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dbt Product Analytics

A dbt package for doing product analytics.

Currently supports event streams and funnel analysis. More features will be added soon!

Installation

Add the following to your packages.yml:

- package: mjirv/dbt_product_analytics
  version: [">=0.1.0"]

Usage

dbt Product Analytics provides four macros: event_stream(), funnel(), retention(), and flows().

Use them in models and analyses like any other dbt macro.

event_stream() (source)

Transforms any time series model with a user ID and event labels into a standardized event stream format.

< 8000 div class="markdown-heading" dir="auto">

Usage

{{ dbt_product_analytics.event_stream(
    from=ref('orders'),
    event_type_col="status",
    user_id_col="customer_id",
    date_col="order_date",
    start_date="2018-01-01",
    end_date="2019-01-01") }}

funnel() (source)

Runs a funnel analysis, i.e. tells you how many users performed step 1 followed by step 2 followed by step 3 etc.

Usage

Example:
{% set events =
  dbt_product_analytics.event_stream(
    from=ref('orders'),
    event_type_col="status",
    user_id_col="customer_id",
    date_col="order_date",
    start_date="2018-01-01",
    end_date="2019-01-01")
%}

{% set steps = ["placed", "completed", "returned" ] %}

{{ dbt_product_analytics.funnel(steps=steps, event_stream=events) }}
-- or materialize your event stream and use:
-- {{ dbt_product_analytics.funnel(steps=steps, event_stream=ref('order_events')) }}
Output:
michael=# select * from dbt_product_analytics.funnel_orders ;
event_type  unique_users  pct_conversion      pct_of_previous  
----------  ------------  ------------------  -----------------
placed      15            1.0                                  
completed   2             0.133333333333333   0.133333333333333
returned    1             0.0666666666666667  0.5
Advanced:

Two optional parameters are available: start_date, and end_date.

  • start_date: Filters your event stream for only events on or after this date
  • end_date: Filters your event stream for only events before this date

retention() (source)

Runs a retention analysis, i.e. tells you how many people who did first_action came back to do second_action on or after 1, 7, 14 days (or weeks, months, years), etc.

Usage

Example:
{{ dbt_product_analytics.retention(
  event_stream=ref('order_events'),
  first_action='completed',
  second_action='completed',
)}}
Output:
michael=# select * from dbt_product_analytics.retention_orders ;
period  unique_users  pct_users        
------  ------------  -----------------
1       43            0.693548387096774
7       41            0.661290322580645
14      37            0.596774193548387
30      28            0.451612903225806
60      2             0.032258064516129
Advanced:

Five optional parameters are available: periods, period_type, group_by, start_date, and end_date.

  • period: The period windows you want look at (defaults to [1, 7, 14, 30, 60, 120])
  • period_type: The date type you want to use (defaults to day)
  • group_by: A column from your event stream that you want to group by (defaults to null)
  • start_date: Filters your event stream for only events on or after this date
  • end_date: Filters your event stream for only events before this date

flows() (source)

Runs a flow analysis, i.e. shows you common paths users take before or after a given event

Usage

Example:
{{
  dbt_product_analytics.flows(
    event_stream=events,
    primary_event='placed'
  )
}}
Output:
michael=# select * from dbt_product_analytics.flows_orders ;
event_0  event_1    event_2   event_3  event_4  event_5  n_events
-------  ---------  --------  -------  -------  -------  --------
placed                                                   13      
placed   completed                                       1       
placed   completed  returned                             1
Advanced:

Five optional parameters are available: n_events_from, before_or_after, top_n, start_date, and end_date.

  • n_events_from: The number of events to include in the flows (defaults to 5)
  • before_or_after: Whether to look at the events following your primary_action or the ones leading up to it (defaults to 'after')
  • top_n: How many flows to include (defaults to displaying the top 20)
  • start_date: Filters your event stream for only events on or after this date
  • end_date: Filters your event stream for only events before this date

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A dbt package for doing product analytics

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0.3.1: Bugfix: BigQuery window functions Latest
Oct 26, 2022
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