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"title": "Solstice SaaS Growth Pack (Sample)",
"id": "justinsolstice/solstice-saas-growth-pack",
"subtitle": "Dashboard-ready synthetic SaaS metrics - MRR, CAC, LTV, churn, ready in 10 min",
"description": "# Solstice SaaS Growth Pack (Sample)\n\n**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.\n\nBuilt by [Solstice AI Studio](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic - no real company, customer, or personal data.\n\n## What is in the box\n\n| File | Rows | Grain | Purpose |\n|---|---|---|---|\n| companies.csv | 6 | company | Master dimension - 6 synthetic startups spanning 6 distinct growth narratives |\n| growth_metrics.csv | 540 | date x company | Daily revenue, MRR, customer counts, CAC, LTV, churn |\n| channel_performance.csv | 3,780 | date x company x channel | Marketing channel impressions, clicks, conversions, cost, attribution |\n| customer_segments.csv | 18 | company x segment | SMB / Mid-Market / Enterprise unit economics |\n| metric_definitions.csv | 7 | metric | Self-documenting formulas |\n| dashboard_suggestions.csv | 8 | chart | 4 starter dashboards with suggested axes |\n\n**Period:** 90 days. **Currency:** USD. **Dates:** ISO-8601 (YYYY-MM-DD). **Join key:** company_id.\n\n## Growth narratives included\n\nEach company embodies a distinct SaaS growth profile - so dashboards show realistic variance instead of random noise:\n\n- **Steady PLG** - strong SEO/content/referral, efficient long-term growth\n- **Paid accelerator** - aggressive paid acquisition, higher CAC\n- **Enterprise lumpy** - quarter-end deal spikes, lower churn\n- **Seasonal B2C** - demand seasonality and periodic swings\n- **Churn recovery** - visible churn event followed by stabilization\n- **Capital infusion** - growth acceleration after mid-period expansion\n\n## Why this dataset\n\n**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.\n\n**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.\n\n**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.\n\n**Realistic magnitudes.** Daily revenue reconciles to MRR over a month. ARR, LTV:CAC, and payback periods sit in credible SaaS ranges.\n\n## Use cases\n\n- Instant demo dashboards for BI / analytics tools\n- User onboarding and first-value experiences\n- SaaS metrics dashboard templates\n- Product showcase and sales enablement\n- Analytics workflow testing (imports, joins, filters)\n- Startup and growth analytics education\n- Customer success and retention analysis\n- Marketing performance and attribution analysis\n\n## Quick start\n\n```\ncompanies.csv -> dimension table\ngrowth_metrics.csv -> primary fact (time x company)\nchannel_performance.csv -> secondary fact (time x company x channel)\ncustomer_segments.csv -> segment roll-up\n```\n\nJoin key is company_id. All dates are ISO-8601. All currency is USD.\n\n**Suggested first dashboard: SaaS Growth Overview**\n- Line chart: date x revenue, filter by company_name\n- Dual-axis line: date x (mrr, active_customers), filter by company_name\n\nFull dashboard recipes in dashboard_suggestions.csv.\n\n### Load with pandas\n\n```python\nimport pandas as pd\n\ncompanies = pd.read_csv(\"companies.csv\")\ngrowth = pd.read_csv(\"growth_metrics.csv\", parse_dates=[\"date\"])\nchannels = pd.read_csv(\"channel_performance.csv\", parse_dates=[\"date\"])\nsegments = pd.read_csv(\"customer_segments.csv\")\n\n# Monthly MRR per company\nmonthly_mrr = (\n growth.assign(month=growth[\"date\"].dt.to_period(\"M\"))\n .groupby([\"company_name\", \"month\"])[\"mrr\"].mean()\n .reset_index()\n)\n```\n\n## Data quality checklist\n\n- All foreign keys resolve (0 orphans)\n- No nulls in required columns\n- No negative revenue, spend, or counts\n- Derived metrics reproduce from inputs (mrr, cac, ltv, churn_rate, conversion_rate, click_through_rate)\n- Continuity invariant holds: prev_active + new - churned = active on every row\n- impressions >= clicks >= conversions on every channel row\n\n## License\n\nReleased under **CC BY 4.0** - use freely for demos, research, internal tooling, education, and commercial templates. Attribution appreciated.\n\nSynthetic data only - no real company, customer, or personal information.\n\n## Get the full pack\n\nThis 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.\n\n**Self-serve (Stripe checkout):**\n- [**Sample Scale tier - $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) - ~25K records, one subject, 72-hour delivery.\n\n**Full pack + enterprise scope:**\n- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) - per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.\n\n**Procurement catalog:**\n- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) - available via Datarade / Monda.\n\n## Citation\n\n```\n@dataset{solstice_saas_growth_pack_2026,\n title = {Solstice SaaS Growth Pack (Sample)},\n author = {Solstice AI Studio},\n year = {2026},\n publisher = {Kaggle},\n url = {https://www.kaggle.com/datasets/justinsolstice/solstice-saas-growth-pack}\n}\n```\n",
"licenses": [
{
"name": "CC BY 4.0"
}
],
"keywords": [
"business",
"marketing",
"tabular",
"finance"
],
"collaborators": [],
"isPrivate": false,
"expectedUpdateFrequency": "never",
"userSpecifiedSources": "Generated synthetically by Solstice AI Studio (solsticestudio.ai) using a proprietary SaaS growth simulation engine. 100% synthetic - no real company, customer, or personal information. Released as a free sample of a larger commercial pack.",
"image": "cover.png",
"data": [
{
"name": "companies.csv",
"description": "Master company dimension. 6 synthetic startups each embodying a distinct growth narrative (steady PLG, paid accelerator, enterprise lumpy, seasonal B2C, churn recovery, capital infusion). Foreign key: company_id.",
"totalBytes": 549,
"columns": [
{
"name": "company_id",
"description": "Stable primary key for the company (e.g. CO-0001). Foreign key target for all other tables."
},
{
"name": "company_name",
"description": "Human-readable company name (synthetic)."
},
{
"name": "industry",
"description": "Industry label for the synthetic company (e.g. Collaboration SaaS, FinTech SaaS)."
},
{
"name": "growth_style",
"description": "One of 6 growth narratives: steady_plg, paid_accelerator, enterprise_lumpy, seasonal_b2c, churn_recovery, capital_infusion."
},
{
"name": "founded_date",
"description": "Founding date (ISO-8601, YYYY-MM-DD)."
},
{
"name": "avg_revenue_per_customer",
"description": "Average monthly revenue per active customer in USD."
},
{
"name": "gross_margin_pct",
"description": "Gross margin as a percentage (e.g. 0.78 = 78%)."
},
{
"name": "initial_active_customers",
"description": "Active customer count at the start of the simulation window."
}
]
},
{
"name": "growth_metrics.csv",
"description": "Primary fact table. Daily revenue, MRR, customer counts, CAC, LTV, and churn per company across 90 days. Grain: date x company. 540 rows (6 companies x 90 days).",
"totalBytes": 45436,
"columns": [
{
"name": "date",
"description": "Daily timestamp (ISO-8601, YYYY-MM-DD)."
},
{
"name": "company_id",
"description": "Foreign key to companies.company_id."
},
{
"name": "company_name",
"description": "Denormalized company name for quick grouping in BI tools."
},
{
"name": "revenue",
"description": "Daily revenue in USD."
},
{
"name": "mrr",
"description": "Monthly Recurring Revenue in USD (rolling). Reconciles with daily revenue over a month."
},
{
"name": "new_customers",
"description": "Count of customers acquired this day. Sum across channels equals channel_performance.conversions for the same date+company."
},
{
"name": "churned_customers",
"description": "Count of customers lost this day."
},
{
"name": "active_customers",
"description": "Customers active at end of day. Respects prev_active + new - churned = active."
},
{
"name": "cac",
"description": "Customer Acquisition Cost in USD (marketing_spend / new_customers)."
},
{
"name": "ltv",
"description": "Customer Lifetime Value in USD (avg_revenue_per_customer * gross_margin / churn_rate)."
},
{
"name": "marketing_spend",
"description": "Total marketing spend in USD. Equals sum of channel_performance.cost for the same date+company."
},
{
"name": "churn_rate",
"description": "Daily churn rate (churned_customers / prev_active)."
}
]
},
{
"name": "channel_performance.csv",
"description": "Marketing channel performance at daily grain. Impressions, clicks, conversions, cost, and attributed revenue per channel per company per day. Channel totals reconcile exactly to growth_metrics (conversions = new_customers, cost = marketing_spend). Grain: date x company x channel. 3,780 rows.",
"totalBytes": 278512,
"columns": [
{
"name": "date",
"description": "Daily timestamp (ISO-8601, YYYY-MM-DD)."
},
{
"name": "company_id",
"description": "Foreign key to companies.company_id."
},
{
"name": "company_name",
"description": "Denormalized company name for quick grouping."
},
{
"name": "channel",
"description": "Marketing channel: paid_search, social, content, email, referral, direct, affiliate."
},
{
"name": "impressions",
"description": "Ad or content impressions delivered (impressions >= clicks invariant)."
},
{
"name": "clicks",
"description": "Clicks generated (clicks >= conversions invariant)."
},
{
"name": "conversions",
"description": "New customers attributed to this channel. Sums to growth_metrics.new_customers per date+company."
},
{
"name": "cost",
"description": "Marketing spend in USD for this channel. Sums to growth_metrics.marketing_spend per date+company."
},
{
"name": "revenue_generated",
"description": "Revenue attributed to this channel in USD."
},
{
"name": "conversion_rate",
"description": "Conversions / clicks."
},
{
"name": "click_through_rate",
"description": "Clicks / impressions."
}
]
},
{
"name": "customer_segments.csv",
"description": "Segment-level unit economics. Average LTV, CAC, churn rate, and revenue per segment (SMB, Mid-Market, Enterprise) per company. 18 rows (6 companies x 3 segments).",
"totalBytes": 1078,
"columns": [
{
"name": "company_id",
"description": "Foreign key to companies.company_id."
},
{
"name": "company_name",
"description": "Denormalized company name."
},
{
"name": "segment",
"description": "Customer segment: SMB, Mid-Market, or Enterprise."
},
{
"name": "avg_ltv",
"description": "Average customer lifetime value for this segment (USD)."
},
{
"name": "avg_cac",
"description": "Average customer acquisition cost for this segment (USD)."
},
{
"name": "churn_rate",
"description": "Segment-level monthly churn rate."
},
{
"name": "avg_revenue",
"description": "Average monthly revenue per customer in this segment (USD)."
}
]
},
{
"name": "metric_definitions.csv",
"description": "Self-documenting metric catalog. Each row is one SaaS metric with definition, formula, source table, and grain. 7 metrics.",
"totalBytes": 1269,
"columns": [
{
"name": "metric_name",
"description": "Canonical metric name (e.g. mrr, cac, ltv, churn_rate)."
},
{
"name": "definition",
"description": "Plain-English definition of the metric."
},
{
"name": "formula",
"description": "Mathematical formula or computation used to derive the metric."
},
{
"name": "table_name",
"description": "Which CSV file the metric appears in."
},
{
"name": "grain",
"description": "Row grain of the metric (e.g. date x company, company x segment)."
}
]
},
{
"name": "dashboard_suggestions.csv",
"description": "Starter dashboard recipes. 8 charts spanning 4 dashboard themes with suggested chart type, x/y axes, and filters. Paste straight into any BI tool.",
"totalBytes": 921,
"columns": [
{
"name": "dashboard_name",
"description": "Named dashboard recipe (e.g. SaaS Growth Overview, Marketing Attribution)."
},
{
"name": "chart_name",
"description": "Short chart title."
},
{
"name": "chart_type",
"description": "Chart visualization type (line, bar, dual_axis_line, stacked_bar, etc)."
},
{
"name": "primary_table",
"description": "Main CSV this chart pulls from."
},
{
"name": "x_axis",
"description": "Suggested x-axis column."
},
{
"name": "y_axis",
"description": "Suggested y-axis column(s) - comma-separated when dual-axis."
},
{
"name": "filter_suggestion",
"description": "Recommended filter (usually company_name for per-company dashboards)."
}
]
}
]
} |