Quote-to-close sales records with win and loss outcomes, 34 firms

Summary

Deal records from 34 US B2B firms of 10 to 120 staff in 9 industries, each joined to the quote emails and follow-ups behind it. Every opportunity keeps its stage history, deal size band, close date and a recorded win or loss with the reason the rep logged, which suits forecasting, next-step drafting and loss analysis with known outcomes. Contact, company and staff names, emails, phone numbers and addresses are replaced with placeholders, and contact records never ship on their own.

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 pricing

At a glance

Planned scope. Volume, sources and timing are confirmed in writing before you sign.

Records: 306Kopportunities with linked activity
Size: 12 GB
Tokens, estimated: 1.3B
Years: 2016 to 2026
Firms: 34

Owner profile: 34 US B2B firms, 10 to 120 staff each, in 9 industries including industrial supply, IT services, signage and commercial cleaning
Industry: Multiple industries
Data type: Sales and CRM
Headcount: 10 to 120 staff per firm
Region: United States
Languages: English
Formats: JSONL, Parquet
Updated: 2026-10-11

Sources

System	Kind	Records
HubSpot record exports	CRM	181K deals, 1.4M activities
Salesforce	CRM	125K opportunities, 930K activities
Google Workspace mail	Email	3.5M linked emails

Good for

deal outcome forecasting with recorded wins and losses
training sales agents to draft next steps from deal history
classifying and analyzing loss reasons
evaluating long-horizon reasoning over stage history and email

Fields

Field	Type	Description
opportunity_id	string	Stable opportunity id
firm_code	string	Code of the contributing firm
industry	enum	One of 9 industry codes
created_at	date	Date the opportunity was opened
closed_at	date	Date it closed, won or lost
stage_history	array<string>	Stage changes in order, with dates
amount_band	enum	Deal size band
outcome	enum	won, lost or no_decision
loss_reason	string	Reason the rep logged, as written
linked_emails	array<string>	Ids of the emails tied to the deal, shipped in the email file
activity_count	int	Calls, meetings and tasks logged on the deal

Example record

{
  "opportunity_id": "[OPP_33815]",
  "firm_code": "F-09",
  "industry": "commercial_cleaning",
  "created_at": "2025-01-14",
  "closed_at": "2025-03-03",
  "stage_history": [
    "Walkthrough scheduled 2025-01-14",
    "Proposal sent 2025-01-29",
    "Revised proposal 2025-02-12",
    "Closed lost 2025-03-03"
  ],
  "amount_band": "50K to 100K USD per year",
  "outcome": "lost",
  "loss_reason": "Incumbent matched our number after seeing our scope. Prospect stayed for continuity.",
  "linked_emails": [
    "[MSG_120448]",
    "[MSG_120502]",
    "[MSG_121987]"
  ],
  "activity_count": 14
}

One record, de-identified. Placeholders such as [PERSON_12] and [COMPANY_4] stay the same across the whole dataset, so threads still read correctly.

Licenses available

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.

The full method