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Single customer view

Introduction​

Developing a single customer view and effectively identifying users is an important part of delivering excellent user experience on digital platforms. The key steps in developing a single customer view are:

  • Capturing first-party user identifiers across all your platforms and products.
  • Developing an understanding of the relationships between those user identifiers (this generally means developing a hierarchy of identifiers and a mapping table).
  • Using these relationships to link together all events from a single user.

This process is often referred to as user stitching. For example, user stitching can involve connecting events from users before and after they log in (or identify in some other way), or mapping together the same user across different devices.

This recipe will focus on exploring an example user stitching table based on Snowplow's various web user identifiers.

What you'll be doing​

You have already set up Snowplow’s out of the box web tracking by instrumenting the JavaScript Tracker in your application. This includes tracking page_view and page_ping events.

With all web events the Snowplow JavaScript tracker captures the following user identifiers automatically:

domain_useridclient side cookie ID set against the domain the tracking is on
network_useridserver side cookie ID set against the collector domain
user_ipaddressthe user’s IP address

Additionally, Snowplow allows you to specify a custom user ID, which you'll be adding in this recipe. You'll then build a user stitching table to explore how you can reliably identify users over time.

Implement a custom user ID​

Adding a custom user ID with the Snowplow JavaScript Tracker is easy. You'll simply add this line to your out of the box tracking:

window.snowplow('setUserId', "example_user_id");

If you are using Google Tag Manager, you can add the variable like so:

window.snowplow('setUserId', "{{example_user_id_variable}}");

Make sure you add this method before you start tracking events, i.e.

window.snowplow('setUserId', "example_user_id");
window.snowplow('enableActivityTracking', { minimumVisitLength: 10, heartbeatDelay: 10 });
window.snowplow('enableLinkClickTracking');
window.snowplow('trackPageView');
window.snowplow('enableFormTracking');

Modeling the data you've collected​

What does the model do?​

Capturing as many user identifiers as possible is only the first step towards having a single customer view over time. You’ll also need to stitch those identifiers together across events. The following SQL query creates a user stitching table.

First generate the table:​

CREATE TABLE derived.user_stitching AS(

    SELECT

        -- custom user ID
        LAST_VALUE(ev.user_id) OVER(PARTITION BY ev.domain_userid ORDER BY ev.derived_tstamp) as user_id,

        -- Snowplow cookie IDs
        ev.domain_userid,
        LAST_VALUE(ev.network_userid) OVER(PARTITION BY ev.domain_userid ORDER BY ev.derived_tstamp) AS network_userid,

        -- time the user was last active
        MIN(ev.derived_tstamp) AS first_active,
        MAX(ev.derived_tstamp) AS last_active

    FROM atomic.events AS ev

    GROUP BY 2, ev.user_id, ev.network_userid, ev.derived_tstamp

);

And then view it:​

SELECT * FROM derived.user_stitching;

Let's break down what you've done​

  • You have learnt what user identifiers Snowplow tracks out of the box, and how you can add a custom user ID to Snowplow web events.
  • You have created a simple user stitching table that links all the identifiers available for a given user over time.

What you might want to do next​

This recipe covers a really simple example of user stitching based on Snowplow's out of the box identifiers and the custom user ID only. Next, you might want to

  • Create a custom user entity to be sent with all events, that captures additional user information such as email address.
  • Implement tracking on other platforms, such as mobile apps and servers, or ingest third party data via webhooks (for example from you email service provider). You can then include the identifiers from these sources in your user stitching table to get a 360 view of your customers across platforms.
  • Use the user stitching table alongside other data models, such as the simple user engagement table in the user engagement recipe. Specifically, you could update the SQL in that recipe to aggregate user engagement based on the stitched user identifiers, rather than simply the domain_userid.
  • Address your obligations as a data controller by learning more about Snowplow's approach to user privacy.

For more information about how to build a single customer view with Snowplow, check out our blog post on the topic.

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