{
  "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)."
        }
      ]
    }
  ]
}