Visualize engagement in your videos and ads with Snowplow’s new Media Analytics Accelerator

Snowplow's new Video and Media Analytics Accelerator, along with the latest release of our media-player package (dbt), brings a series of new features and visualizations to understand user behavior across your media and video content.

While 91% of businesses use video as a marketing tool, few businesses deeply understand how their users interact with this content and what effect this has on business metrics. Collecting vital engagement data plays a significant role in understanding your audience, optimizing your content, and measuring the impact of your video and media marketing efforts.

Snowplow has long supported brands like Just Watch to capture and model granular engagement across their media content. The introduction of our new Video and Media Analytics Accelerator, along with the latest release of our media-player package (dbt), brings a series of new features and enhancements for accurate metrics and media ad tracking to further elevate and visualize your understanding of user behavior across your media and video content.

See the engagement and ad metrics that matter

Our new accelerator guides you through tracking, modeling and visualizing valuable insights across your media players. If you haven't quite got your Snowplow pipeline set up yet, you can upload our sample event data to try out modeling and exploring the metrics available. The provided Tableau workbook is a great place to get started. 

Before we jump into the dashboards available as part of this accelerator, let's talk about the introduction of two new modeled tables as part of our latest media player package release. These new tables enable users to report on ads played during media playback.

• The media_ad_views table shows information about each ad view such as the percentage progress reached, whether the ad was clicked or skipped, and more.
• While media_ads table aggregates the ad views per each ad viewed within a media content and calculates the total metrics for all ad views and for unique users. 

With the accelerator, you can visualize both engagement in content and advertising on your platform across a series of three dashboards. You can see an interactive version of the Tableau dashboards in the accelerator docs.

1. Overview: Get a high-level view across all your media and video content performance including ‘total watch time’, ‘plays over time’, and ‘top-performing content’.
2. Content: Gain deeper insights into your overall and individual media with impressions to plays, overview audience retention, and deep dive into individual content performance.
3. Ads: Improve advertising performance with key metrics like ‘click-through-rate’ and see what content returned the most ad views and clicks. Know how far viewers reach through your ads with the Ad Audience Retention chart, to understand where in the ad viewers may be skipping to improve ad creative and performance.

New video-tracking metrics for improved reporting

The latest update to our media models (v0.6) brought a series of new metrics to improve reporting.

Improved time and playback metrics

Media events now provide playback metrics calculated on the tracker, for example, ‘time played’ and ‘average playback rate’. This enables the package to report more accurate metrics in the media_plays and media_stats tables than previously possible! The new information is tracked in the media session context entity, introducing a range of new metrics as described below.

New content watched metric

Understand audience engagement with your media content with the new metric content_watched_secs, the total seconds of the content played. Each part of the content played is counted only once, eliminating duplicate counts for rewinding or rewatching. This metric is used in our dbt package to calculate an accurate percentage of content played statistics.

Understand buffering 

Buffering is one of the key elements of video performance, and we now include the total number of seconds that the video was buffering (buffering_time_secs). This allows you to understand key insights like buffering ratio - the ratio between buffing duration and the actual duration of the video. The longer a video is stalling the more likely a viewer is likely to leave. You now have the data to back this up and discover areas of improvement.



SELECT
DATE(START_TSTAMP) AS START_DATE,
AVG(BUFFERING_TIME_SECS/DURATION_SECS) AS BUFFERING_RATIO
FROM SNOWPLOW_MEDIA_PLAYER_PLAYS_BY_PAGEVIEW
GROUP BY 1



Mobile Support

Under the hood, the entire base of the package has been replaced to support mobile events. The dependency on the Snowplow Web dbt package has been removed by adding a self-contained base inside the media package. You can now model both web and mobile events together to get insights across both platforms.

Getting Started

With all these new enhancements, you can gain deeper insights into user behavior and elevate your media strategies for better engagement and performance. These new features are all possible thanks to the new media tracking SDKs. Your feedback is invaluable to us, so please don't hesitate to share your thoughts on Discourse.

For open source users, the latest Snowplow Media Player dbt package (v0.6) has been released under our new Snowplow Personal and Academic License and is available as part of our new Data Model Pack. If you would like to discuss the use of our Data Models Pack, please book a walkthrough with one of our experts.

Try out the accelerator today and unlock the full potential of adding tracking to your media players!