Bob Bress

1. Predictive Analytics for Yield Management
Bob Bress, VP, Analytics & Business Intelligence
Predictive Analytics World
May 16, 2017 – San Francisco, CA

2. 2
• Balancing advertising supply with demand
• Setting contracts against a price per thousand
impressions
• Guaranteeing impression delivery
• Minimizing over-delivery while monetizing the
full sale
• Accounting for varying levels of uncertainty
Predictive Analytics plays a critical
role in TV advertising.

3. Inventory Sellers Need Viewership Predictions
3
Impressions
Not Monetized
Day of Sale Day of Program
Inventory
Guaranteed at
this Rating

4. Prediction to Account for Risk
4

5. 5
• Package-in a diverse set of inventory
• Allow for as much delivery flexibility as possible
• Add sufficient buffer in impression estimates or rates
There are many ways inventory sellers
can manage risk of impression delivery.

6. 6
Various changes can affect
the accuracy of predictions.
• Programming changes on
the predicted network
• Competitive programming
changes
• Current Events
• Sports scheduling
• Network re-branding
• Expired carriage
agreements
• Cord-cutting

7. 7
The New Age of TV Advertising
Programmatic  Addressable  Non-Linear

8. 8
/,prōgrƏ’matik/ TV inventory planned, bought and
sold on impressions using system automation with
the use of advanced audience data, creating value
and operational efficiency for both the buy and sell
side.
Programmatic TV advertising is…

9. Programmatic Advertising
9
Automated
Proposal
Generation
Daily
Scheduling
Ad Insertion
Pacing
Measurement
Yield Optimization Engine
Reporting
Platform
Inventory
Supply
Viewership
Data
Advertiser
Demand

10. Programmatic Business Intelligence
Applications
10

11. 11
Household Addressable advertising is…
ə-ˈdre-sə-bəl Data-driven TV advertising
targeting to specific consumer households.

12. Household Addressable Advertising
12

13. Predicting Targeted Segments
13
Advertiser 1
Segment
Advertiser 2
Segment
Non-targeted
Segment

14. 14
Single Advertisers (Full Avail )
Multiple Target Segments
Audience-Focused
Same Avail used by Multiple Advertisers

15. Addressable Business Intelligence
15

16. 16
Non-Linear TV advertising is…
Advertising on time-shifted viewing (e.g.,
DVR, On-demand, OTT, etc.)

17. Schedule optimization yields media efficiency and/or greater
on-target impressions and message effectiveness
17
NetworksRankedby
AverageDailyRatings
Allocation Before Allocation After
Network 12a-6a 6a-12p 12p-6p 6p-12a
NICK 0.3% 0.2% 0.2% 0.4%
USA 0.4% 0.4% 0.4% 0.3%
FOXNC 1.1% 0.9% 0.7% 0.8%
TNT 1.0% 0.9% 0.7% 0.6%
TOON 0.6% 0.7% 0.7% 0.6%
HIST 0.4% 0.4% 0.4% 0.4%
HGTV 0.2% 0.4% 0.4% 0.4%
AEN 0.3% 0.4% 0.3% 0.3%
FX 0.2% 0.4% 0.4% 0.3%
AMC 0.6% 0.5% 0.5% 0.4%
TBSC 0.6% 0.5% 0.4% 0.4%
ESPN 0.8% 0.8% 0.5% 0.5%
FOOD 0.3% 0.6% 0.6% 0.5%
TLC 0.3% 0.5% 0.6% 0.5%
FAM 0.5% 0.3% 0.4% 0.4%
DISC 0.1% 0.3% 0.4% 0.4%
SPIKE 0.4% 0.4% 0.4% 0.4%
HALL 0.5% 0.5% 0.5% 0.4%
LIF 0.4% 0.4% 0.4% 0.4%
MTV 0.3% 0.3% 0.3% 0.2%
CMDY 0.9% 1.0% 0.8% 0.7%
MSNBC 1.1% 1.0% 0.8% 0.9%
BET 0.8% 0.7% 0.6% 0.7%
CNN 1.2% 1.1% 0.8% 0.8%
SYFY 0.9% 0.8% 0.8% 0.6%
ENT 0.2% 0.5% 0.4% 0.4%
APL 1.2% 1.0% 0.8% 0.9%
TRU 1.0% 0.9% 0.8% 0.7%
BRVO 0.6% 0.6% 0.6% 0.5%
TWC 0.4% 0.3% 0.3% 0.3%
TRAV 0.2% 0.4% 0.4% 0.4%
HLN 0.8% 0.7% 0.6% 0.6%
VH1 0.4% 0.4% 0.4% 0.4%
ESPN2 0.6% 0.5% 0.5% 0.4%
GOLF 0.6% 0.5% 0.5% 0.5%
CNBC 1.3% 1.0% 0.9% 0.9%
FS1 1.4% 1.2% 1.1% 1.2%
NBCSN 1.2% 1.0% 0.9% 0.9%
NFLN 1.1% 1.0% 1.0% 0.9%
BTN 0.6% 0.7% 0.7% 0.6%
CSC 1.1% 1.1% 1.0% 0.9%
Network 12a-6a 6a-12p 12p-6p 6p-12a
NICK 0.4% 0.2% 0.3% 0.5%
USA 0.5% 0.4% 0.4% 0.4%
FOXNC 1.3% 1.0% 0.9% 0.9%
TNT 1.2% 1.1% 0.9% 0.8%
TOON 0.8% 0.9% 0.9% 0.7%
HIST 0.4% 0.4% 0.4% 0.5%
HGTV 0.2% 0.4% 0.4% 0.4%
AEN 0.4% 0.4% 0.4% 0.4%
FX 0.3% 0.5% 0.5% 0.4%
AMC 0.8% 0.6% 0.6% 0.4%
TBSC 0.6% 0.5% 0.5% 0.4%
ESPN 0.9% 0.8% 0.6% 0.5%
FOOD 0.3% 0.6% 0.6% 0.5%
TLC 0.3% 0.6% 0.7% 0.7%
FAM 0.5% 0.4% 0.5% 0.4%
DISC 0.1% 0.3% 0.5% 0.4%
SPIKE 0.4% 0.5% 0.5% 0.4%
HALL 0.6% 0.6% 0.6% 0.5%
LIF 0.5% 0.5% 0.4% 0.4%
MTV 0.6% 0.6% 0.5% 0.4%
CMDY 1.1% 1.2% 1.0% 0.9%
MSNBC 1.3% 1.1% 1.0% 1.1%
BET 1.0% 0.9% 0.7% 0.8%
CNN 1.4% 1.3% 1.1% 1.1%
SYFY 1.1% 0.9% 0.9% 0.7%
ENT 0.3% 0.6% 0.5% 0.5%
APL 1.4% 1.2% 1.0% 1.0%
TRU 1.2% 1.1% 1.0% 0.9%
BRVO 0.7% 0.7% 0.7% 0.6%
TWC 0.7% 0.6% 0.6% 0.5%
TRAV 0.3% 0.5% 0.5% 0.5%
HLN 1.4% 1.1% 1.0% 1.0%
VH1 0.7% 0.0% 0.6% 0.6%
ESPN2 0.0% 0.6% 0.6% 0.5%
GOLF 0.0% 0.0% 0.8% 0.7%
CNBC 0.0% 1.2% 1.1% 1.1%
FS1 0.0% 0.0% 1.4% 1.5%
NBCSN 0.0% 0.0% 0.0% 1.1%
NFLN 0.0% 0.0% 0.0% 0.6%
BTN 0.0% 0.0% 0.0% 0.0%
CSC 0.0% 0.0% 0.0% 0.0%

18. Effective implementation of dynamic schedule optimization
resolves problems related to longer term forecasting
18
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
9/23
9/24
9/25
9/26
9/27
9/28
9/29
9/30
10/1
10/2
10/3
10/4
10/5
10/6
10/7
10/8
10/9
10/10
10/11
10/12
10/13
10/14
10/15
10/16
10/17
10/18
10/19
10/20
10/21
10/22
10/23
10/24
10/25
10/26
10/27
10/28
10/29
10/30
10/31
11/1
11/2
11/3
11/4
11/5
11/6
11/7
11/8
11/9
11/10
Cumulative % Off - Predicted in Advance Cumulative % Off - Schedule Optimized

19. 19
Actionable lessons learned
• Predictive analytics are key to forecasting viewership,
advertising demand, and valuation of inventory
• In order to automate a diverse set of prediction
problems - model robustness is key
• Ensemble methods can provide a good basis for a wide
range of prediction problems
• Capturing contextual knowledge directly can drive
predictive accuracy
• Real-time execution can beat the best forecasts