Snowplow JavaScript Tracker 2.6.0 released with Optimizely and Augur integration

Share

We are excited to announce the release of version 2.6.0 of the Snowplow JavaScript Tracker! This release brings turnkey Optimizely and Augur.io integration, so you can automatically grab A/B testing data (from Optimizely) and device and user recognition data (from Augur) with the events you track with the JavaScript Tracker.

In addition, we have rolled out support for Enhanced Ecommerce tracking, improved domain management and better handling of time! Read on to find out more…

Optimizely and Augur logos

  1. Optimizely integration
  2. Augur.io integration
  3. Enhanced Ecommerce tracking
  4. Better automatic domain management (for cookies) and other new functionality
  5. Improved handling of time
  6. Updates and Bug fixes
  7. Upgrading
  8. Documentation and help

1. Optimizely integration

The Optimizely integration delivers your Optimizely testing data with each event tracked in Snowplow, making it easy to identify:

  1. Whether an experiment was running when the event was recorded
  2. If so, which variation the user is exposed to
  3. All the relevant metadata associated with the experiment (and any others that are running)

It is very common for Snowplow users to track A/B testing data in Snowplow. This means you can assess the effectiveness of different experiments directly by analyzing your Snowplow data. This is enormously valuable, as it means you can not only measure the impact of individaul experiments, but slice results by any of the myriad dimensions that Snowplow makes available to you. (Including any that you build yourself on the event-level data, e.g. behavioural segments.) In addition, you can build a picture, for individual users, of the different experiments that they have been exposed to over their lifetimes, enabling you to model the impact of individual and collective testing on user behaviour over a long time horizon.

The integration makes it simple for Optimizely users to grab their Optimizely data in Snowplow: previously users had to write custom JavaScript to grab the relevant fields from the Optimizely data object and push it into Snowplow, either using their own events (experiment ‘a’ run) or context (event ‘b’ occurred whilst experiment ‘a’ was running). Now Optimizely users can simply specify which parts of the data object they would like recorded in Snowplow when the JavaScript is initialized, and the tracker will take care of the rest, grabbing the relevant data from Optimizely and sending it as custom context with every event that is recorded into Snowplow. It is as simple as follows:

window.snowplow('newTracker', 'cf', 'd3rkrsqld9gmqf.cloudfront.net', { appId: 'CFe23a', platform: 'web', contexts: { ... 'optimizelyExperiments': true, 'optimizelyStates': true, 'optimizelyVariations': true, 'optimizelyVisitor': true, 'optimizelyAudiences': true, 'optimizelyDimensions': true } <span class="p">});

The integration works by auto-populating the different contexts listed above (Experiments, States, Variations, Visitor, Audiences and Dimensions. You can view the different Redshift table definitions that are populated using the Optimizely context below:

Tracker Argument Corresponding Redshift table definition
optimizelyExperiments com.optimizely/experiment_1.sql
optimizelyStates com.optimizely/state_1.sql
optimizelyVariations com.optimizely/variation_1.sql
optimizelyVisitor com.optimizely/visitor_1.sql
optimizelyAudiences com.optimizely/visitor_audience_1.sql
optimizelyDimensions com.optimizely/visitor_dimension_1.sql

Some notes on using these contexts:

2. Augur.io integration

Augur.io is a device and user recognition service, that amongst other things has robust device fingerprinting technology that does not rely on cookies.

The Augur.io integration means that Augur device recognition data is automatically captured and passed into Snowplow with each event recorded, which includes the following data points:

The full SQL table definition can be found here.

Like the Optimizely integration, the Augur integration is enabled when you initialize the JavaScript:

window.snowplow('newTracker', 'cf', 'd3rkrsqld9gmqf.cloudfront.net', { appId: 'CFe23a', platform: 'web', contexts: { ... 'augurIdentityLite': true } <span class="p">});

Note that you need to set up your own Augur account and to be loading the Augur Javascript separately for this integration to work. Please see the Augur website for details.

3. Enhanced Ecommerce tracking

It has always been possible for Snowplow users to track enhanced ecommerce-like events, including product views (impressions), add to baskets and remove from baskets events.

A number of our users come to Snowplow from Google Analytics, having already implemented Enhanced Ecommerce. With this release, they can now mirror their GA enhanced ecommerce integrations in Snowplow directly, cutting down implementation time.

There are two ways to setup enhanced ecommerce tracking in Snowplow:

  1. Assuming you setup Enhanced Ecommerce via GTM and the GTM dataLayer, we recommend integrating Snowplow tracking tags as documented here.
  2. If you have not integrated Enhanced Ecommerce via GTM, you can mirror the integration in Snowplow using the new Enhanced Ecommerce methods listed below.
Tracker Function

Corresponding Redshift table definition
trackEnhancedEcommerceAction com.google.analytics.enhanced-ecommerce/action_1.sql
addEnhancedEcommerceActionContext com.google.analytics.enhanced-ecommerce/action_field_object_1.sql
addEnhancedEcommerceImpressionContext com.google.analytics.enhanced-ecommerce/impression_field_object_1.sql
addEnhancedEcommerceProductContext com.google.analytics.enhanced-ecommerce/product_field_object_1.sql
addEnhancedEcommercePromoContext com.google.analytics.enhanced-ecommerce/promo_field_object_1.sql

4. Better automatic domain management (for cookies) and other new functionality

The first party cookies set by the Javascript tracker are automatically set to the top-level domain of the web page. That means if a user is on www.mysite.com, they will be set to www.mysite.com. If the user moves to a new top level domain e.g. blog.mysite.com, a new cookie will be set on the new top level domain blog.mysite.com. That means the domain_userid value recorded for the user on www.mysite.com will be different to the domain_userid value set on blog.mysite.com.

That is not ideal: in general we would like each user (or failing that device) to have a consistent first party cookie ID across different top level domains. Previously, this was achieved by setting the cookie domain to .mysite.com when initializing the tracking:

window.snowplow('newTracker', 'cf', 'd3rkrsqld9gmqf.cloudfront.net', { appId: 'CFe23a', platform: 'web', ... cookieDomain: '.mysite.com' <span class="p">});

That was fine for users rolling out Snowplow tracking on one domain, but for users who wanted to roll out Snowplow across hundreds of domains, it created friction because a different tag (with a different cookieDomain value) needed to be set for each root domain.

Now that is no longer necessary, you can simply set discoverRootDomain to true, and the cookie domain will automatically be set to the root domain rather than the top level domain:

window.snowplow('newTracker', 'cf', 'd3rkrsqld9gmqf.cloudfront.net', { appId: 'CFe23a', platform: 'web', ... forceUnsecureTracker: true, discoverRootDomain: true <span class="p">});

We have also added a feature that enables you to force sending data from the tracker via HTTP rather than HTTPS. Note that this should only be used in test environments. To force sending events via HTTP, set forceUnsecureTracker to `true in the tracker initialization:

window.snowplow('newTracker', 'cf', 'd3rkrsqld9gmqf.cloudfront.net', { appId: 'CFe23a', platform: 'web', ... forceUnsecureTracker: true <span class="p">});

5. Improved handling of time

Previously, the tracker recorded the timestamp on the client device when an event was recorded. This is the value that you see when querying the dvce_created_tstamp in Redshift.

Now the tracker records the timestamp when the event was recorded and the timestamp when the event was sent to Snowplow. Note that there can often been a delay between an event happening and the data being sent, because:

Snowplow uses the combination of the dvce_created_tstamp, dvce_sent_tstamp and collector_tstamp to figure out the actual time (in UTC) when the event occurred and report that in the derived_tstamp field for easy use in time-based analysis. For more information on the algorithm used, please see our earlier blog post improving Snowplow’s understanding of time. As far as we are aware we are the only analytics provider with a robust approach to handling late arrival of data.

6. Updates and Bug fixes

Other updates include:

7. Upgrading

The upgraded minified tracker is available here:

http(s)://d1fc8wv8zag5ca.cloudfront.net/2.6.0/sp.js 

8. Documentation and help

Check out the JavaScript Tracker’s documentation:

The v2.6.0 release page on GitHub has the full list of changes made in this version.

Finally, if you run into any issues or have any questions, please raise an issue or get in touch with us via the usual channels.

Share

Related articles