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Play Head Time analysis on
OTT video at scale
Adam Liu​
Biplab Chattopadhyay​
Agenda
▪ Introduction
▪ Definition
▪ Technical challenges
▪ Solutions
▪ Business use cases
Product placement in the movie - Castaway
A poll based on the clip
Which product placement you noticed in the clip?
A poll based on the clip
Answer:
Solving this problem requires capturing
accurate Play Head Time of the video from many
players at global scale with identi...
Play head time
Play Head Time is the pointer representing exact point in a video's play-span that is
currently being watch...
Play head time
Types of play head time:
• Progress-bar PHT
• Position of player UI progress bar
• Reported PHT
• Player re...
Publisher 1
Play head time data collection at scale
Player 3
Player 2
Player 4
Player 1
Player sensor
Publisher 2
Player 3...
Challenges
▪ PHT Data collection challenges
▪ Data precision​
▪ Diversity of data source​s
▪ Impact of advertising​
▪ Dive...
Data precision
• Millisecond level resolution of Play Head
Time value
• Sampling frequency
• Event based sampling
• PHT da...
Diversity of data source
• Player level diversity
• Different players report different video events
• Different players re...
Impact of advertising
• Client-side-ad-insertion
• Ads are managed and added on the end-user’s
device. From delivery point...
Diversity of user behavior
• Watching ads
• Pre-roll ads
• Mid-roll ads
• Post-roll ads
• Seek/rewind:
• Seek/rewind conte...
PHT data analysis pipeline
Player
Sensor
(PHT
collection)
Gateway
Raw
Heart
Beat
data store
Analytics
notebooks
Periodic
h...
Play head time adjustment
30s pre-roll
ad
2min middle-roll ad
Adjusted PHT:
PHT:
00:00 05:00 05:00 07:00
00:30 05:30 07:30...
Adjusted PHT data verification
How do we know when player behavior and
backend processing goes out-of-sync
resulting incor...
PHT processing and application pipeline
Player
Sensor
(PHT collection)
Gateway
User Device
Big Query
Backend data
processi...
Ad Server Logs
• Ad Unites ID
• Advertiser Name
• Campaign Name
• Line Item
• Creative
Device Metadata
• Device ID
• Devic...
Product placement dashboard
Use-case 1: Viewer content engagement analysis
• Viewer content engagement analysis
records how viewers engage with
publis...
Use-case 2: Ad content mutual impact analysis
• Mismatched Ad placement on a content
causes bad user behavior
• Results in...
Use-case 3: Product placement measurement
• Product placement, also known as
embedded marketing, is a marketing
technique ...
If you are interested in Play Head Time and our Use
Cases, please feel free to contact us!
Biplab Chattopadhyay
Architect,...
Feedback
Your feedback is important to us.
Don’t forget to rate and review the sessions.
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Play Head Time Analysis On OTT Video At Scale

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Play Head Time(PHT) is the pointer representing exact point in a video’s play-span that is currently being watched by the user. We are all familiar with Play head pointer being displayed as a slider bar on the video screen. Play head time can apply to regular media content, as well to Ads. It is usually displayed on the slider bar of a running video. However, when measured and analyzed at large scale, with accuracy, in near-real-time, across players and publisher environments – it enables us to solve some very interesting and practical business problems.

For example:

Product placement analysis and measurement.
Content viewer engagement analysis.
Ad Content mutual impact analysis.
At Conviva, with its sensor software present on billions of devices on the planet, across hundreds of publishers/players – we are able to analyze video PHT at scale to solve the above use-cases, and more. There are many challenges to collecting and analyzing Play head time.

Very large data volume.
Need for high precision, at least seconds level data accuracy.
Complex and diverse environment. We need to analyze different user behaviors when watching videos, including pause, buffering, seek, rewind etc. We need to understand different player behaviors across different publisher environments across different video (Live, SVOD, …).
Final challenge is data sanitization.
In this talk we will present how in Conviva we collect PHT data from billions of devices across players and publishers. How in Conviva we use Databricks technology stack to analyze, sanitize, store and process Play Head Time from billions of devices in real-time. How in Conviva we use large volume of processed Play Head Time data to solve real-world business problems.

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Play Head Time Analysis On OTT Video At Scale

  1. 1. Play Head Time analysis on OTT video at scale Adam Liu​ Biplab Chattopadhyay​
  2. 2. Agenda ▪ Introduction ▪ Definition ▪ Technical challenges ▪ Solutions ▪ Business use cases
  3. 3. Product placement in the movie - Castaway
  4. 4. A poll based on the clip Which product placement you noticed in the clip?
  5. 5. A poll based on the clip Answer:
  6. 6. Solving this problem requires capturing accurate Play Head Time of the video from many players at global scale with identity. 1. How many and what kind of people/households, on what type of devices watched my brand on this movie worldwide? 2. What actions did the individuals take after watching my brand placement in the movie?
  7. 7. Play head time Play Head Time is the pointer representing exact point in a video's play-span that is currently being watched by the user. We represent that using time in milliseconds from the beginning of the video content.
  8. 8. Play head time Types of play head time: • Progress-bar PHT • Position of player UI progress bar • Reported PHT • Player reported Play Head Time, as collected by player sensor • Adjusted PHT • True play head progression on pure content
  9. 9. Publisher 1 Play head time data collection at scale Player 3 Player 2 Player 4 Player 1 Player sensor Publisher 2 Player 3 Player 2 Player 4 Player 1 Publisher 3 Player 3 Player 2 Player 4 Player 1 Video Analytics Backend Applications 1 Applications 2 Applications 3 Applications 4 Applications 5
  10. 10. Challenges ▪ PHT Data collection challenges ▪ Data precision​ ▪ Diversity of data source​s ▪ Impact of advertising​ ▪ Diversity of user behaviors​ ▪ Data verification​ at large scale ▪ Challenges to solving real world problems ▪ Data volume / coverage ▪ Identifying the user and its post view behavior
  11. 11. Data precision • Millisecond level resolution of Play Head Time value • Sampling frequency • Event based sampling • PHT data is collected from the player with millisecond accuracy as and when video events occur • Periodic sampling • PHT data is collected from the player through periodic heartbeat messages Conviva heart beat Periodic pht Seek end event pht State Change Event PHT Seek start event pht Ads start event pht
  12. 12. Diversity of data source • Player level diversity • Different players report different video events • Different players report different data in the video events • Example of important events collected during video play: • Video start • Seek forward • Seek backward • Buffering • Ad start • Ad end • Video end • Video type level diversity • Live • VOD • DVR PHT of Seek start: PHT of Seek end(not supported): Roku
  13. 13. Impact of advertising • Client-side-ad-insertion • Ads are managed and added on the end-user’s device. From delivery point-of-view, content and ads are separated. • Player plays content and advertisements independent of each other. • Server-side-ad-insertion • Ads are stitched to the streaming manifest on server side before actual playback allowing smoother playback and transitions
  14. 14. Diversity of user behavior • Watching ads • Pre-roll ads • Mid-roll ads • Post-roll ads • Seek/rewind: • Seek/rewind content • Seek/rewind over single ad • Seek/rewind over multiple ads • Skip Ads • Pause: • User press the pause button • Pause caused by buffering 30s Ad play Skip Ads 1min 1min Ad play Pre-roll ad Mid-roll ad Mid-roll ad Video playback Seek/rewind
  15. 15. PHT data analysis pipeline Player Sensor (PHT collection) Gateway Raw Heart Beat data store Analytics notebooks Periodic heartbeat (With PHT data) PHT data analysis & visualization User device Publisher Player Play head time Using Databricks technology stack to analyze, sanitize, store and process Play Head Time from billions of OTT devices. • Analyze Play Head Time coverage and data accuracy for all players all publishers. • Analyze the diversity of user behavior that match to the Play Head Time • Understand the Play Head Time offset caused by ad play • Implement PHT adjustment algorithms • Verify the adjusted PHT data
  16. 16. Play head time adjustment 30s pre-roll ad 2min middle-roll ad Adjusted PHT: PHT: 00:00 05:00 05:00 07:00 00:30 05:30 07:30 09:30 Adjusted PHT: 01:00 PHT: 01:30 02:30 03:00 04:30 05:00 05:30 08:00 06:30 09:00 Adjusted PHT: 00:30 PHT: 01:00 05:00 05:30 05:00 07:30 06:00 08:30 Ad play Skip Ads Offset = 30s Offset = 2min30s Video playback Seek/rewind
  17. 17. Adjusted PHT data verification How do we know when player behavior and backend processing goes out-of-sync resulting incorrect Adjusted-PHT data? Solution: • For each content, calculate the content length • Publisher provided • Inferred from playback data • For each content, calculate the offset between content length and the Max PHT, then plot the distribution for all contents • Original PHT data • Correct adjusted-PHT data
  18. 18. PHT processing and application pipeline Player Sensor (PHT collection) Gateway User Device Big Query Backend data processing and aggregation pipeline Periodic heartbeat Messages (PHT data) Adjusted PHT Data Video session construction Video session store ETL Jobs Distributed Databases Application Application Applications Analysis and visualization Publisher Player Play head time Heartbeat message • Pht • Client-id • Show- name • Device type • DMA • ...
  19. 19. Ad Server Logs • Ad Unites ID • Advertiser Name • Campaign Name • Line Item • Creative Device Metadata • Device ID • Device Hardware Type • Device Name • Device Operation System Data enrichment 3rd Party Demographic Metadata • Stream ID • Age • Gender • Marital Status • Education Level • Income Video Content Metadata • Content ID • Series Name • Episode Name • Genre List • Content Category Enriched play head time data Product placement dashboard
  20. 20. Product placement dashboard
  21. 21. Use-case 1: Viewer content engagement analysis • Viewer content engagement analysis records how viewers engage with publisher videos by identifying parts of a video that they re-watch, pause, skip, track where on the video most viewers are dropping-off, and so on. • Case-study: A Star Is Born (Movie) • The movie has two popular scenes​ 1. Where actor and actress sing together at a vocal concert​ 2. Where the actress sings the song -"Always Remember US this way” • These two scenes correspond to two peaks of this curve:​ • 40 minute​ • 57 minute​
  22. 22. Use-case 2: Ad content mutual impact analysis • Mismatched Ad placement on a content causes bad user behavior • Results in: • User dropping out fromAd • User dropping out from Content • General bad user engagement • Publisher brand loyalty gets impact • Advertisers lose money • PHT level analytics helps detect these impacts and empower publishers and advertisers to take right actions to mitigate these effects
  23. 23. Use-case 3: Product placement measurement • Product placement, also known as embedded marketing, is a marketing technique where references to specific brands or products are incorporated into a publisher content/video (E.g. a movie, a show) • In exchange for product placement rights, companies may pay a production company or studio in cash, goods, or services • If the lead actor is drinking a clearly labeled Coca-Cola beverage or using a clearly labeled Samsung cell phone, then this is product placement • Aside from an increase in profits, product placement can also boost brand recognition
  24. 24. If you are interested in Play Head Time and our Use Cases, please feel free to contact us! Biplab Chattopadhyay Architect, Advertising, Conviva https://www.linkedin.com/in/biplab- chattopadhyay-3a741b4/ Adam Liu Data Scientist, Conviva https://www.linkedin.com/in/adam-liu- 692b8270/
  25. 25. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.
  26. 26. Thank you!

Play Head Time(PHT) is the pointer representing exact point in a video’s play-span that is currently being watched by the user. We are all familiar with Play head pointer being displayed as a slider bar on the video screen. Play head time can apply to regular media content, as well to Ads. It is usually displayed on the slider bar of a running video. However, when measured and analyzed at large scale, with accuracy, in near-real-time, across players and publisher environments – it enables us to solve some very interesting and practical business problems. For example: Product placement analysis and measurement. Content viewer engagement analysis. Ad Content mutual impact analysis. At Conviva, with its sensor software present on billions of devices on the planet, across hundreds of publishers/players – we are able to analyze video PHT at scale to solve the above use-cases, and more. There are many challenges to collecting and analyzing Play head time. Very large data volume. Need for high precision, at least seconds level data accuracy. Complex and diverse environment. We need to analyze different user behaviors when watching videos, including pause, buffering, seek, rewind etc. We need to understand different player behaviors across different publisher environments across different video (Live, SVOD, …). Final challenge is data sanitization. In this talk we will present how in Conviva we collect PHT data from billions of devices across players and publishers. How in Conviva we use Databricks technology stack to analyze, sanitize, store and process Play Head Time from billions of devices in real-time. How in Conviva we use large volume of processed Play Head Time data to solve real-world business problems.

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