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license: cc-by-4.0

task_categories:

tabular-classification
tabular-regression
time-series-forecasting

language:

tags:

synthetic
saas
business-intelligence
analytics
dashboards
startup
growth
mrr
cac
ltv
churn
marketing
tabular

pretty_name: Solstice SaaS Growth Pack

size_categories:

1K<n<10K

configs:

config_name: companies

data_files:
- split: train
  
  path: companies.csv
config_name: growth_metrics

data_files:
- split: train
  
  path: growth_metrics.csv
config_name: channel_performance

data_files:
- split: train
  
  path: channel_performance.csv
config_name: customer_segments

data_files:
- split: train
  
  path: customer_segments.csv
config_name: metric_definitions

data_files:
- split: train
  
  path: metric_definitions.csv
config_name: dashboard_suggestions

data_files:
- split: train
  
  path: dashboard_suggestions.csv

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

|---|---|---|---|

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):

Sample Scale tier — $5,000 — ~25K records, one subject, 72-hour delivery.

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:

SolsticeAI Data Storefront — available via Datarade / Monda.

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}


}