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date
stringdate
2026-01-01 00:00:00
2026-03-31 00:00:00
company_id
stringclasses
6 values
company_name
stringclasses
6 values
channel
stringclasses
7 values
impressions
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revenue_generated
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conversion_rate
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0
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click_through_rate
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0.23
2026-01-01
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2026-01-01
C001
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2026-01-02
C001
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2026-01-02
C001
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2026-01-02
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2026-01-02
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2026-01-03
C001
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2026-01-03
C001
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2026-01-03
C001
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2026-01-04
C001
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2026-01-04
C001
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2026-01-04
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2026-01-06
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C001
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2026-01-07
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2026-01-07
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C001
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2026-01-08
C001
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2026-01-08
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2026-01-08
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2026-01-08
C001
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2026-01-08
C001
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2026-01-09
C001
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2026-01-09
C001
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2026-01-09
C001
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2026-01-09
C001
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2026-01-09
C001
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C001
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2026-01-09
C001
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2026-01-10
C001
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2026-01-10
C001
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C001
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C001
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C001
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C001
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2026-01-12
C001
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C001
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C001
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C001
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C001
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C001
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2026-01-15
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End of preview. Expand in Data Studio

Solstice SaaS Growth Pack (Sample)

A dashboard-ready synthetic SaaS metrics dataset. Import the 6 CSVs straight into any BI tool and have a credible SaaS growth dashboard in under 10 minutes — no cleanup, no modeling.

Built by Solstice AI Studio as a free sample of a larger commercial pack. 100% synthetic — no real company, customer, or personal data.

What's in the box

File Rows Grain Purpose
companies.csv 6 company Master dimension — 6 synthetic startups spanning 6 distinct growth narratives
growth_metrics.csv 540 date × company Daily revenue, MRR, customer counts, CAC, LTV, churn
channel_performance.csv 3,780 date × company × channel Marketing channel impressions, clicks, conversions, cost, attribution
customer_segments.csv 18 company × segment SMB / Mid-Market / Enterprise unit economics
metric_definitions.csv 7 metric Self-documenting formulas
dashboard_suggestions.csv 8 chart 4 starter dashboards with suggested axes

Period: 90 days. Currency: USD. Dates: ISO-8601 (YYYY-MM-DD). Join key: company_id.

Growth narratives included

Each company embodies a distinct SaaS growth profile — so dashboards show realistic variance instead of random noise:

  • Steady PLG — strong SEO/content/referral, efficient long-term growth
  • Paid accelerator — aggressive paid acquisition, higher CAC
  • Enterprise lumpy — quarter-end deal spikes, lower churn
  • Seasonal B2C — demand seasonality and periodic swings
  • Churn recovery — visible churn event followed by stabilization
  • Capital infusion — growth acceleration after mid-period expansion

Why this dataset

Clean joins, zero cleanup. Stable IDs, one clear grain per table, no null-heavy columns, no ambiguous foreign keys. Import order: companies → growth_metrics → channel_performance → customer_segments.

Pre-calculated SaaS metrics. MRR, CAC, LTV, churn rate, conversion rate, CTR — all included, formulas documented in metric_definitions.csv. Users get to insight on first import.

Cross-table consistency. Daily channel conversions sum exactly to new_customers. Daily channel cost sums exactly to marketing_spend. Active customer counts respect prev + new − churned = active on every row.

Realistic magnitudes. Daily revenue reconciles to MRR over a month. ARR, LTV:CAC, and payback periods sit in credible SaaS ranges.

Use cases

  • Instant demo dashboards for BI / analytics tools
  • User onboarding & first-value experiences
  • SaaS metrics dashboard templates
  • Product showcase & sales enablement
  • Analytics workflow testing (imports, joins, filters)
  • Startup & growth analytics education
  • Customer success & retention analysis
  • Marketing performance & attribution analysis

Quick start

companies.csv           → dimension table
growth_metrics.csv      → primary fact (time × company)
channel_performance.csv → secondary fact (time × company × channel)
customer_segments.csv   → segment roll-up

Join key is company_id. All dates are ISO-8601. All currency is USD.

Suggested first dashboard: SaaS Growth Overview

  • Line chart: date × revenue, filter by company_name
  • Dual-axis line: date × (mrr, active_customers), filter by company_name

Full dashboard recipes in dashboard_suggestions.csv.

Load with pandas

import pandas as pd

companies = pd.read_csv("companies.csv")
growth = pd.read_csv("growth_metrics.csv", parse_dates=["date"])
channels = pd.read_csv("channel_performance.csv", parse_dates=["date"])
segments = pd.read_csv("customer_segments.csv")

# Monthly MRR per company
monthly_mrr = (
    growth.assign(month=growth["date"].dt.to_period("M"))
          .groupby(["company_name", "month"])["mrr"].mean()
          .reset_index()
)

Data quality checklist

  • All foreign keys resolve (0 orphans)
  • No nulls in required columns
  • No negative revenue, spend, or counts
  • Derived metrics reproduce from inputs (mrr, cac, ltv, churn_rate, conversion_rate, click_through_rate)
  • Continuity invariant holds: prev_active + new − churned = active on every row
  • impressions ≥ clicks ≥ conversions on every channel row

Schema

See SCHEMA.md for full column definitions, join model, metric formulas, and synthetic profile documentation.

License

Released under CC BY 4.0 — use freely for demos, research, internal tooling, education, and commercial templates. Attribution appreciated.

Synthetic data only — no real company, customer, or personal information.

Get the full pack

This repo is a 6-company, 90-day sample. The production pack scales to any company count (12 / 50 / 500+), any date range (1 quarter / 1 year / 3 years), any seed for reproducibility, custom growth-profile mixes, and custom industry / channel configurations.

Self-serve (Stripe checkout):

Full pack + enterprise scope:

  • www.solsticestudio.ai/datasets - per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.

Procurement catalog:

Citation

@dataset{solstice_saas_growth_pack_2026,
  title        = {Solstice SaaS Growth Pack (Sample)},
  author       = {Solstice AI Studio},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/solsticestudioai/saas-growth-pack}
}
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