These are the slides from my presentation at the Analytics Summit in Hamburg, Germany, on November 19th. I am Viewster's Head of Analytics and this presentation discusses how we built a robust system for tracking video-related analytics using Google Analytics. Main sections: 1. Setting the Scene - Viewster in Summer 2013 2. Problem – Thousands of video titles, multiple platforms 3. Solution – Custom Google Analytics implementation 4. Setting up reporting 5. Actual learning! 6. What now? Topics discussed: Google Tag Manager, Custom Events, Custom Dimensions, Custom Metrics, Data Import, the Measurement Protocol, Custom Reports, Custom Segments, Shortcuts, Calculated Metrics, Time Tracking, and more! More info: Me on LinkedIn: https://de.linkedin.com/in/randallhelms Viewster on LinkedIn: https://www.linkedin.com/company/viewster-ag Our website: http://viewster.com/ The Analytics Summit: https://www.analytics-summit.de/

1. Analytics Summit
November 19 2015
Video Tracking in Google Analytics
Lessons Learned and Tricks Discovered
(Director’s Cut)
Randall Helms

2. Analytics Summit
November 19 2015
Note on this version of the slide deck:
This an update on my presentation at the Analytics Summit.
During the Q&A section there were some very valid questions asked, and I wanted to expand on them here
in the slide deck, to make this more useful to other Google Analytics users.
Special thanks to Timo and the team at Trakken for inviting me to speak.
Also a huge thanks to the developers, past and present, in Kiev and Timisoara who have helped me build out
Viewster’s GA/GTM implementation: Maksim Pysanets, Sasha Sidorenko, Anton Marianov, Bogdan
Romashko, Oleksii Beloborodov, Dima Kovbasyuk, Oleksandr Drachuk, Stanislav Sydorenko, Igor Sosedov,
Fabian Mistoiu, Vitalii Cherepakha, Evgeny Pankratov, Raul Genescu, Igor Sosedov, Cosmin Grosan, Razvan
Olaru, Zoltan Csibi and Alex Dudinskyi
Randall Helms
Berlin, November 2015
LinkedIn: https://de.linkedin.com/in/randallhelms
Twitter: @sonicrampage

3. A Brief Guide to Video Tracking at Viewster
1. Setting the Scene - Viewster in Summer 2013
2. Problem – Thousands of video titles, multiple platforms
3. Solution – Custom Google Analytics implementation
4. Setting up reporting
5. Actual learning!
6. What now?

4. Back in the mists of
time …
(aka the summer of 2013)

5. Zurich-based Ad-Supported Video-on-Demand (AVOD) service,
with offices in Kiev, Timisoara, and Berlin (new when I joined)
Content:
• Thousands of titles
• Wide mix of genres
• Mostly lesser-known independent material
• No overall theme – content purchased opportunistically
Introducing Viewster in 2013

6. Viewster.com
Platform
Desktop Site
Product Mix – Summer 2013
Connected Devices
Mobile Apps iOS
Android
Smart TVs
Roku
Product GA Tracking Solution
GA Javascript
Samsung CTV

7. • Video & ad start tracking to backend SQL server
• Standard Google Analytics JS setup for all products
• Minimal video tracking
• Faulty tracking on mobile – not using native GA tracking tools
Results of the tracking situation:
• Poor understanding of user behaviour
• Poor knowledge of best-performing content
• Lack of useful data to drive business strategy
Tracking Situation – Summer 2013

8. What did we need
to know?

9. • How do users interact with our product?
• How can we improve our product?
• What separates returning users from those who
come once and never return?
• How can we be more effective at online marketing?
Standard Questions

10. • What content do users watch?
• How long do users watch for?
• Does different content inspire different user
behaviour?
• How can we make smarter decisions about what
content to buy?
• Do users watch differently on different devices?
Video-Specific Questions

11. • Build out Data Warehouse (DWH)
• Full event tracking
• Accessible via SQL queries
• Connected to visualization tool (Tableau,
Qlikview, etc)
• Fix our Google Analytics implementation
Our options

12. Solution: Custom
Google Analytics
Implementation

13. Viewster.com
Platform
Desktop Site
Product Mix – Start 2014
Connected Devices
Mobile Apps iOS
Android
Smart TVs
Roku
Product GA Tracking Solution
GA Javascript
Mobile Site
Samsung CTV

14. Viewster.com
Platform
Desktop Site
Product Mix – Current
Connected Devices
Mobile Apps iOS
Android
Smart TVs
Roku
Product GA Tracking Solution
Android GTM
Mobile Site
Samsung CTV
Xbox
iOS GTM
GTM JS Container
Measurement
Protocol

15. How We Customized Google Analytics for Video:
Google Tag Manager:
• Custom Event tracking
• Custom Dimensions & Metrics
Why GTM? Super flexible, allows non-developers to make changes
Google Analytics:
• Calculated Metrics
• Data Import
• Goals

16. Event Category
• Ad Started
• Ad Finished
• Video Started
• Video Paused
• Video Resumed
• Video Finished
• Watch Time
• Language Change
• Quality Change
• Auto-Play Next
Episode
• Change Episode
• Video
Custom Video Events Hierarchy in GTM
Event Action Event Label
Built entirely from
macros (or hardcoded)
in Google Tag Manager;
no need for developers
to pass a value

17. When does a Video Start?
1. When the request goes to the video server?
2. When the video begins loading?
3. When the video has finished loading and is playing?
In 2013 we had Video Start tracking with all three definitions, with
different developers on different platforms applying different logic
For the sake of data consistency, the same events must be
triggered at the same points across all platforms you control!
My definition of a Video Start: When the video has loaded and
begun playing
Getting Events Right

18. Custom Dimensions
Sent via dataLayer
• Movie ID
• Video Title
• Video Type
• Genre
• Audio Language
• Subtitle Language
• Video Session ID
• Movie Position
Custom Metrics
Sent via dataLayer:
• Seconds Watched
Added via GTM:
• Video Starts
• Video Pauses
• Video Resumes
• Video Finishes
Calculated Metrics:
• Avg. Watch Time
• Finish Ratio
• Views per User
• Watch Time / User
Via Data Import:
Anything! Just needs
to match a Movie ID
Custom Definitions
Custom definitions (metrics
and dimensions) are
crucial to understanding
how well content performs
in Google Analytics.
Adding Custom Variables to Events

19. Challenges:
1. How long do users watch a certain series episode for on
average?
2. What is the distribution of time watched for views of that
episode?
3. How often do users finish the episode?
Time Tracking for Video (1/3)

20. Solution: JavaScript Timer
Time Tracking for Video (2/3)
Seconds 0 20 40 60 80 100 120 140
User
Timer
Events:
Clicks ResumeClicks PauseClicks Play
Player
Clicks to Home
Video Loads Video Pauses Video Resumes Video Stops
Timer Starts Timer Pauses Timer Resumes Timer Stops
GTM Video Started Video Paused Video Resumed Watch Time
The Watch Time event sends
a custom metric of the
seconds watched to GTM

21. Video Started
Events
Video Title
Time Tracking for Video (3/3)
Watch Time
Episode Number
Video Session ID
Custom Metrics
Video View
Custom Dimensions Calculated Metrics
Average Watch Time
Watch Time /
Video Views
Watch Time
Video View Duration
Time Watched
Distribution
Video Views by Video
View Duration value
Video Finish
Video Finished
Finish Ratio
Video Finishes /
Video Views
Key:
• Custom via Data Layer
• Custom via Data Import
• Custom via GTM

22. Importing Data – Several Methods
Data Import – Done in Google Analytics either via API or via CSV upload
dataLayer.push – Pass variables to GTM with custom events

23. GTM Tricks
Example 1: Creating an Event Label using Custom Dimension macros
Example 2: Hardcoding
Custom Metrics
Example 3: Create new variables with Lookup Table

24. QA & Testing
Tools for testing:
• GA Test Property: Separate property just for testing; get clean data
• GTM Test Container: Copy of a container with all ‘live’ events, but a different GA property
end point
• Tip: Use a Constant variable for the GA property ID to quickly change the end point
• GTM debug mode (web): Check that your tags are firing with the correct info in variables
• GA real-time view (all products): See that GA is receiving your events in real-time
• Logs from USB command line debug (Android) & Xcode (iOS): Check that events are firing
correctly with the right info

25. Learning from the
data: building
robust reports

26. With Custom Reports you can combine your custom and standard dimensions and
metrics into meaningful reports to help you better understand performance
Building Custom Reports for Video
Custom Dimension Custom Metric Standard Metric Custom Metric
Custom Metric Standard Metric
Calculated Metric
Calculated Metric Calculated Metric Calculated Metric
Data sample: Segmented data from our Android app

27. Custom Segments allow you to effectively compare users of different content types
Custom Segments
Anime Viewers
Viewers of other video content
PS: Use Shortcut to save custom report/custom segment configurations

28. Dashboards are a great way to monitor content performance in one place
Dashboards

29. What did we
learn from the
data?

30. Hypothesis: Hollywood trailers have a negative impact on Viewster
To test the video part of this analysis, I used custom segments to split up users:
• Those who watch only trailers
• Those who watch trailers and long-form content
• Those who watch only long-form content
• Those who watched no content
Results:
IVA trailers brought low-quality users (who were expecting Hollywood blockbusters)
These users were less likely to watch other content
Recommendation: Remove trailers from the service
Content Strategy from GA: Example 1

31. Content Strategy from GA: Example 1
23%
38%
77%
62%
Anime Users General Audience
% of Users Watching Trailers
Yes No
1.7
3.2
2.3
3.9
1.6
3.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Actors &
Trailers
Homepage Series &
Movies
Genre 404 Errors Other
Pages/Session by Landing Page
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
All Anime Users Trailer Watchers
Total Time per User (Minutes)

32. Hypothesis: Anime attracts better quality users
Testing Methodology:
• Use custom segments to split users in core territories (US,
UK, DACH, Scandinavia, FR, Benelux) by the following
criteria:
• Those who have watched at least 1 anime video
• Those who have watched video but no anime
• Those who have watched no video
• Compare across range of user behavior metrics
Content Strategy from GA: Example 2

33. Comparing Anime Viewers to Non-Anime Viewers
The result was clear. Anime users were better quality:
• More likely to return: 53% more likely to return 5+ times in
a month
• Spend more time on site: 44% longer average session
duration
• Watch more content: 58% more views per user

34. What’s next?

35. • Need to understand content consumption
• Chose a custom Google Analytics implementation on web,
mobile and connected devices
• Heavy reliance on Google Tag Manager
• Understood video via custom events, enriched through
custom dimensions and metrics
• Reporting used custom segments and custom reports
• Deep analysis to drive business strategy forward
Quick Recap

36. Introducing Omakase

37. New Project: Omakase
Launched October, 2015
Subscription Product based on the Anime lifestyle:
1. Bi-Monthly Goodie Box with exclusive Anime-themed
merchandise
2. Premium Video Service (HD, no ads – in future exclusives)
3. Digital Goodies (forthcoming) – Music, Comics, In-Game
Avatars, and more!