3PL client support tickets and warehouse exception notes, 10 operators
Summary
Client tickets from 10 third-party warehouse operators serving ecommerce and wholesale brands, joined to the warehouse exceptions behind them: receiving discrepancies, mis-picks, inventory adjustments, missed carrier pickups and returns disputes. Each ticket carries the full thread, the ops notes and the resolution, so models learn how a warehouse explains and fixes its own errors. Client brands, end consumer names and addresses, and order numbers are replaced with placeholders.
Collected to order. We collect it from US companies for your order. Scope, volume and timing are confirmed in writing before you sign.
Pricing on request
Request scope and pricingAt a glance
- Planned scope. Volume, sources and timing are confirmed in writing before you sign.
- Records
- 9.1Mtickets, exception events, emails and channel messages
- Size
- 12.5 GB
- Tokens, estimated
- 1.6B
- Years
- 2020 to 2026
- Firms
- 10
- Owner profile
- 10 US third-party logistics warehouse operators, 25 to 180 staff each, 1 to 4 buildings each
- Industry
- Logistics and freight
- Data type
- Support and service
- Headcount
- 25 to 180 staff per firm
- Region
- United States (West, Midwest, Southeast)
- Languages
- English, Spanish
- Formats
- JSONL, Parquet
- Updated
Sources
| System | Kind | Records |
|---|---|---|
| Zendesk | Support tickets | 1.18M tickets |
| Warehouse management system exception logs | Databases | 4.4M exception events |
| Google Workspace mail | 2.6M messages | |
| Slack public channels | Team chat | 920K messages |
Good for
- training support agents to answer 3PL client tickets with evidence from warehouse records
- root-causing mis-picks and receiving discrepancies from scan and exception history
- evaluating ticket triage and SLA routing across client tiers
- drafting client-facing incident summaries for inventory adjustments
Fields
| Field | Type | Description |
|---|---|---|
| ticket_id | string | Ticket number |
| firm_code | string | Pool member code |
| client_code | string | Placeholder for the warehouse's client |
| channel | enum | email, portal or phone note |
| category | enum | receiving, inventory, outbound, carrier, returns or billing question |
| priority | enum | low, normal, high or urgent |
| created_at | timestamp | When the client opened the ticket |
| thread | array<string> | Messages in order, prefixed client or agent |
| wms_exceptions | array<string> | Linked warehouse exception events |
| resolution | string | How the ticket was closed |
| solved_at | timestamp | When the ticket was solved |
Example record
{
"ticket_id": "ZD-482190",
"firm_code": "3PL-06",
"client_code": "[COMPANY_44]",
"channel": "portal",
"category": "receiving",
"priority": "high",
"created_at": "2024-11-04T10:12:00-08:00",
"thread": [
"client: ASN 77341 shows 1,440 units of [SKU_118] but the portal has 1,296 available. Our promo goes live Thursday.",
"agent: Checking with the receiving lead now.",
"agent: Receiving counted 81 of 90 cartons on the PO, 16 units per carton. Pallet label photos attached. Your vendor shipped 9 cartons short.",
"client: Ok thanks, we will chase the vendor."
],
"wms_exceptions": [
"RCV-SHORT 144 units, dock 3, 11/01 14:22"
],
"resolution": "Receipt confirmed 144 units short against the ASN. Discrepancy report sent, no inventory adjustment needed.",
"solved_at": "2024-11-04T13:47:00-08:00"
}Licenses available
Exclusive
One buyer. Nobody else can license it during the exclusive term, usually 2 years.
Non-exclusive
Licensed to more than one buyer.
First look
One buyer gets 2 to 4 weeks to make an offer before anyone else sees the dataset.
What ships with it
- Datasheet: what it is, what it is good for, verified size, sources, years, format and limits.
- Provenance file: the owner shown by code, staff notice and consent status, exclusions, mapped to the EU AI Act Article 53 training-data summary and California AB 2013.
- Manifest with a hash for every file.
- QC report and de-identification report.
- Your own copy, with unique canary records.
How it is de-identified
Default exclusions removed, files flattened to text, identifiers found in three layers and replaced with consistent placeholders, and the result measured against a 99% bar for each identifier type in each source.