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Implementing event analytics

This tutorial walks through how to transform raw event data into sessions. Many “out-of-box” web analytics solutions come already prepackaged with sessions, but they work as a “black box.” It doesn’t give the user either insight into or control how these sessions defined and work. With Cube SQL-based sessions data model, you’ll have full control over how these metrics are defined. It will give you great flexibility when designing sessions and events to your unique business use case. A few question we’ll answer with our sessions data model:
  • How do we measure session duration?
  • What is our bounce rate?
  • What areas of the app are most used?
  • Where are users spending most of their time?
  • How do we filter sessions where a user performs a specific action?
We’ll explore the subject using the data from Segment.com’s analytics.js library. The same concept could be applied for different data collection tools, such as Snowplow.

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What is a session?

A session is defined as a group of interactions one user takes within a given time frame on your app. Usually that time frame defaults to 30 minutes, meaning that whatever a user does on your app (e.g. browses pages, downloads resources, purchases products) before they leave equals one session.

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Unify events and page views into single cube

Segment stores page view data as a pages table and events data as a tracks table. For sessions we want to rely not only on page views data, but on events as well. Imagine you have a highly interactive app, a user loads a page and can stay on this page interacting with the website for while. Hence, you want to count events as part of the session as well. To do that we need to combine page view data and event data into a single cube. We’ll call the cube just events and assign a page views event type to pageview. Also, we’re going to assign a unique event_id to every event to use as primary key. 
cubes:
  - name: events
    sql: |
      SELECT
        t.id || '-e' as event_id
        , t.anonymous_id as anonymous_id
        , t.timestamp
        , t.event
        , t.context_page_path as page_path
        , NULL as referrer
      from javascript.tracks as t

      UNION ALL

      SELECT
        p.id as event_id
        , p.anonymous_id
        , p.timestamp
        , 'pageview' as event
        , p.context_page_path as page_path
        , p.referrer as referrer
      FROM javascript.pages as p
The above SQL creates base table for our events cube. Now we can add some measures to calculate the number of events and number of page views only, using a filter on event column. 
cubes:
  - name: events
    # ...

    measures:
      - name: count
        sql: event_id
        type: count

      - name: page_views_count
        sql: event_id
        type: count
        filters: [{ sql: "{CUBE}.event = 'pageview'" }]
Having this in place, we will already be able to calculate the total number of events and pageviews. Next, we’re going to add dimensions to be able to filter events in a specific time range and for specific types. 
cubes:
  - name: events
    # ...

    dimensions:
      - name: anonymous_id
        sql: anonymous_id
        type: number
        primary_key: true

      - name: event_id
        sql: event_id
        type: number
        primary_key: true

      - name: timestamp
        sql: timestamp
        type: time

      - name: event
        sql: event
        type: string
Now we have everything for Events cube and can move forward to grouping these events into sessions.

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Creating Sessions

As a recap, a session is defined as a group of interactions one user takes within a given time frame on your app. Usually that time frame defaults to 30 minutes. First, we’re going to use LAG() function in Redshift to determine an inactivity_time between events. 
select
  e.event_id AS event_id
  , e.anonymous_id AS anonymous_id
  , e.timestamp AS timestamp
  , DATEDIFF(minutes, LAG(e.timestamp) OVER(PARTITION BY e.anonymous_id ORDER BY e.timestamp), e.timestamp) AS inactivity_time
FROM events AS e
inactivity_time is the time in minutes between the current event and the previous. We’re going to use inactivity_time to terminate a session based on 30 minutes of inactivity. This window could be changed to any value, based on how users interact with your app. Now we’re ready to introduce our Sessions cube. 
cubes:
  - name: sessions
    sql: |
      SELECT
        ROW_NUMBER() OVER(PARTITION BY event.anonymous_id ORDER BY event.timestamp) || ' - '|| event.anonymous_id AS session_id
        , event.anonymous_id
        , event.timestamp AS session_start_at
        , ROW_NUMBER() OVER(PARTITION BY event.anonymous_id ORDER BY event.timestamp) AS session_sequence
        , LEAD(timestamp) OVER(PARTITION BY event.anonymous_id ORDER BY event.timestamp) AS next_session_start_at
      FROM (
        SELECT e.anonymous_id
          , e.timestamp
          , DATEDIFF(minutes
          , LAG(e.timestamp) OVER(PARTITION BY e.anonymous_id ORDER BY e.timestamp)
          , e.timestamp) AS inactivity_time
        FROM {events.sql()} AS e
      ) AS event
      WHERE (event.inactivity_time > 30 OR event.inactivity_time IS NULL)
As a primary key, we’re going to use session_id, which is the combination of the anonymous_id and the session sequence, since it’s guaranteed to be unique for each session. Having this in place, we can already count sessions and plot a time series chart of sessions. 
cubes:
  - name: sessions
    # ...

    measures:
      - name: count
        sql: session_id
        type: count

    dimensions:
      - name: anonymous_id
        sql: anonymous_id
        type: number
        primary_key: true

      - name: session_id
        sql: session_id
        type: number
        primary_key: true

      - name: start_at
        sql: session_start_at
        type: time

      - name: next_start_at
        sql: next_session_start_at
        type: time

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Connecting Events to Sessions

The next step is to identify the events contained within the session and the events ending the session. It’s required to get metrics such as session duration and events per session, or to identify sessions where specific events occurred (we’re going to use that for funnel analysis later on). We’re going to declare a join such that the events cube has a many_to_one relation to the sessions cube, and specify a condition, such as all users’ events from session start (inclusive) till the start of the next session (exclusive) belong to that session. 
cubes:
  - name: events
    # ...

    joins:
      - name: sessions
        relationship: many_to_one
        sql: |
          {events.anonymous_id} = {sessions.anonymous_id}
          AND {events.timestamp} >= {sessions.start_at}
          AND ({events.timestamp} < {sessions.next_start_at} or {sessions.next_start_at} is null)
To determine the end of the session, we’re going to use a subquery dimension. 
cubes:
  - name: events
    # ...

    measures:
      - name: last_event_timestamp
        sql: timestamp
        type: max
        public: false

  - name: sessions
    # ...

    dimensions:
      - name: end_raw
        sql: "{events.last_event_timestamp}"
        type: time
        sub_query: true
        public: false

      - name: end_at
        sql: |
          CASE WHEN {end_raw} + INTERVAL '1 minutes' > {CUBE}.next_session_start_at
            THEN {CUBE}.next_session_start_at
            ELSE {end_raw} + INTERVAL '30 minutes'
          END

      - name: duration_minutes
        sql: "datediff(minutes, {CUBE}.session_start_at, {end_at})"
        type: number
        
    measures:
      - name: average_duration_minutes
        sql: "{duration_minutes}"
        type: avg

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Mapping Sessions to Users

Right now all our sessions are anonymous, so the final step in our modeling would be to map sessions to users in case, they have signed up and have been assigned a user_id. Segment keeps track of such assignments in a table called identifies. Every time you identify a user with segment it will connect the current anonymous_id to the identified user id. We’re going to create an identifies cube, which will not contain any visible measures and dimensions for users to use in Insights, but instead will provide us with a user_id to use in the Sessions cube. Also, identifies could be used later on to join sessions to your users cube, which could be a cube built based on your internal database data for users. 
# Create a new file for the `identifies` cube with following content
cubes:
  - name: identifies
    sql: "SELECT distinct user_id, anonymous_id FROM javascript.identifies"

    dimensions:
      - name: id
        sql: "user_id || '-' || anonymous_id"
        type: string
        primary_key: true

      - name: anonymous_id
        sql: anonymous_id
        type: number

      - name: user_id
        sql: user_id
        type: number
        format: id
We need to declare a relationship between identifies and sessions, where session has a many_to_one relationship with identity. 
cubes:
  - name: sessions
    # ...

    joins:
      - name: identifies
        relationship: many_to_one
        sql: "{identifies.anonymous_id} = {sessions.anonymous_id}"
Once we have it, we can create a dimension user_id, which will be either a user_id from the identifies table or an anonymous_id in case we don’t have the identity of a visitor, which means that this visitor never signed in. 
cube("sessions", {
  // ...,

  dimensions: {
    user_id: {
      sql: `coalesce(${identifies.user_id}, ${CUBE}.anonymous_id)`,
      type: `string`
    }
  }
})
That was our final step in building a foundation for a sessions data model. Congratulations on making it here! Now we’re ready to add some advanced metrics on top of it.

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More metrics for Sessions

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Number of Events per Session

This one is super easy to add with a subquery dimension. We just calculate the number of events, which we already have as a measure in the events cube, as a dimension in the sessions cube. 
cubes:
  - name: sessions
    # ...

    dimensions:
      - name: number_events
        sql: "{events.count}"
        type: number
        sub_query: true

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Bounce Rate

we’ve just defined the number of events per session, we can easily add a dimension is_bounced to identify bounced sessions to the Sessions cube. Using this dimension, we can add two measures to the Sessions cube as well - a count of bounced sessions and a bounce rate. 
cubes:
  - name: sessions
    # ...

    dimensions:
      - name: is_bounced
        type: string
        case:
          when: [{ sql: "{number_events} = 1", label: "True" }]
          else: { label: "False" }

    measures:
      - name: bounced_count
        sql: session_id
        type: count
        filters:
          - - sql: "{is_bounced} = 'True'

      - name: bounce_rate
        sql: "100.00 * {bounced_count} / NULLIF({count}, 0)"
        type: number
        format: percent

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First Referrer

We already have this column in place in our base table. We’re just going to define a dimension on top of this. 
cubes:
  - name: sessions
    # ...

    measures:
      - name: first_referrer
        type: string
        sql: first_referrer

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Sessions New vs Returning

Same as for the first referrer. We already have a session_sequence field in the base table, which we can use for the is_first dimension. If session_sequence is 1 - then it belongs to the first session, otherwise - to a repeated session. 
cube("sessions", {
  // ...,

  dimensions: {
    is_first: {
      type: `string`,
      case: {
        when: [{ sql: `${CUBE}.session_sequence = 1`, label: `First` }],
        else: { label: `Repeat` }
      }
    }
  },

  measures: {
    repeat_count: {
      description: `Repeat Sessions Count`,
      sql: `session_id`,
      type: `count`,
      filters: [{ sql: `${is_first} = 'Repeat'` }]
    },

    repeat_percent: {
      description: `Percent of Repeat Sessions`,
      sql: `100.00 * ${repeat_count} / NULLIF(${count}, 0)`,
      type: `number`,
      format: `percent`
    }
  }
})
Define a dimension form_submitted_count on the Sessions using sub_query. 
cubes:
  - name: sessions
    # ...

    # Add this dimension to the `sessions` cube
    dimensions:
      - name: form_submitted_count
        sql: "{events.form_submitted_count}"
        type: number
        sub_query: true
Create a measure to count only sessions where form_submitted_count is greater than 0. 
cubes:
  - name: sessions
    # ...

    # Add this measure to the `sessions` cube
    measures:
      - name: with_form_submitted_count
        sql: session_id
        type: count
        filters: [{ sql: "{form_submitted_count} > 0" }]
Now we can use the with_form_submitted_count measure to get only sessions when the form_submitted event occurred.