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saas-growth-pack

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Initial public release of Solstice SaaS Growth Pack (sample)

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  1. README.md +155 -0
  2. SCHEMA.md +163 -0
  3. channel_performance.csv +0 -0
  4. companies.csv +7 -0
  5. customer_segments.csv +19 -0
  6. dashboard_suggestions.csv +9 -0
  7. dataset-metadata.json +20 -0
  8. growth_metrics.csv +541 -0
  9. metric_definitions.csv +8 -0
README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - tabular-classification
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+ - tabular-regression
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+ - time-series-forecasting
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+ language:
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+ - en
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+ tags:
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+ - synthetic
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+ - saas
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+ - business-intelligence
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+ - analytics
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+ - dashboards
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+ - startup
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+ - growth
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+ - mrr
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+ - cac
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+ - ltv
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+ - churn
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+ - marketing
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+ - tabular
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+ pretty_name: Solstice SaaS Growth Pack
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+ size_categories:
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+ - 1K<n<10K
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+ ---
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+
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+ # Solstice SaaS Growth Pack (Sample)
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+
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+ **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.
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+
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+ Built by [Solstice EIM](https://solsticestudio.ai) as a free sample of a larger commercial pack. 100% synthetic — no real company, customer, or personal data.
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+
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+ ## What's in the box
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+
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+ | File | Rows | Grain | Purpose |
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+ |---|---|---|---|
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+ | `companies.csv` | 6 | company | Master dimension — 6 synthetic startups spanning 6 distinct growth narratives |
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+ | `growth_metrics.csv` | 540 | date × company | Daily revenue, MRR, customer counts, CAC, LTV, churn |
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+ | `channel_performance.csv` | 3,780 | date × company × channel | Marketing channel impressions, clicks, conversions, cost, attribution |
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+ | `customer_segments.csv` | 18 | company × segment | SMB / Mid-Market / Enterprise unit economics |
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+ | `metric_definitions.csv` | 7 | metric | Self-documenting formulas |
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+ | `dashboard_suggestions.csv` | 8 | chart | 4 starter dashboards with suggested axes |
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+
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+ **Period:** 90 days. **Currency:** USD. **Dates:** ISO-8601 (`YYYY-MM-DD`). **Join key:** `company_id`.
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+
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+ ## Growth narratives included
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+
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+ Each company embodies a distinct SaaS growth profile — so dashboards show realistic variance instead of random noise:
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+
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+ - **Steady PLG** — strong SEO/content/referral, efficient long-term growth
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+ - **Paid accelerator** — aggressive paid acquisition, higher CAC
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+ - **Enterprise lumpy** — quarter-end deal spikes, lower churn
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+ - **Seasonal B2C** — demand seasonality and periodic swings
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+ - **Churn recovery** — visible churn event followed by stabilization
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+ - **Capital infusion** — growth acceleration after mid-period expansion
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+
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+ ## Why this dataset
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+
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+ **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.
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+
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+ **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.
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+
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+ **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.
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+
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+ **Realistic magnitudes.** Daily revenue reconciles to MRR over a month. ARR, LTV:CAC, and payback periods sit in credible SaaS ranges.
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+
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+ ## Use cases
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+
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+ - Instant demo dashboards for BI / analytics tools
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+ - User onboarding & first-value experiences
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+ - SaaS metrics dashboard templates
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+ - Product showcase & sales enablement
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+ - Analytics workflow testing (imports, joins, filters)
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+ - Startup & growth analytics education
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+ - Customer success & retention analysis
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+ - Marketing performance & attribution analysis
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+
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+ ## Quick start
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+
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+ ```
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+ companies.csv → dimension table
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+ growth_metrics.csv → primary fact (time × company)
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+ channel_performance.csv → secondary fact (time × company × channel)
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+ customer_segments.csv → segment roll-up
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+ ```
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+
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+ Join key is `company_id`. All dates are ISO-8601. All currency is USD.
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+
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+ **Suggested first dashboard: SaaS Growth Overview**
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+ - Line chart: `date` × `revenue`, filter by `company_name`
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+ - Dual-axis line: `date` × (`mrr`, `active_customers`), filter by `company_name`
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+
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+ Full dashboard recipes in `dashboard_suggestions.csv`.
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+
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+ ### Load with pandas
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+
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+ ```python
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+ import pandas as pd
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+
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+ companies = pd.read_csv("companies.csv")
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+ growth = pd.read_csv("growth_metrics.csv", parse_dates=["date"])
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+ channels = pd.read_csv("channel_performance.csv", parse_dates=["date"])
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+ segments = pd.read_csv("customer_segments.csv")
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+
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+ # Monthly MRR per company
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+ monthly_mrr = (
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+ growth.assign(month=growth["date"].dt.to_period("M"))
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+ .groupby(["company_name", "month"])["mrr"].mean()
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+ .reset_index()
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+ )
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+ ```
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+
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+ ## Data quality checklist
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+
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+ - All foreign keys resolve (0 orphans)
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+ - No nulls in required columns
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+ - No negative revenue, spend, or counts
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+ - Derived metrics reproduce from inputs (mrr, cac, ltv, churn_rate, conversion_rate, click_through_rate)
120
+ - Continuity invariant holds: `prev_active + new − churned = active` on every row
121
+ - `impressions ≥ clicks ≥ conversions` on every channel row
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+
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+ ## Schema
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+
125
+ See `SCHEMA.md` for full column definitions, join model, metric formulas, and synthetic profile documentation.
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+
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+ ## License
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+
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+ Released under **CC BY 4.0** — use freely for demos, research, internal tooling, education, and commercial templates. Attribution appreciated.
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+
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+ Synthetic data only — no real company, customer, or personal information.
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+
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+ ## Full pack
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+
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+ This is a **6-company, 90-day sample**. The production pack scales to:
136
+
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+ - any company count (12 / 50 / 500+)
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+ - any date range (1 quarter / 1 year / 3 years)
139
+ - any seed for reproducibility
140
+ - custom growth profile mixes
141
+ - custom industry / channel configurations
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+
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+ Contact **[Solstice EIM](https://solsticestudio.ai)** for licensing the full pack, custom generation, or BI-vendor onboarding partnerships.
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+
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+ ## Citation
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+
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+ ```bibtex
148
+ @dataset{solstice_saas_growth_pack_2026,
149
+ title = {Solstice SaaS Growth Pack (Sample)},
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+ author = {Solstice EIM},
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+ year = {2026},
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+ publisher = {Hugging Face},
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+ url = {https://huggingface.co/datasets/justintelligence/saas-growth-pack}
154
+ }
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+ ```
SCHEMA.md ADDED
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+ # Solstice SaaS Growth Pack — Schema
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+
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+ ## Goal
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+ A dashboard-ready synthetic SaaS metrics pack. Imports cleanly into any BI tool and immediately supports SaaS growth, acquisition, and retention dashboards — no cleanup, no modeling.
5
+
6
+ ## Pack Contents
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+
8
+ ### `companies.csv`
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+ Grain: `company_id`
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+
11
+ | Column | Type | Description |
12
+ |---|---|---|
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+ | `company_id` | string | Stable primary key for each company |
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+ | `company_name` | string | Human-readable company name |
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+ | `industry` | string | Industry classification |
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+ | `growth_style` | string | Synthetic profile used to drive realistic trends |
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+ | `founded_date` | date | Company founding date |
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+ | `avg_revenue_per_customer` | decimal | Average monthly revenue per active customer |
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+ | `gross_margin_pct` | decimal | Gross margin percentage used in LTV estimates |
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+ | `initial_active_customers` | integer | Starting active customer base |
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+
22
+ ### `growth_metrics.csv`
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+ Grain: `date x company_id`
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+
25
+ | Column | Type | Description |
26
+ |---|---|---|
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+ | `date` | date | Observation date |
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+ | `company_id` | string | Foreign key to `companies.csv` |
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+ | `company_name` | string | Convenience label for charting |
30
+ | `revenue` | decimal | Estimated recognized revenue for the day (≈ MRR / 30.44 with small daily variation) |
31
+ | `mrr` | decimal | Monthly recurring revenue estimate |
32
+ | `new_customers` | integer | Customers acquired on the day |
33
+ | `churned_customers` | integer | Customers lost on the day |
34
+ | `active_customers` | integer | Active customer count at day end |
35
+ | `cac` | decimal | Customer acquisition cost |
36
+ | `ltv` | decimal | Customer lifetime value estimate |
37
+ | `marketing_spend` | decimal | Marketing spend for the day |
38
+ | `churn_rate` | decimal | Daily churn rate as a share of previous active customers |
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+
40
+ ### `channel_performance.csv`
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+ Grain: `date x company_id x channel`
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+
43
+ | Column | Type | Description |
44
+ |---|---|---|
45
+ | `date` | date | Observation date |
46
+ | `company_id` | string | Foreign key to `companies.csv` |
47
+ | `company_name` | string | Convenience label for charting |
48
+ | `channel` | string | Acquisition channel |
49
+ | `impressions` | integer | Channel impressions |
50
+ | `clicks` | integer | Channel clicks |
51
+ | `conversions` | integer | New customers attributed to the channel |
52
+ | `cost` | decimal | Daily channel spend |
53
+ | `revenue_generated` | decimal | Revenue attributed to channel conversions |
54
+ | `conversion_rate` | decimal | `conversions / clicks` |
55
+ | `click_through_rate` | decimal | `clicks / impressions` |
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+
57
+ ### `customer_segments.csv`
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+ Grain: `company_id x segment`
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+
60
+ | Column | Type | Description |
61
+ |---|---|---|
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+ | `company_id` | string | Foreign key to `companies.csv` |
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+ | `company_name` | string | Convenience label for charting |
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+ | `segment` | string | Customer segment (`SMB`, `Mid-Market`, `Enterprise`) |
65
+ | `avg_ltv` | decimal | Average LTV for the segment |
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+ | `avg_cac` | decimal | Average CAC for the segment |
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+ | `churn_rate` | decimal | Segment churn rate |
68
+ | `avg_revenue` | decimal | Average recurring revenue per customer in the segment |
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+
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+ ### `metric_definitions.csv`
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+ Grain: `metric_name`
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+
73
+ | Column | Type | Description |
74
+ |---|---|---|
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+ | `metric_name` | string | Name of metric |
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+ | `definition` | string | Human-readable definition |
77
+ | `formula` | string | Formula reference |
78
+ | `table_name` | string | Source table |
79
+ | `grain` | string | Grain where the metric is valid |
80
+
81
+ ### `dashboard_suggestions.csv`
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+ Grain: `dashboard_name x chart_name`
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+
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+ | Column | Type | Description |
85
+ |---|---|---|
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+ | `dashboard_name` | string | Suggested dashboard grouping |
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+ | `chart_name` | string | Suggested chart title |
88
+ | `chart_type` | string | Suggested visualization type |
89
+ | `primary_table` | string | Main source table |
90
+ | `x_axis` | string | Recommended x-axis field |
91
+ | `y_axis` | string | Recommended y-axis field(s) |
92
+ | `filter_suggestion` | string | Suggested dashboard filters |
93
+
94
+ ## Join Model
95
+
96
+ - `companies.company_id = growth_metrics.company_id`
97
+ - `companies.company_id = channel_performance.company_id`
98
+ - `companies.company_id = customer_segments.company_id`
99
+
100
+ The dataset is intentionally denormalized with `company_name` repeated in fact tables so dashboards can still work even if users only import one or two files.
101
+
102
+ ## Metric Definitions
103
+
104
+ ### `revenue`
105
+ - Formula: `(active_customers * avg_revenue_per_customer) / 30.44`
106
+ - Notes: Daily recognized revenue approximation. Summing a full month of `revenue` reconciles to `mrr` within ~5%.
107
+
108
+ ### `mrr`
109
+ - Formula: `active_customers * avg_revenue_per_customer`
110
+ - Notes: Included directly in `growth_metrics.csv`
111
+
112
+ ### `cac`
113
+ - Formula: `marketing_spend / new_customers`
114
+ - Notes: Protected from divide-by-zero by generator rules
115
+
116
+ ### `ltv`
117
+ - Formula: `(avg_revenue_per_customer * gross_margin_pct) / max(churn_rate, 0.01)`
118
+ - Notes: Daily churn rate is floored at 0.01 to avoid unstable LTV spikes on low-churn days.
119
+
120
+ ### `churn_rate`
121
+ - Formula: `churned_customers / previous_active_customers`
122
+
123
+ ### `conversion_rate`
124
+ - Formula: `conversions / clicks`
125
+
126
+ ### `click_through_rate`
127
+ - Formula: `clicks / impressions`
128
+
129
+ ## Synthetic Profiles
130
+
131
+ The generator uses multiple company profiles so the dashboards show realistic differences:
132
+
133
+ - `steady_plg`: strong SEO/content/referral, efficient long-term growth
134
+ - `paid_accelerator`: aggressive paid acquisition, higher spend and growth
135
+ - `enterprise_lumpy`: quarter-end deal spikes and lower churn
136
+ - `seasonal_b2c`: seasonal demand swings
137
+ - `churn_recovery`: visible churn event followed by recovery
138
+ - `capital_infusion`: growth acceleration after a mid-period expansion phase
139
+
140
+ ## Dashboard Recommendations
141
+
142
+ ### SaaS Growth Overview
143
+ - Revenue Over Time
144
+ - MRR and Active Customers
145
+
146
+ ### Acquisition Efficiency
147
+ - CAC vs LTV
148
+ - Channel Revenue Contribution
149
+
150
+ ### Customer Health
151
+ - New vs Churned Customers (Clustered Column)
152
+ - Churn Rate Over Time (Line)
153
+
154
+ ### Segment Economics
155
+ - Segment LTV/CAC (Grouped Bar)
156
+ - Segment Revenue Mix (Stacked Bar)
157
+
158
+ ## Import Notes
159
+
160
+ - All dates are ISO-8601 (`YYYY-MM-DD`)
161
+ - Currency values are USD
162
+ - IDs are stable and consistent
163
+ - No null-heavy cleanup is required before dashboarding
channel_performance.csv ADDED
The diff for this file is too large to render. See raw diff
 
companies.csv ADDED
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1
+ company_id,company_name,industry,growth_style,founded_date,avg_revenue_per_customer,gross_margin_pct,initial_active_customers
2
+ C001,AlphaFlow,Developer Tools,steady_plg,2025-01-15,461.00,0.77,80
3
+ C002,NorthOps,FinTech,paid_accelerator,2024-09-10,612.00,0.73,94
4
+ C003,BrightOps,Cybersecurity,enterprise_lumpy,2024-01-25,738.00,0.80,107
5
+ C004,ClearAnalytics,HealthTech,seasonal_b2c,2021-04-08,612.00,0.78,80
6
+ C005,VectorFlow,HR Tech,churn_recovery,2023-01-19,279.00,0.79,125
7
+ C006,SummitLoop,Vertical SaaS,capital_infusion,2022-03-05,307.00,0.90,169
customer_segments.csv ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ company_id,company_name,segment,avg_ltv,avg_cac,churn_rate,avg_revenue
2
+ C001,AlphaFlow,SMB,10821.95,255.67,0.0217,305.33
3
+ C001,AlphaFlow,Mid-Market,24281.48,328.51,0.0151,476.09
4
+ C001,AlphaFlow,Enterprise,82322.24,855.09,0.0088,1069.12
5
+ C002,NorthOps,SMB,10214.63,343.65,0.0266,372.40
6
+ C002,NorthOps,Mid-Market,19657.31,434.26,0.0222,598.76
7
+ C002,NorthOps,Enterprise,71699.88,1244.20,0.0144,1414.04
8
+ C003,BrightOps,SMB,29701.10,422.59,0.0131,488.08
9
+ C003,BrightOps,Mid-Market,49625.36,618.64,0.0113,698.25
10
+ C003,BrightOps,Enterprise,144869.60,1511.94,0.0068,1810.87
11
+ C004,ClearAnalytics,SMB,8478.91,314.09,0.0386,419.62
12
+ C004,ClearAnalytics,Mid-Market,15292.05,440.14,0.0289,566.18
13
+ C004,ClearAnalytics,Enterprise,57715.19,1304.15,0.0189,1400.88
14
+ C005,VectorFlow,SMB,3203.72,148.71,0.0434,175.97
15
+ C005,VectorFlow,Mid-Market,6870.07,200.52,0.0342,297.51
16
+ C005,VectorFlow,Enterprise,25060.19,521.09,0.0203,642.45
17
+ C006,SummitLoop,SMB,6442.98,163.77,0.0283,202.25
18
+ C006,SummitLoop,Mid-Market,11188.89,233.04,0.0236,293.21
19
+ C006,SummitLoop,Enterprise,54772.25,553.23,0.0113,688.57
dashboard_suggestions.csv ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ dashboard_name,chart_name,chart_type,primary_table,x_axis,y_axis,filter_suggestion
2
+ SaaS Growth Overview,Revenue Over Time,Line,growth_metrics,date,revenue,company_name
3
+ SaaS Growth Overview,MRR and Active Customers,Dual Axis Line,growth_metrics,date,"mrr, active_customers",company_name
4
+ Acquisition Efficiency,CAC vs LTV,Scatter,growth_metrics,cac,ltv,company_name
5
+ Acquisition Efficiency,Channel Revenue Contribution,Stacked Bar,channel_performance,date,revenue_generated,"channel, company_name"
6
+ Customer Health,New vs Churned Customers,Clustered Column,growth_metrics,date,"new_customers, churned_customers",company_name
7
+ Customer Health,Churn Rate Over Time,Line,growth_metrics,date,churn_rate,company_name
8
+ Segment Economics,Segment LTV/CAC,Grouped Bar,customer_segments,segment,"avg_ltv, avg_cac",company_name
9
+ Segment Economics,Segment Revenue Mix,Stacked Bar,customer_segments,company_name,avg_revenue,segment
dataset-metadata.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "title": "Solstice SaaS Growth Pack (Sample)",
3
+ "id": "justin_solstice/solstice-saas-growth-pack",
4
+ "licenses": [{"name": "CC-BY-4.0"}],
5
+ "keywords": [
6
+ "business",
7
+ "saas",
8
+ "synthetic",
9
+ "analytics",
10
+ "business intelligence",
11
+ "dashboards",
12
+ "startup",
13
+ "growth",
14
+ "marketing",
15
+ "churn",
16
+ "tabular"
17
+ ],
18
+ "collaborators": [],
19
+ "data": []
20
+ }
growth_metrics.csv ADDED
@@ -0,0 +1,541 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ date,company_id,company_name,revenue,mrr,new_customers,churned_customers,active_customers,cac,ltv,marketing_spend,churn_rate
2
+ 2026-01-01,C001,AlphaFlow,1224.25,37341.00,1,0,81,456.21,35497.00,456.21,0.0000
3
+ 2026-01-02,C001,AlphaFlow,1277.19,37802.00,1,0,82,551.25,35497.00,551.25,0.0000
4
+ 2026-01-03,C001,AlphaFlow,1244.38,37802.00,1,1,82,546.28,29095.90,546.28,0.0122
5
+ 2026-01-04,C001,AlphaFlow,1231.35,38263.00,1,0,83,455.53,35497.00,455.53,0.0000
6
+ 2026-01-05,C001,AlphaFlow,1261.22,38724.00,1,0,84,456.92,35497.00,456.92,0.0000
7
+ 2026-01-06,C001,AlphaFlow,1297.29,40568.00,4,0,88,452.49,35497.00,1809.96,0.0000
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+ 2026-01-17,C006,SummitLoop,2214.50,65391.00,5,0,213,381.32,27630.00,1906.62,0.0000
469
+ 2026-01-18,C006,SummitLoop,2218.53,66005.00,3,1,215,463.04,27630.00,1389.11,0.0047
470
+ 2026-01-19,C006,SummitLoop,2130.74,67233.00,5,1,219,461.91,27630.00,2309.54,0.0047
471
+ 2026-01-20,C006,SummitLoop,2199.78,68461.00,4,0,223,459.00,27630.00,1836.00,0.0000
472
+ 2026-01-21,C006,SummitLoop,2186.58,68768.00,3,2,224,420.54,27630.00,1261.62,0.0090
473
+ 2026-01-22,C006,SummitLoop,2316.26,68461.00,3,4,223,400.37,15435.75,1201.11,0.0179
474
+ 2026-01-23,C006,SummitLoop,2396.15,70610.00,7,0,230,455.05,27630.00,3185.33,0.0000
475
+ 2026-01-24,C006,SummitLoop,2427.63,71224.00,3,1,232,470.01,27630.00,1410.02,0.0043
476
+ 2026-01-25,C006,SummitLoop,2303.82,72759.00,5,0,237,403.72,27630.00,2018.61,0.0000
477
+ 2026-01-26,C006,SummitLoop,2405.53,74601.00,6,0,243,421.45,27630.00,2528.68,0.0000
478
+ 2026-01-27,C006,SummitLoop,2406.59,73987.00,2,4,241,464.19,16745.45,928.37,0.0165
479
+ 2026-01-28,C006,SummitLoop,2410.72,75215.00,4,0,245,423.48,27630.00,1693.92,0.0000
480
+ 2026-01-29,C006,SummitLoop,2537.61,75829.00,4,2,247,407.21,27630.00,1628.84,0.0082
481
+ 2026-01-30,C006,SummitLoop,2443.08,77057.00,4,0,251,443.97,27630.00,1775.87,0.0000
482
+ 2026-01-31,C006,SummitLoop,2602.60,77978.00,5,2,254,371.26,27630.00,1856.31,0.0080
483
+ 2026-02-01,C006,SummitLoop,2479.86,77978.00,1,1,254,450.91,27630.00,450.91,0.0039
484
+ 2026-02-02,C006,SummitLoop,2643.74,78285.00,4,3,255,383.09,23415.25,1532.35,0.0118
485
+ 2026-02-03,C006,SummitLoop,2726.38,80127.00,6,0,261,386.29,27630.00,2317.72,0.0000
486
+ 2026-02-04,C006,SummitLoop,2692.94,81048.00,4,1,264,447.96,27630.00,1791.85,0.0038
487
+ 2026-02-05,C006,SummitLoop,2696.48,82583.00,6,1,269,455.42,27630.00,2732.53,0.0038
488
+ 2026-02-06,C006,SummitLoop,2777.82,83811.00,4,0,273,460.90,27630.00,1843.62,0.0000
489
+ 2026-02-07,C006,SummitLoop,2721.94,84118.00,4,3,274,412.98,25118.18,1651.93,0.0110
490
+ 2026-02-08,C006,SummitLoop,2742.03,84118.00,1,1,274,475.15,27630.00,475.15,0.0036
491
+ 2026-02-09,C006,SummitLoop,2804.28,84425.00,5,4,275,477.29,18924.66,2386.45,0.0146
492
+ 2026-02-10,C006,SummitLoop,2955.97,85960.00,5,0,280,401.66,27630.00,2008.28,0.0000
493
+ 2026-02-11,C006,SummitLoop,2908.94,87495.00,5,0,285,438.30,27630.00,2191.51,0.0000
494
+ 2026-02-12,C006,SummitLoop,2974.01,88723.00,5,1,289,435.34,27630.00,2176.71,0.0035
495
+ 2026-02-13,C006,SummitLoop,2844.62,89951.00,4,0,293,445.98,27630.00,1783.91,0.0000
496
+ 2026-02-14,C006,SummitLoop,3067.17,90872.00,3,0,296,465.57,27630.00,1396.70,0.0000
497
+ 2026-02-15,C006,SummitLoop,3159.79,91793.00,5,2,299,384.50,27630.00,1922.49,0.0068
498
+ 2026-02-16,C006,SummitLoop,2953.83,93328.00,5,0,304,477.64,27630.00,2388.18,0.0000
499
+ 2026-02-17,C006,SummitLoop,3168.22,94249.00,6,3,307,444.49,27630.00,2666.94,0.0099
500
+ 2026-02-18,C006,SummitLoop,3098.98,95784.00,5,0,312,422.70,27630.00,2113.52,0.0000
501
+ 2026-02-19,C006,SummitLoop,3245.93,96091.00,3,2,313,460.90,27630.00,1382.69,0.0064
502
+ 2026-02-20,C006,SummitLoop,3221.14,97319.00,4,0,317,476.30,27630.00,1905.19,0.0000
503
+ 2026-02-21,C006,SummitLoop,3185.44,97933.00,3,1,319,408.47,27630.00,1225.42,0.0032
504
+ 2026-02-22,C006,SummitLoop,3170.29,98854.00,5,2,322,445.75,27630.00,2228.74,0.0063
505
+ 2026-02-23,C006,SummitLoop,3367.43,100389.00,5,0,327,397.16,27630.00,1985.80,0.0000
506
+ 2026-02-24,C006,SummitLoop,3324.23,101617.00,4,0,331,470.56,27630.00,1882.25,0.0000
507
+ 2026-02-25,C006,SummitLoop,3412.54,102231.00,4,2,333,436.91,27630.00,1747.65,0.0060
508
+ 2026-02-26,C006,SummitLoop,3545.14,103152.00,4,1,336,394.50,27630.00,1577.99,0.0030
509
+ 2026-02-27,C006,SummitLoop,3534.53,104687.00,5,0,341,377.37,27630.00,1886.86,0.0000
510
+ 2026-02-28,C006,SummitLoop,3341.21,104687.00,2,2,341,414.37,27630.00,828.73,0.0059
511
+ 2026-03-01,C006,SummitLoop,3434.15,104380.00,1,2,340,382.84,27630.00,382.84,0.0059
512
+ 2026-03-02,C006,SummitLoop,3396.59,104994.00,2,0,342,465.43,27630.00,930.86,0.0000
513
+ 2026-03-03,C006,SummitLoop,3605.64,105608.00,5,3,344,439.08,27630.00,2195.40,0.0088
514
+ 2026-03-04,C006,SummitLoop,3410.15,106529.00,3,0,347,460.21,27630.00,1380.62,0.0000
515
+ 2026-03-05,C006,SummitLoop,3650.89,107757.00,6,2,351,396.09,27630.00,2376.52,0.0058
516
+ 2026-03-06,C006,SummitLoop,3647.05,109292.00,7,2,356,430.03,27630.00,3010.21,0.0057
517
+ 2026-03-07,C006,SummitLoop,3763.22,109292.00,3,3,356,367.68,27630.00,1103.05,0.0084
518
+ 2026-03-08,C006,SummitLoop,3676.64,109599.00,2,1,357,410.38,27630.00,820.76,0.0028
519
+ 2026-03-09,C006,SummitLoop,3764.94,111134.00,5,0,362,467.80,27630.00,2339.01,0.0000
520
+ 2026-03-10,C006,SummitLoop,3743.51,112669.00,5,0,367,382.76,27630.00,1913.81,0.0000
521
+ 2026-03-11,C006,SummitLoop,3830.04,113590.00,4,1,370,469.72,27630.00,1878.89,0.0027
522
+ 2026-03-12,C006,SummitLoop,3764.65,113897.00,4,3,371,453.48,27630.00,1813.91,0.0081
523
+ 2026-03-13,C006,SummitLoop,3772.53,114511.00,3,1,373,471.37,27630.00,1414.12,0.0027
524
+ 2026-03-14,C006,SummitLoop,3912.53,114818.00,1,0,374,386.37,27630.00,386.37,0.0000
525
+ 2026-03-15,C006,SummitLoop,3683.92,115125.00,1,0,375,392.07,27630.00,392.07,0.0000
526
+ 2026-03-16,C006,SummitLoop,3746.83,114511.00,1,3,373,384.24,27630.00,384.24,0.0080
527
+ 2026-03-17,C006,SummitLoop,3836.07,115125.00,6,4,375,472.98,25822.43,2837.86,0.0107
528
+ 2026-03-18,C006,SummitLoop,3796.66,116046.00,3,0,378,460.62,27630.00,1381.86,0.0000
529
+ 2026-03-19,C006,SummitLoop,3878.85,116353.00,2,1,379,456.21,27630.00,912.43,0.0026
530
+ 2026-03-20,C006,SummitLoop,3972.49,116660.00,2,1,380,378.09,27630.00,756.18,0.0026
531
+ 2026-03-21,C006,SummitLoop,3891.97,118195.00,5,0,385,403.01,27630.00,2015.07,0.0000
532
+ 2026-03-22,C006,SummitLoop,4060.23,118502.00,4,3,386,372.54,27630.00,1490.14,0.0078
533
+ 2026-03-23,C006,SummitLoop,3914.65,119730.00,4,0,390,384.81,27630.00,1539.25,0.0000
534
+ 2026-03-24,C006,SummitLoop,4120.89,120958.00,5,1,394,383.46,27630.00,1917.29,0.0026
535
+ 2026-03-25,C006,SummitLoop,4122.90,122186.00,4,0,398,458.71,27630.00,1834.83,0.0000
536
+ 2026-03-26,C006,SummitLoop,4115.82,123107.00,5,2,401,439.17,27630.00,2195.87,0.0050
537
+ 2026-03-27,C006,SummitLoop,4132.36,123721.00,4,2,403,473.03,27630.00,1892.11,0.0050
538
+ 2026-03-28,C006,SummitLoop,4120.37,124335.00,3,1,405,370.71,27630.00,1112.14,0.0025
539
+ 2026-03-29,C006,SummitLoop,3986.20,125256.00,3,0,408,457.46,27630.00,1372.39,0.0000
540
+ 2026-03-30,C006,SummitLoop,4308.93,126484.00,5,1,412,456.80,27630.00,2284.01,0.0025
541
+ 2026-03-31,C006,SummitLoop,4071.03,127405.00,3,0,415,382.50,27630.00,1147.51,0.0000
metric_definitions.csv ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ metric_name,definition,formula,table_name,grain
2
+ revenue,"Estimated recognized revenue for the day. Approximately MRR divided by days per month, with small daily variation.",(active_customers * avg_revenue_per_customer) / 30.44,growth_metrics,date x company_id
3
+ mrr,Monthly recurring revenue estimate derived from active customers and average revenue per customer.,active_customers * avg_revenue_per_customer,growth_metrics,date x company_id
4
+ cac,Customer acquisition cost for the day.,marketing_spend / new_customers,growth_metrics,date x company_id
5
+ ltv,Customer lifetime value estimate using gross-margin-adjusted ARPU and daily churn. Churn rate is floored at 0.01 to prevent unstable spikes when daily churn is near zero.,"(avg_revenue_per_customer * gross_margin_pct) / max(churn_rate, 0.01)",growth_metrics,date x company_id
6
+ churn_rate,Daily churn as a share of previous day's active customers.,churned_customers / previous_active_customers,growth_metrics,date x company_id
7
+ conversion_rate,Share of clicks that converted by channel and day.,conversions / clicks,channel_performance,date x company_id x channel
8
+ click_through_rate,Share of impressions that became clicks by channel and day.,clicks / impressions,channel_performance,date x company_id x channel