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Personalized Video Programming
Unleashing the Power of Prescriptive Analytics as a
Revenue Driver
Dr. Thomas J. Sullivan
Chief Data Scientist
Nicholas Os...
Outline
• About IRIS.TV
• Path Toward Prescriptive Analytics
• Mathematical Formulation
• Conditions for Success
• Human v...
Personalized Video Programming
What is IRIS.TV?
The IRIS.TV Video Programming Platform is a
lightweight API made up of thr...
The IRIS.TV Experience
* Video Video VideoVideo
* * *
Initial View Recommended Views
Path Toward Prescriptive Analytics
“LEVER”
An available resource
that may be used to
generate an expected
response
A Simple Mathematical Explanation
With an understanding of:
• The expected outcome that changing lever(s) will have on a g...
Predictive Model Variable Type
1. Exogenous model variables: those input variables that can not be
modified by anyone (e.g...
Higher Dimensional Solution Frontier
Feasible Solution Area
Constraints may
make some
outcomes infeasible
𝑦 = 𝛼 + 𝛽1 𝑥1 + ...
In What Type of Environment Can Prescriptive Analytics be Useful?
• Levers and constraints are known
• The relationship be...
The Feedback Loop
Historical
Data
Descriptive
Analytics
Predictive
Analytics
Identify
Goals and
Constraints
Prescriptive
A...
CASE STUDY: NBA Finals
Background
• Week before NBA Finals
• Major sports client serving
videos
• Seeking prescriptions to...
Prescriptive Illustrations
Levers that can be used to influence Video Lift
Video Lift = Recommended Views/User Experiences...
Available Levers
Note: Levers ordered by expected effect on
Video Lift
Correct Supply / Demand Imbalance
Add Video Categor...
Relevant Movable Levers
• Supply / Demand Analysis of portfolio assets (to meet audience
needs and retain them longer)
• P...
Supply / Demand Imbalance
Category Completeness Prescription
Add category metadata, including NBA assets, in order to increase lift
Initial View Out...
Asset Length Prescription
Use video length lever to develop videos less than 3 minutes long
Initial View Outcomes by Video...
Audience Engagement Prescription
Engaged audience is expected to be largest around noon Monday
Final Prescriptions
• Supply / Demand imbalance
• Metadata - Context
• Video Length
– Prescriptions based on decomposition...
What is the Appropriate Balance Between Human and Machine?
Iterative interaction – as far as
possible, human informs machi...
Closing Thoughts
“This is not a race against the machines.
If we race against them, we lose.
This is a race with the machi...
Featured Customers
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Big Data Day LA 2016/ Use Case Driven track - Shaping the Role of Data Science: An Evolution towards Prescriptive Analytics as Key Driver in Revenue Acceleration, Thomas Sullivan, Chief Data Scientist, IRIS.TV

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At IRIS.TV, our business builds algorithmic solutions for video recommendation with the end goal to deliver a great user experience as evidenced by users viewing more video content. This talk outlines our reasons for expanding from a descriptive/predictive approach to data analytics toward a philosophy that features more prescriptive analytics, driven by our data science team.

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Big Data Day LA 2016/ Use Case Driven track - Shaping the Role of Data Science: An Evolution towards Prescriptive Analytics as Key Driver in Revenue Acceleration, Thomas Sullivan, Chief Data Scientist, IRIS.TV

  1. 1. Personalized Video Programming
  2. 2. Unleashing the Power of Prescriptive Analytics as a Revenue Driver Dr. Thomas J. Sullivan Chief Data Scientist Nicholas Oswald Data Scientist
  3. 3. Outline • About IRIS.TV • Path Toward Prescriptive Analytics • Mathematical Formulation • Conditions for Success • Human vs. Machine Tradeoff • The Feedback Loop • Use Case: Online Video
  4. 4. Personalized Video Programming What is IRIS.TV? The IRIS.TV Video Programming Platform is a lightweight API made up of three components easily integrated into your existing video environment • Video personalization across all devices • Automated data structuring • Business Intelligence & Programming Management
  5. 5. The IRIS.TV Experience * Video Video VideoVideo * * * Initial View Recommended Views
  6. 6. Path Toward Prescriptive Analytics “LEVER” An available resource that may be used to generate an expected response
  7. 7. A Simple Mathematical Explanation With an understanding of: • The expected outcome that changing lever(s) will have on a goal (e.g. increase monthly revenue) • A desired goal (optimize use of current resources and/or achieve a different level of outcome)… …prescriptive analytics can be used as a decision aid when identifying how levers can be adjusted (“courses of action”) EXAMPLE: 𝑦 = 𝛼 + 𝛽𝑥 𝑥 = 𝑦∗ − 𝛼 𝛽 Predict y: Prescribe an estimated value of x that will generate a desired outcome, y*:
  8. 8. Predictive Model Variable Type 1. Exogenous model variables: those input variables that can not be modified by anyone (e.g. time, weather) 2. Immovable Levers: Resources over which the consumer of prescriptive analytics has no control (e.g. a different department) 3. Movable Levers: Resources that may be re-allocated, as prescribed, toward achieving a desired outcome (e.g. money, people, computing cycles) Prescriptions, though considerate of all types of input variables, focus on the movable levers, particularly those that result in greatest impact
  9. 9. Higher Dimensional Solution Frontier Feasible Solution Area Constraints may make some outcomes infeasible 𝑦 = 𝛼 + 𝛽1 𝑥1 + 𝛽2 𝑥2 + …
  10. 10. In What Type of Environment Can Prescriptive Analytics be Useful? • Levers and constraints are known • The relationship between the outcome and the input levers are known or estimated (with cause-effect established to an acceptable level) • Consumer(s) of prescriptions has the ability and the willingness to change levers • Feedback loop exists: – Historical Data Exists (Descriptive) – Data are turned into predictive (via modeling, simulation, NN, etc) analytics – Data-driven prescriptions are generated and implemented – Observed effects - along with subject matter expertise - are used to validate and refine prescriptive analysis • Trust in prescriptions can evolve over time
  11. 11. The Feedback Loop Historical Data Descriptive Analytics Predictive Analytics Identify Goals and Constraints Prescriptive Analytics Implementation Exogenous Immovable Levers Movable Levers Subject Matter Expertise
  12. 12. CASE STUDY: NBA Finals Background • Week before NBA Finals • Major sports client serving videos • Seeking prescriptions to improve Video Lift
  13. 13. Prescriptive Illustrations Levers that can be used to influence Video Lift Video Lift = Recommended Views/User Experiences * Video Video VideoVideo * * *
  14. 14. Available Levers Note: Levers ordered by expected effect on Video Lift Correct Supply / Demand Imbalance Add Video Category Metadata Reduce Average Length of Video Add Valid Video Source ID Add Video Adjustable Levers for Increasing Video Lift Potential Increase in Video Lift
  15. 15. Relevant Movable Levers • Supply / Demand Analysis of portfolio assets (to meet audience needs and retain them longer) • Pilot for decomposing asset length and completeness of categories (increase video lift) • Heat maps for illustrating when viewers are on the site (staff interaction and ad placement) By Moving these 3 levers users will watch 20% more videos
  16. 16. Supply / Demand Imbalance
  17. 17. Category Completeness Prescription Add category metadata, including NBA assets, in order to increase lift Initial View Outcomes by Missing Category
  18. 18. Asset Length Prescription Use video length lever to develop videos less than 3 minutes long Initial View Outcomes by Video Length
  19. 19. Audience Engagement Prescription Engaged audience is expected to be largest around noon Monday
  20. 20. Final Prescriptions • Supply / Demand imbalance • Metadata - Context • Video Length – Prescriptions based on decomposition of the three main levers in the previous slides to isolate expected value of moving levers individually or jointly – Better outcome expected if done before Monday noon when audience engagement peaks
  21. 21. What is the Appropriate Balance Between Human and Machine? Iterative interaction – as far as possible, human informs machine, machine infers rules Lots of subject matter expertise/little access to suite of analytical tools Little subject matter expertise / insufficient access to suite of analytical tools
  22. 22. Closing Thoughts “This is not a race against the machines. If we race against them, we lose. This is a race with the machines.” -The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
  23. 23. Featured Customers

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