Clear History

This accelerator will show you how to perform attribution modeling on your Snowplow data, enabling you to attribute a portion of the value of a conversion to specific channels based on the conversion pathway. With this information, you can calculate the revenue per channel and Return on Advertising Spend (ROAS*) for each marketing channel, giving you a data-driven approach to making strategic marketing decisions such as channel investment and focus.

We will use the term channels throughout this Accelerator, however you are free to define these however you like and can include other things, e.g. specific campaigns.

Here you will learn to:

  • Model and Visualize Snowplow data
    • using the snowplow_fractribution dbt package and Python script
    • using our sample data (no need to have a working pipeline)
  • Set-up Snowplow Tracking and Enrichment to prepare your data sources
  • Apply what you have learned on your own pipeline to gain insights

Who is this guide for?

  • Data practitioners who would like to get familiar with further options for modeling Snowplow data.
  • Data practitioners who want to learn how to use the snowplow_fractribution dbt package, to gain insight into ROAS figures using attribution modeling as quickly as possible.
  • This accelerator currently supports Snowflake, BigQuery, Redshift and Databricks

What you will learn

In an estimated minimum of half a day (~5.5 hours) you can achieve the following:

  • Upload - Upload some sample data
  • Model - Configure and run the snowplow-fractribution data model
  • Visualize - Have a closer look at the Attribution Modeling report table to better understand the outcome of the analysis.
  • Track - Set-up and deploy tracking needed for your website or single page application for being able to perform attribution modeling
  • Enrich - Add an enrichment to your data
  • Next steps - Gain value from your own pipeline data
gantt dateFormat HH-mm axisFormat %H:%M section 1. Upload 30min :upload, 00-00, 30m section 2. Model 2h :model, after upload, 2h section 3. Visualize 10min :visualize, after model, 10m section 4. Track 1h :track, after visualize, 1h section 5. Enrich 1h :enrich, after track, 1h section 6. Next steps 1h :next steps, after enrich, 1h

You don’t necessarily need to follow all the steps in order. You could choose to skip steps 1-3 where you would learn how to perform Attribution Modeling through the sample data. If you have your own website or single page application, you could follow steps 4-6 to run the analysis on your own data right away and go through the first sections afterwards.

Prerequisites

  • It is assumed that you are already familiar with the snowplow-web dbt package, or have a snowplow_web_page_views and Snowplow events table already in your data warehouse. If not, we recommend completing the Advanced Analytics for Web accelerator.
  • It is preferable to be familiar with the snowplow-ecommerce dbt package, that you can use to process your transaction events which are needed for the conversion source for the snowplow-fractribution package to work.

Modeling

  • dbt CLI installed or dbt Cloud account available
    • New dbt project created and configured
  • Python 3 Installed
  • Snowflake, Databricks, Redshift or BigQuery account and a user with access to create schemas and tables

Tracking and Enrichment

  • Snowplow pipeline
  • Web app to implement tracking

Snowplow Attribution Modeling is closely based on Google’s Fractional Attribution - coined Fractribution. If you would like to learn more about how it works, please see these slides and this document

*ROAS: The amount of revenue that is earned for every dollar spent on advertising