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| Key Drivers of TV Viewership -------------------------------- |
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The understanding
of key drivers of the Television viewership
is an important first step in predicting the
TV audience for a program, developing telecasting
schedules, audience classification & profiling,
and determining pricing strategies etc. An insight
into the key drivers and interpretation in game
theory framework can help a broadcasting channel
in operational and strategic decision-making.
The key drivers of the TV viewership can be
classified in the following two categories.
Drivers of TV audience in a competitive context
Drivers in the context of audience and TV program
profiling
This note addresses an approach to identifying
the key drivers in a competitive context.
Drivers of TV Audience in a competitive
context
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A. What drives the viewership of a program on
a given day?
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1. Genre of the program
2. Index of loyalty: 7th episode Vs. 1st episode
3. Availability of repeat telecast
4. Genre of the program on the competing channels
5. Index of loyalty of the programs on the competing channels
6. Availability of repeat telecast of the channels on
the competing channels
7. Disrupting events- a big cricket match, WTC collapse,
attack on parliament
8. Day of the week.
9. The signs of the Times. Dominant category. There are
times for romance, and there are times for revolt.
10. Availability of counter-disruption initiatives
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| B. Some hypothesis to be tested/issues to be
addressed: |
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1. Is the repeat-telecasts of the prime-time programs
a bad strategy? In a competitive context where a viewer may
want to see both the programs, availability of a repeat telecast
enables the viewer to switch to the program that does not have
repeat telecast choice. Besides the value of an audience in
repeat telecast slot is often much less.
2. Is all the channels' placing similar programs in the same
time slot an instance of Prisoner's dilemma or a case of Hotelling's
law in the Game Theory framework? The phenomenon where every
player has a strictly dominant strategy that leads to a very
bad outcome for all players is called the prisoners' dilemma.
The Hotelling's law states that in many markets it is rational
for all the producers to make their products as similar as possible.
3. Can the first mover advantage be quantified? What are strategic
options to neutralize it?
4. Is repeat telecast a good counter-disruption initiative?
If yes, how should it be planned? The options are:
a. To offer repeat telecast of only those episodes that face
major disruption.
b. Reschedule the episode facing a major disruption.
5. Determination of entry strategy: Dynamics of introducing
a new program, Strategies for inducing trial:
a. Should a Soap be countered with a Soap?
b. Is there a preferred timing for launch of a new program?
6. What are the relative strengths of different genre of programs
for a given slot? What is the monetary value of a given time
slot?
7. Is TV audience game a zero-sum game (fixed number of audience,
fixed amount of aggregate advertising spend) or a Variable pay-off
game (where a co-ordinations amongst the players can increase
the total pay-off)? What are the strategic options and theoretical
equilibriums in this game?
8. Exit or Re-scheduling strategy: When should a program be
taken off? Can it be rescheduled? If yes, what is the best re-scheduling
option?
9. Pricing strategy: Bundling, innovative pricing etc.
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| C. Data requirements: |
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For each time slot, for each channel:
i. Program on the channel
ii. Genre of the program
iii. Episode number
iv. Day and date of telecast
v. Preceding program
vi. Succeeding program
vii. Anti-disruption measures adopted
viii. Availability, and details (time, TRPs, advertising tariff,
advertising time) of repeat telecast
ix. TRPs reported
x. Advertising rates
xi. Advertising time in minutes
xii. Disrupting event
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| D. Modeling: |
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Building a relevant and clean database is the
first step to modeling. Various modeling tools and techniques
include Artificial Neural Networks, Decision Trees (Classification
and Regression Trees - CART), Chi Square Automatic Interaction
Detection (CHAID), Genetic Algorithms, Nearest Neighbor Method,
and Rule Induction. |
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| Broadcasters |
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Check yesterday's program performance
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Track viewership in special TGs
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Check on any lost business opportunity
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Keep daily track of channel distribution
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| Advertisers |
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Check yesterday's ad spot delivery
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Track viewership of spots in core TGs
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Monitor the efficiency of ad spends
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Media Planners/Buyers
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Make a media plan based on customized TGs
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Identify opportunities immediately by tracking
new channels/programs the next day
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Check audience deliveries for clients campaigns
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Keep daily track of the
aired ad spots vis-à-vis the ad schedule
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