of recorded customer service and support calls, usually with speaker-labeled, time-aligned…AudioTextStructured records
- Customer and revenueSales CRM pipeline historiesA sales CRM dataset is a record of how deals actually moved through a company's pipeline: opportunities with their stage changes, close-date pushes and…Structured recordsText
- Customer and revenueSales call recordings and transcriptsA sales call transcript dataset is a set of recorded sales conversations, such as discovery calls, product demos and negotiations, with speaker-attributed…AudioTextStructured records
See listings in the data catalog →
Typical fields#
- Record ID (pseudonymous)
- Created and closed timestamps
- Category or type
- Request or input text
- Ordered steps or actions
- Outcome or resolution
- Role of each actor (not identity)
Sample record
Illustrative · fictional
- record_id
- rec_8f21c0
- created / closed
- 2023-04-11 → 2023-04-12
- category
- Billing question
- request
- Customer [NAME] asks why invoice [ID] was charged twice.
- steps
- 1. Checked payment log 2. Found duplicate 3. Issued refund
- outcome
- Resolved · refund issued
- actors
- customer, agent_tier1
What buyers look for#
- Complete start-to-finish records
- Consistent fields over years
- Clear, labeled outcomes
- Low duplication
- Work done by skilled professionals
- Edge cases and failures, not only successes
Personal details removed#
- Names
- Email addresses
- Phone numbers
- Postal addresses
- Account, customer and order numbers
- Government IDs
- Payment details
- Health information
- IP addresses and device IDs
- Credentials, keys and hostnames
How buyers test it#
- Sample review by buyer researchers
- Residual personal-data scan
- Duplicate and leakage check against public data
- Label and outcome accuracy spot-check
- Rights and consent documentation review
Rules that may apply
Federal Wiretap Act 18 U.S.C. 2511
Applies to recorded calls and intercepted communications; some states require all-party consent. Assessed deal by deal.
California Consumer Privacy Act Cal. Civ. Code 1798.100 et seq.
May apply where records include personal information of California residents and the business meets the law's thresholds. Assessed deal by deal.
Reviewer note: applicability is confirmed with counsel for each deal. Not legal advice.
All data types · Legal and contract data · Software engineering data · Accounting and finance data
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