clarity solution group presentation at the chief analytics officer, fall 2016

Machine Learning and the Future

of Media Leveraging ML techniques to solve Business

Objectives

2

Agenda

Proprietary and Confidential - ©2016 Clarity Solution Group, LLC

ML Case study

Case Study 1

Media Industry

Industry Trends and Issues

Media Industry

The potential for ML

Media Industry

Best practice approach

ML Case study

Case Study 2

3

Industry Issue: Abundance

Propriet ary and Confidential - ©2016 Clarity Solution Group, LLC

More content than ever, but…

4 Propriet ary and Confidential - ©2016 Clarity Solution Group, LLC

Industry Issue: Disintermediation

5

Industry Issue: Changing patterns


Consumers are not viewing programs when they air, or in one

sitting, or on one device

6

Industry Issue: Optimize

Find the optimal viewing behavior for monetization


7

Implications

1. More personalization

• more segmented, affordable video bundles ?

• Integrate video streaming service with broadband access ?

2. Deeper relationships with fans

• With so much competition across so many channels, you can’t just develop content to get the largest audience


8

The Potential of Machine Learning


Set-top box data Digital data

Personalized

bundles

Identifying

high churn

risk

Personalized

marketing

Product

development

based on

viewing habits

Advertising

delivery

optimization

9

Data implications

1. Challenges identifying people across devices

2. Poor data integration processes


10

Overall Process


POC- Representative Sample data +

Desktop version

code in Python

ML Implementation- SPARK

OUTPUT Database

Defining end to end solution is the key

11

Key Considerations


Implication Consideration

Machine Learning is not a stand-alone exercise

Outline end to end process with business application integration points

Business Collaboration in “training” process is critical

Ensure heavy degree of subject matter expert involvement

Recognize the importance of technique in the solution

Leverage a data science process: Problem to hypothesis to technique selection

Underlying technology is not “one size fits all”

Machine Learning / Big Data solutions require customization and corresponding

investment in people

12

The Business Problem

Digging Deeper

Understanding the question that the Client is trying to answer drives innovation (ML Algorithm), service and effectiveness (technology and platform)


Client

Clarity

Consultants

Iterative Process

Case Study- 1

13

Q: How do you define “more effective”

Maximize Reach of a campaign? Hit the sweet spot for frequency?

The Business Problem


Q: How do you attract an advertiser who is not currently

advertising on your network?

More steps

Case Study- 1

Step 1 A: You demonstrate to advertisers that allocating some of their advertising

dollars from other networks to yours will be “more effective” for them

Step 2 Q: How ?

A: You show them that combining some Ad spots from your network to their buy would be a “more effective” (optimal) way

Step 3

14

Business Opportunity

Optimization of Network Allocation has many positive Impacts for

both the Media Business and the advertiser


Improved Budget

Reallocation Accelerated

Client-Engagement

Increased revenue

Improved Ad slots

v isibility driv ing

cross-selling

$$$$ $$ $ $

$ $

$$ $ $

$ $

Simulated Campaign

Original Campaign

Ad_Slots in Client’s Network

Improved Campaign

Customer Reach/Frequency

Case Study- 1

15

Machine Learning Approach- Optimization


• The core Algorithm to be used is a Genetic Algorithm.

• It will simulate new campaigns substituting a part of the

advertiser’s (past) campaign with Ad_Slots from the

Clients Network

• We will to try to find a better (more optimal –

REACH/FREQUENCY) simulated campaign.

• Hence, we show that by switching some of the AD_Slots

we can find solutions that improves the campaign.

Let’s go through a basic example

Algorithm description:

Genetic algorithm is a

machine learning technique

that is used as a search heuristic by mimicking

natural selection

Case Study- 1

16

• Produce an initial population of individuals

• Evaluate the fitness of all individuals

• While termination condition not met Do

Select fitter indiv iduals for reproduction

Recombine between indiv iduals

Mutate indiv iduals

Evaluate the fitness of the modified indiv iduals

Generate a new population

• End While

Machine Learning Approach- Optimization


Let’s go through a basic example

• Small representative

sample of data

• Understand the nuances

in the data

• Determine the ML

algorithm that can be

leveraged

• Build a baseline desktop

version (prototype

creation)

• Train the prototype on the

sample data

First Prove Value- Develop

a Proof of Concept model

Case Study- 1

Algorithm description:

17

Machine Learning- Results


Business Value

• Reduction in effort

• Lead Generator to approach an advertiser

• Advertiser satisfaction and loyalty– direct measurement and improvement of Reach and Frequency

Case Study- 1

18

Machine Learning- Results


Technical Measures

• Automated a manual process

• Job parallelism in SPARK reduced run-time

• Created a repeatable process- Quarterly lead generator report

Case Study- 1

19

Q: Are there any other factors that might affect the

data?

A: Yes- location/population in a particular DMA

Business Problem and opportunity


Case Study- 2

Q: How do you measure degree of completeness for data

extracts to price effectively?

Step 1 A: Benchmark upcoming data with

historical data sources.

Step 2 Q: How do you Benchmark data? Data extracts will have

inherent differences like seasonality?

A: We get rid of seasonality.

Step 3

20

Business Problem and opportunity


Case Study- 2

Q: How do you measure degree of completeness for data

extracts to price effectively?

Increased trust with Business Partners

Improved knowledge of inventory for data Insights

Increased revenue

More steps

Step 1

Step 2

Step 3

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Machine Learning Approach and Results


Case Study- 2

Business Value

• Effective pricing and Increase in

Customer trust.

• Derived actionable insights to evaluate and improve data

quality.

• Led to an efficient data (useful

data completeness factor > 95%) storage process

Time Series Forecasting- Sample plot taken from Internet

22

Key Considerations


Questions?

24

Industry issue: Disintermediation


…traditional networks and MSOs are being cut out

25

Implications

1. More personalization

• more segmented, affordable video bundles ?

• Integrate video streaming service with broadband access ?

2. Deeper relationships with fans

• With so much competition across so many channels, you can’t just develop content to get the largest audience


26

Data implications

1. Challenges identifying people across devices

2. Poor data integration processes


clarity solution group presentation at the chief analytics officer, fall 2016

Data & Analytics