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Content analytics


If the primary function of your site is content consumption, whether it's reading news articles or watching videos, you'll want to understand how that content is performing. While traditional web analytics is focused on page views and sessions, you might be more interested in how long users are engaging with what content.

This recipe will give you an overview of how Snowplow empowers you to get better insights into how your content is performing.

What you'll be doingโ€‹

You have already set up Snowplowโ€™s out of the box web tracking by instrumenting the Javascript Tracker in your application. This includes tracking page_view and page_ping events.

To understand how people are engaging with your content, youโ€™ll want to be tie these events to specific pieces of content, not just pages.

For this purpose, you can add a content entity which will be sent every time these events are tracked. You can then aggregate all of your user behavioral data into one row per content piece to get a better view of how your content is performing.

Design and implement the content entityโ€‹

Designing the entityโ€‹

We have already created a custom content entity for you in Iglu Central.

The content entity has the following fields:

nameThe name of the piece of contentstringmaxLength: 255โœ…ย 
idThe content identifierstringmaxLength: 255โŒ
categoryThe category of the piece of contentstringmaxLength: 255โŒ
date_publishedThe date the piece of content was publishedstringmaxLength: 255โŒ
authorThe author of the piece of contentstringmaxLength: 255โŒ

Implementing the entityโ€‹

In the Javascript Trackerโ€‹

Add the content entity to your page_view and page_ping events by editing your trackPageView events to include the entity. Specifically, update



window.snowplow('trackPageView', {
"context": [{
"schema": "",
"data": {
"name": "example_name",
"id": "example_id",
"category": "example_category",
"date_published": "01-01-1970",
"author": "example_author"

Via Google Tag Managerโ€‹

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

window.snowplow('trackPageView', {
"context": [{
"schema": "",
"data": {
"name": "{{example_name_variable}}",
"id": "{{example_id_variable}}",
"category": "{{example_category_variable}}",
"date_published": "{{example_date_variable}}",
"author": "{{example_author_variable}}"

Modeling the data you've collectedโ€‹

What does the model do?โ€‹

The tracking above captures which content users are consuming and how they are engaging with it. This allows you to get a better understanding of how your content is performing.

For this recipe we'll create a simple table describing content engagement. Once you have collected some data with your new tracking you can run the following two queries in your tool of choice.

First generate the table:โ€‹

CREATE TABLE derived.content AS(

WITH content_page_views AS(

SELECT AS page_view_id,
c.category AS content_category, AS content_name,
c.date_published AS date_published, AS author,
10*SUM(CASE WHEN ev.event_name = 'page_ping' THEN 1 ELSE 0 END) AS time_engaged_in_s,
ROUND(100*(LEAST(LEAST(GREATEST(MAX(COALESCE(ev.pp_yoffset_max, 0)), 0), MAX(ev.doc_height)) + ev.br_viewheight, ev.doc_height)/ev.doc_height::FLOAT)) AS percentage_vertical_scroll_depth

INNER JOIN atomic.com_snowplowanalytics_snowplow_web_page_1 AS wp
ON ev.event_id = wp.root_id AND ev.collector_tstamp = wp.root_tstamp
INNER JOIN atomic.io_snowplow_foundation_content_1 AS c
ON ev.event_id = c.root_id AND ev.collector_tstamp = c.root_tstamp

GROUP BY 1,2,3,4,5,ev.br_viewheight,ev.doc_height


COUNT(DISTINCT page_view_id) AS page_views,
ROUND(SUM(time_engaged_in_s)/COUNT(DISTINCT page_view_id)) AS average_time_engaged_in_s,
ROUND(SUM(percentage_vertical_scroll_depth)/COUNT(DISTINCT page_view_id))AS average_percentage_vertical_scroll_depth

FROM content_page_views

GROUP BY 1,2,3,4


And then view it:โ€‹

SELECT * FROM derived.content;

Let's break down what you've doneโ€‹

  • You have captured granular data around how your users are engaging with your content, including time engaged and scroll depth.
  • You have modeled this data into a content engagement table that surfaces the user engagement per content piece. This gives you an overview of how your content is performing across your site.

What you might want to do nextโ€‹

Understanding how your users are engaging with your content is just the first step. Next, you might want to

  • Extend this table to include where the content is being promoted on your site to understand how placement affects performance.
  • Start mapping the relationships between content pieces based on user behavior, working towards compelling content recommendations.
  • Pivot this data to look at users instead: understand which marketing channels users come from, and how that affects their engagement with your content.
  • Etc.

To learn more about Snowplow for media and entertainment, check outย our blog series on the topic.

Ready to get started with content recommendations? Check out our step-by-step guide.

Unleash the power of your behavioral data
If you're looking for more guided approaches that contains information about tracking and modeling your data, check out all our Data Product Accelerators!
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