3 Matching Annotations
  1. Nov 2020
    1. Once you have pageviews in your warehouse, you’ll need to do two thingsSessionization: Aggregate these pageviews into sessions (or “sessionization”) writing logic to identify gaps of 30 minutes or more.User stitching: If a user first visits your site without any identifying information (typically a `customer_id` or `email`), and then converts at a later date, their previous (anonymous) sessions should be updated to include their information. Your web tracking system should have a way to link these sessions together.This modeling is pretty complex, especially for companies with thousands of pageviews a day (thank goodness for incremental models 🙌). Fortunately, some very smart coworkers have written packages to do the heavy lifting for you, whether your page views are tracked with Snowplow, Segment or Heap. Leverage their work by installing the right package to transform the data for you.

      [[1. Gather your required data sources]] - once we have data, we need to do two things [[sessionization]] - the aggregation of pageviews / etc into a session

      and [[user stitching]] - when we have a user without any identifying information, and then converts - kind of like the anonymous users / signups - and trying to tie them back to a source

    2. So what do you actually need to build an attribution model?Raw data in your warehouse that represents customer interactions with your brand. For ecommerce companies, this is website visits. For B2B customers, it might be conversations with sales teams.SQL

      to build an [[attribution model]] we need the raw data - this raw data should capture the [[customer interactions]], and in our case - also partner interactions, or people working with the partner?

    3. Modeling marketing attribution Marketing attribution has long been one of the stickiest problems in analytics. But with raw data, SQL, and dbt a previously complex problem can become beautifully simple.

      [[marketing attribution]]