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Generate CRM Synthetic Data in Python

CRM data is the backbone of every B2B sales tool — but it's also sensitive, and real pipeline data is never available early in development. Misata generates a four-table CRM dataset: companies, contacts, deals, and activities. Deal values are lognormally distributed with a median around $25k (the shape of real B2B pipelines), stage distribution reflects real funnel drop-off, and activity types mirror actual sales rep behavior — 40% email, 30% call, 20% meeting, 10% demo.

Every deal references a valid contact and company. Activities are tied to real deals and contacts. probability increases monotonically with stage advancement. close_date falls in the future for open-stage deals.

import misata

tables = misata.generate("A B2B SaaS CRM with 500 companies and a full sales pipeline", rows=500, seed=42)
print(list(tables.keys()))   # ['companies', 'contacts', 'deals', 'activities']
print(tables["deals"].groupby("stage")["value"].describe())

What Misata generates

Four tables: companies → contacts and deals (both linked to companies), and activities (linked to deals and contacts). Full referential integrity throughout.

Tables and columns

Table Key columns
companies company_id, name, industry, size, country, revenue, website
contacts contact_id, company_id, name, email, phone, title, lead_source, created_at
deals deal_id, contact_id, company_id, name, value, stage, probability, close_date, owner
activities activity_id, deal_id, contact_id, type, subject, outcome, activity_date

Realistic distributions

  • Deal values lognormal ~$25k median — long tail of enterprise deals matching real B2B pipelines
  • Pipeline stages: prospecting 35%, qualification 25%, proposal 20%, negotiation 12%, closed-won 8%
  • Activity types: email 40%, call 30%, meeting 20%, demo 10% — matching real sales rep behavior
  • probability is correlated with stage — later stage = higher close probability
  • lead_source varies across organic, paid, referral, and outbound channels

Quick start

import misata

tables = misata.generate(
    "B2B SaaS company with 500 accounts and a 6-month sales pipeline",
    rows=500,
    seed=42,
)

# Pipeline value by stage
print(tables["deals"].groupby("stage")["value"].agg(["count", "sum", "mean"]))

# Activity volume by type
print(tables["activities"]["type"].value_counts())

# Win rate
closed = tables["deals"][tables["deals"]["stage"] == "closed-won"]
print(f"Win rate: {len(closed)/len(tables['deals']):.1%}")

Common use cases

  • CRM platform demos — populate a demo environment with realistic accounts, contacts, and pipeline data so prospects see a lived-in product
  • Lead scoring model training — generate thousands of contacts with source, title, and deal outcomes to train and evaluate propensity models
  • Revenue forecasting prototypes — build weighted pipeline models against deals with stage-accurate probability values before connecting real data
  • CRM migration testing — validate ETL scripts and field mappings against a full relational dataset before touching production records
  • Sales analytics dashboards — build conversion rate, activity cadence, and pipeline velocity reports on realistic CRM data
  • Integration QA — test webhooks, sync jobs, and API integrations against realistic email formats, phone numbers, and company sizes

Advanced: Q4 close push narrative

tables = misata.generate(
    "SaaS company with aggressive Q4 close push — deal activity spikes in October-November, "
    "strong closed-won in December",
    rows=1000,
    seed=42,
)

Advanced: locale-aware generation

# European B2B — German, French, Spanish company names and contacts
tables = misata.generate("European B2B software company with accounts in Germany and France", rows=500)

# US enterprise — US company names, enterprise deal sizes
tables = misata.generate("US enterprise software company with Fortune 500 accounts", rows=300)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "B2B SaaS CRM with 500 accounts",
    min_quality_score=85,
    smart_correlations=True,  # auto-adds company_revenue↔deal_value correlation
    rows=500,
    seed=42,
)