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 (opens in a new tab)’s analytics.js library. The same concept could be applied for different data collection tools, such as Snowplow (opens in a new tab).

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.

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.

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 (opens in a new tab) 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

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

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.

cubes:  - name: sessions  # ...    dimensions:  - name: user_id  sql: "coalesce({identifies.user_id}, {CUBE}.anonymous_id)"  type: string   </CodeTabs>   Based on the just-created dimension, we can add two new metrics: the count of users and the average sessions per user.   <CodeTabs>   ```javascript cube("sessions", {  // ...,    measures: {  users_count: {  sql: `${user_id}`,  type: `count_distinct`  },    average_sessions_per_user: {  sql: `${count}::NUMERIC / NULLIF(${users_count}, 0)`,  type: `number`  }  } })

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.

More metrics for Sessions

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

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

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

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.

cubes:  - name: sessions  # ...    dimensions:   - name: is_first  type: string  case:  when: [{ sql: "{CUBE}.session_sequence = 1", label: "First" }]  else: { label: "Repeat" }    measures:  - name: repeat_count  description: Repeat Sessions Count  sql: session_id  type: count  filters: [{ sql: "{is_first} = 'Repeat'" }]    - name: repeat_percent  description: Percent of Repeat Sessions  sql: "100.00 * {repeat_count} / NULLIF({count}, 0)"  type: number  format: percent   </CodeTabs>   ### Filter Sessions, where user performs specific event   Often, you want to select specific sessions where a user performed some important action. In the example below, we’ll filter out sessions where the `form_submitted` event happened. To do that, we need to follow 3 steps:   Define a measure on the Events cube to count only `form_submitted` events.   <CodeTabs>   ```javascript cube("events", {  // ...,    // Add this measure to the `events` cube  measures: {  form_submitted_count: {  sql: `event_id`,  type: `count`,  filters: [{ sql: `${CUBE}.event = 'form_submitted'` }]  }  } })

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.