Semantic layer implementation of the quantitative PMF framework from Tribe Capital: Growth Accounting × Cohort Analysis × Distribution

Implemented as a dbt package, without dependency on dbt Cloud or any paid features.

# 1. Install dbt for your warehouse
pip install dbt-postgres      # or dbt-bigquery, dbt-snowflake, dbt-duckdb …

# 2. Configure your warehouse connection
#    Edit ~/.dbt/profiles.yml  (see profiles.yml.example in this repo)

# 3. Point the package at your tables
#    Edit dbt_project.yml → vars.metrics → source_table for each metric

# 4. Run
dbt run

dbt writes the output tables to your warehouse.

pmf_dbt/
├── dbt_project.yml          # metric definitions live here (vars.metrics)
├── macros/
│   ├── pmf_growth_accounting.sql   # core logic — shared across all metrics
│   ├── pmf_cohort.sql
│   └── pmf_distribution.sql
└── models/pmf/
    ├── gmv__growth_accounting__monthly.sql   # one-liner: calls the macro
    ├── gmv__cohort__monthly.sql
    ├── gmv__distribution__monthly.sql
    ├── gmv__growth_accounting__weekly.sql
    ├── gmv__cohort__weekly.sql
    ├── mau__growth_accounting__monthly.sql
    ├── mau__cohort__monthly.sql
    └── mau__distribution__monthly.sql

The macros contain all the logic. The model files are one line each. You never need to edit the macros unless you want to extend the framework.

Step 1 — Add the metric definition to dbt_project.yml under vars.metrics:

vars:
  metrics:
    revenue:                              # your metric name
      source_table: "your_schema.payments"
      entity_id:    "merchant_id"         # the "who"
      time_column:  "paid_at"
      value_expr:   "SUM(net_amount)"
      is_binary:    false                 # false = ordinal (has expansion/contraction)
      time_grains:  ["monthly", "weekly"]
      row_filter:   "status = 'settled'"  # optional

Step 2 — Create model files (one per framework × grain):

# models/pmf/revenue__growth_accounting__monthly.sql
{{ pmf_growth_accounting('revenue', var('metrics')['revenue'], 'monthly') }}

# models/pmf/revenue__cohort__monthly.sql
{{ pmf_cohort('revenue', var('metrics')['revenue'], 'monthly') }}

# models/pmf/revenue__distribution__monthly.sql
{{ pmf_distribution('revenue', var('metrics')['revenue'], 'monthly') }}

Step 3 — Run:

dbt run --select revenue__*

Ordinal metrics: CDF — entity_value, pct_entities_at_or_below, pct_value_at_or_below

Binary metrics: L-N — intensity_value (days active), pct_of_active_entities, cumulative_pct

All tables share the same schema. Union them for cross-metric dashboards:

select * from gmv__growth_accounting__monthly
union all
select * from mau__growth_accounting__monthly
order by metric_name, period