I: Why Demand Analysis - Columbia Institute for Tele

Transcription

Demand
Analysis
For Media &
Information
Products
I.
OUTLINE: MEDIA DEMAND
ANALYSIS
WHY DEMAND ANALYSIS IV. DEMAND EXPERIMENTS
• Importance and Special Problems
of Media Demand Estimation
• Case Discussion: Viacom Golden
Years Media
II. ANALYTICAL/STATISTICAL
MODELS
•
•
•
•
Statistical Inference
Econometric Demand Estimation
Conjoint Analysis
Diffusion Models
• Test Marketing
• Uncontrolled Research
• Controlled Studies of Actual
Purchases
• Laboratory Purchase Experiments
• Psycho-Physiology Testing
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
III. EMPIRICAL SAMPLING OF
AUDIENCE/ CONSUMERS VI. MEASURING TRAFFIC
• Sampling Methods
VII. SELF-REPORTING
• Next Generation People Meter:
© Eli M. Noam, October 30, 2010
1
The Digital Meter System
• Metering Alternative: Cable Box
and Tivo Box
• Audience Metrics
• Qualitative Measures
• Auditing
VIII. CONCLUSIONS
• Is This What Media Firms23Need?
I: Why Demand
Analysis
Start of Lecture
21
24
http://www.sunways-direct.com/magnifying%20glass.JPG
The Media Value Chain
Resources:
HR
Tech
Finance
Accounting
of
Performance
Value
Creation:
C
ti
Strategy
Environment:
Production
Marketing
IP Creation
Pricing
Info.
Environment
Demand
Distribution
• In the previous chapter, we
concluded that one of the
characteristics of media
companies is the high risk,
uncertainty and instability of
demand for their products
Law &
Regulation
22
25
1
A famous Hollywood saying:
“Nobody knows Anything”
- William Goldman, (Columbia
MA ’56) Oscar-winning screenwriter
- (Butch Cassidy and the Sundance Kid;
All the President’s Men); Stepford
Wives, The Great Waldo Pepper;
Marathon Man; A Bridge Too Far; etc.
26
29
Case Discussion:
William
Goldman
“N b d
“Nobody
Knows
Anything.”
http://images.google.com/imgres?imgurl=http://www.wga.org/uploadedImages/news_and_events/101_screenplay/goldman_william.jpg&imgrefurl=http://www.
wga.org/subpage_newsevents.aspx%3Fid%3D1679&h=1525&w=1500&sz=1278&hl=en&start=12&tbnid=6TCls5WzVoSMtM:&tbnh=150&tbnw=148&prev=/i
mages%3Fq%3DWilliam%2BGoldman%26svnum%3D10%26hl%3Den%26lr%3D
A
Hypothetical
Case
30
27
http://www.bestchoicecare.com/library/images/tvcouple.jpg
Case Discussion:
“Viacom Golden Years
Media”
The Question now is:
• Is Goldman right?
– Does one really “never know
y
g
anything?”
• Or, more correctly, can one
know better?
• Can one increase the probability
of being right?
• Viacom is considering to enter the retirementage market
– Through multiple platforms:
¾Cable
¾C
bl Channel
Ch
l
(“Golden Years Channel”)
¾DVD (“Best of Golden Years”)
¾Magazine (“Golden Years”)
¾Website (“GY Portal”)
28
31
http://www.bestchoicecare.co
m/library/images/tvcouple.jpg
2
How would Viacom estimate and
measure its audience, their content
preferences, their consumption
preferences and their willingness to pay?
http://www.cdc.gov/communication/images/tv2.jpg
Other Viacom Channels
• Target Audiences:
–BET (African American)
– Logo (Gay)
–Sundance (film fans)
32
35
http://www.outsidein.co.uk/photos/sunray%20watching%20TV.jpg
Viacom’s Existing Cable
Channels
• Ordered by target audience age
– Noggin (pre-schoolers)
(pre schoolers)
– Nickelodeon (tweens)
– The N (teens)
– MTV, MTV2 (15+)
– mtvU (college)
33
Viacom’s Existing Cable
Channels (by target age)
– VH1 ((25+))
– Comedy Central (20+)
– Spike TV (30+)
– Nick at Nite (50+)
–TV Land (50+)
34
I.1. Importance
and
Special Problems
off Demand
D
d
Estimation for
Media Industries
37
3
Why Demand Analysis?
• Every industry & firm wants
to know
–Who its p
potential buyers
y are
–What their willingness to
pay is
–What their price sensitivity
38
is
- How to identify promotional
effectiveness
- How to identify market segments and
select target markets
• Etc
41
But it is Always Difficult To
Determine Demand
–What product features they
value
–What
Wh t th
they lik
like about
b t
competing products
39
- How to position its product
- How to plan the marketing and
promotion plan
- What
h the
h pricing
i i strategy should
h ld
be
- Deploy its sales force
- How to select and manage
40
distribution channels
• It’s easy to graph a hypothetical
demand curve in a theoretical
economics model
• But very hard in the real world to
determine actual nature of
demand, and the factors that go
42
into it
“Assume a
Demand Curve”
P
Q
But Where Exactly Is It?
43
4
Long Planning Horizons
Demand analysis is
particularly important
(and difficult) for media
and information firms
• Presence of non-maximizers
of profit who will supply
products
d t outside
t id the
th market
k t
• Continuous-flow products
(telecom services, cable TV,
newspapers, etc)
Why?
44
Recall the Fundamental Economic
Characteristics of Media
Long Planning Horizons
A. Supply Side
1.
High fixed costs, low marginal costs
2.
Convergent supply side
3.
Divergent cost in value chain
4.
Accelerating returns
5.
Excess supply
B Demand Side
B.
6.
Network effects
7.
Non-normal distribution of demand
C. Markets
8.
Price deflation
9.
Intangibles
10. Public goods
11. Non-maximizers of profit
12. Role of government
• require distribution networks,
strong economics of scale and
network effects andinvestment
f ahead
far
h d off actuall demand.
d
d
45
Eli M. Noam, Mobility, 2006
1. High Investment Needs
and Uncertainty
• Media content is expensive to
produce, is competitively
unique and has short shelf
unique,
life.
–Demand estimation is
essential to reduce risk of a
project
47
46
–Fiber-to-the-home
–Broadcast satellites
–Business plans
–IT equipment and semiconductors
48
Investment Uncertainty
• Outside investors must
evaluate projects (films, tech)
andd companies
i by
b evaluating
l ti
the quality of the demand
forecasts.
49
5
g. Indirect Transactions
http://realestatetomato.typepad.com/the_real_estate_tomato/80_20_principle.jpg
50
• “Public Good” characteristics
• Media products often given
awayy rather then sold to
identifiable users. (e.g.,
broadcasting)
–Audiences must be identified
53
for advertisers
2. Instability of Preferences
4. Unstable Markets
1. Content suppliers must be
able to rapidly respond to
changing
g g audience tastes
• “Excess supply”
• “Accelerating Returns”
• “Price Deflation”
• “Convergent Supply
Industries”
–“Convergence of suppliers”
51
3. Unique Products
• For each discrete-product media,
Product is unique
-Films, books, music
-Therefore separate marketing
“drives” necessary for each of
thousands of new products
• Many products are “intangibles” and
hard to evaluate in advance
52
54
5. Technology Change
• For “new media”
and applications
–Rapid
p change
g
of technology
–Short product
cycles
http://www.rmh.de/media/intemplate/4_anim.jpg
55
6
Technology Change (Cont.)
• No consumer experience
with many new products
– e.g., MP3 players,
l
video cellphones, etc.
• Techno-optimism
(“push”)by producers
Iridium
56
6. The Subjective Value of
Information
• Information is an experienced good.
Its value is only determined after
consumption
consumption.
• Thus, research revealing the value
of information prior to consumption
is important to media providers.
S. Rafaell and D.R. Raben. “Experimental Investigation of the Subjective Value of Information in Trading,”
in the Journal of the Association for Information Systems, Vol. 4, 2003, pp.119-139.
57
7. Supply Affects Demand
• Media create a buzz for their own
product and references shapes
audience
58
8. “Network Effect”
• Media demand is interdependent with that
of others:
– Telecom, Internet: benefits to users rise
with numbers of others on the network
–For Film, TV, Music, popular
Magazines and Books: often share
experience with peers; a major benefit
of media consumption is to be
connected with one’s peers.
59
Implications
• Leads to “extremes” of
success because of the way
users dynamically
y
y influence
each other.
De Vany and Walls, “Motion Picture Profit, The Stable Paretian
Hypothesis, and the Curse of the Superstar,” forthcoming in the Journal of 60
Economic Dynamics and Control, 2004.
“Network Effect”
• The average utility of the
service increases with the
number of other
participants Therefore,
participants.
Therefore the
demand increases with size
of networks. The more
people are on the network,
or share the experience, the
more people are willing to
pay.
Demand Curve
P
Q
61
7
• For these and other reasons,
demand analysis is
particularly important in the
media and information field.
• And particularity difficult
62
For more details see
Appendix A:
Special Problems in
Estimating Demand
B. Examples for the
Problems of
Forecasting Demand
63
66
Type I and
T
Type
II Errors
E
64
67
8
“Type I Errors”: The
wrong action is
taken (accept
hypothesis
incorrectly)( A
“false positive”)
Media Flops
Type I and Type II errors
Book Flops are Bi-partisan
68
71
A Type I Error is the false rejection of a true null. It has a probability of alpha
(α). In other words, this error occurs as a result of the fact that we have to
somehow separate probable from improbable.
72
www.uwsp.edu/PSYCH/stat/10
Picture Phones
• Most forecasts overestimate
the demand for products
rather than underestimate.
• Eternal optimism
Carey, John & Elton, Marin. “Forecasting demand for new consumer services:
challenges and alternatives.” New Infotainment Technologies in the Home. Demand70
Side Perspectives, Lawrence Erlbaum Associates, New Jersey, pp. 35-57.
• AT&T (1963): “There will be
10 million picture phones in
use by US households in
1980 ”
1980.
http://research.microsof
t.com/users/jckrumm/i
mages/picturephone%2
0head.jpg
73
9
Satellite Phones
• The Wall Street
Journal (1998): “The
consensus forecast by
media analysts is of
30 million satellite
phone subscribers by
2006.”
74
http://www.blueskynetwork.com/Images/Products/9500Pop.jpg
Estimating DVD Demand
http://images-eu.amazon.com/images/P/B0002VE5GW.02.LZZZZZZZ.jpg
75
Estimating DVD Demand
• In 2004, DreamWorks Animation
grossly over-estimated the DVD sales
for “Shrek 2.”
• Retailers returned millions of unsold
copies.
• DreamWorks fell short of earnings
forecasts by 25%
Merissa Marr, “How DreamWorks misjudged DVD sales of its monster
hit,” The Wall Street Journal, May 31, 2005 from Post-Gazette. 15, June
76
2005. http://www.post-gazette.com/pg/05151/513324.stm
“Type II Errors”: The
right action is not
taken
(reject hypothesis
incorrectly)( A “false
negative”)
78
The Telephone
• Western Union,
world’s largest
telecom company:
there is no market for
the telephone. (1877)
79
http://www.fmd.duke.edu/images/contacts.jpg
10
TV vs. Film
Type I and Type II errors
• Movie mogul Daryl Zanuck
“[Television] won’t be able to hold on to
any market it captures after the first six
months. People will soon get tired of
staring at a plywood box every night.
night ”
A Type II Error is the false retention of a false null. It has a probability equal to
beta (β).
Darryl Zanuck
20th Century Fox studios
chief; 1946
83
www.uwsp.edu/PSYCH/stat/10
Source: TIME, December 31, 1999
Film
http://www.reep.org/resources/adv2001/images/angels/old_tv1.jpg
TV vs. Radio
• Charlie Chaplin
(1916): “The cinema
is little more than a
f d What
fad.
Wh t audiences
di
really want to see is
flesh and blood on
the stage.”
• New York Times (1939): TV
will never compete with radio
since it requires families to
stare into a screen.
screen
81
84
http://www.doctormacro.com/Images/Chaplin,%20Charlie/Chaplin,%20Charlie%20(Gold%20Rush,%20The)_01.jpg
TV Invention
http://www.sfist.com/archives/images/old-TV-set.jpg
Computers
82
http://www.solarnavigator.net/inventors/inventor_images/John_Log
gie_Baird_young_man.jpg
“For God’s sake go down to
reception and get rid of a
lunatic who’s down there.
He says he’s got a machine
for seeing by wireless!
Watch him- he may have a
razor with him.”
-Editor of the Daily Express in
response to a visit by John Logie
Baird, 1925
• “I think there is a
world market for
maybe five
computers”
-Thomas Watson,
Chairman of IBM,
1943
Thomas Watson Library, Columbia
Business School
85
11
PC
Internet
• Ken Olsen, President, Digital
Equipment Corporation (1977):
“There is no reason anyone
would want a computer in their
home”
• “Two years from
now, spam will
be solved
solved.”
-Bill Gates, 2004
http://derstandard.at/?url=/?id=1979631
86
Source: http://www.digidome.nl/images/Ken_Olsen-1.jpg
89
Source: http://ceee.gwu.edu/school_reform/kids_computer72dpi.jpg
Cell Phones
• McKinsey (1981) study for
AT&T: there will be only
900,000 cell phones in use worldwide by the year 2000.
• Reality: almost 1 billion
87
90
http://www.3g.co.uk/PR/April2003/Brick.jpg
PC
• “640 kilobytes of
memory should
be enough for
anybody.”
- Bill Gates, 1992
http://derstandard.at/?url=/?id=1979631
88
Thus:
“Nobody
Knows
Anything””
(William Goldman,
Hollywood Pundit,
1983)
91
12
True?
• Yes, True
• But task is not to be exactly
right, but to reduce the
probability of Type I and
Type II errors
92
95
But we must also keep
asking the question: should
media companies use
demand estimation
techniques, like a car
manufacturer or an airline?
• To succeed against
competitors one need not be
always
l
right
i ht
• Just a little less wrong
93
This Is The Subject Of
This Unit:
• How media and
communications firms can
improve assessing the demand
for their products and services.
94
96
• Shouldn’t media creations be
based on
– artistic judgment
– news judgment
– public
bli responsibility
ibilit
97
13
Critiques of Audience
Research
• Is peoples’ demand shaping
media content?
• Or is media content shaping
peoples’ demand?
• Garrison Keillor:
“Guys in suits with charts”
have changed public
radio into an audiencedriven enterprise.
http://beyondwellbeing.com/al/garrison.keillor.gif
Alan G. Stavitsky, “Guys in Suits with Charts: Audience Research in U.S. Public Radio,” Journal of Broadcasting and98
Electronic Media, Spring 1995, pp/ 1-14
101
• Social Science and
communications research have
not resolved this question.
• There is a continuous back-andforth between explanations
whether “powerful media” or
“powerful audiences” determine
media content.
• Argues that the focus on
audiences has ruined radio’s
“i t ll t l andd morall
“intellectual
growth, passion, variety, and
pleasure.”
Stavitsky, Alan. “Guys in Suits with Charts: Audience Research in U.S. Public Radio.”
Aranet. Spring 1995. Journal of Broadcasting and Electronic Media. Last accessed99
on 7
June 2007 at http://www.aranet.com/library/pdf/doc-0088.pdf.
Sonia M. Livingstone, “The Rise and Fall of Audience Research: An Old Story
With a New Ending,” Journal of Communication; Autumn 1993; 43, 4.
102
Entertainment as Play
• Doesn’t media create its own
demand, by influencing
people
l andd their
th i preferences?
f
?
• Shouldn’t it be ahead of the
audience not following it?
100
• Psychological Theory: Desire
for entertainment is an effect
off ancestral
t l adaptations
d t ti
for
f
“pretend play.”
Francis F. Steen, Stephanie A. Owens, “Evolution’s Pedagogy: An Adaptationist Model of Pretense
and Entertainment,” Journal of Cognition and Culture 1. 4 (2004): 289-321.
103
14
• Evolutionary psychology: desire
for “play” is an intrinsic human
character, because it is a crucial
feature and skill for human
survival
survival.
http://www.stpeteha.org/images/Children%20pla
ying%20on%20sidewalk.jpg
Peter Vorderer, Christoph Klimmt, Ute Ritterfeld, “Enjoyment: At the Heart of
Media Entertainment,” Communication Theory 14:4, November 2004
political part of
communications
research (e.g.,
Frankfurt School)
believes in allpowerful media
Max Horkheimer (L) and Theodor Adomo (R)
104
• Entertainment is a form of
“pretend play,” allowing
people to gain experience that
they can use in future
challenging situations.
–Like a simulation
Francis F. Steen, Stephanie A. Owens, “Evolution’s Pedagogy: An Adaptationist Model of Pretense
and Entertainment,” Journal of Cognition and Culture 1. 4 (2004): 289-321.
In Contrast, the Perspective of the
“Political Economy” and “Critical
Studies”
• The more
105
Predator Evasion
• Like in play-chase games,
where one functionally learns
strategic skills to evade or
defeat a predator or adversary
106
http://www.arikah.com/encyclopedia/Theodor_Adorno
107
The “Nielsen Approach”: the
powerful audience
• Audience preferences govern
• Media companies satisfy
these preferences
108
The Approach of “Cultural
Studies”: A synthesis
–Media “texts” are not passively
accepted by the audience.
audience activity is involved in the
–audience
“encoding” process.
• The meaning of media texts depends
on the cultural background of the
audience. (“Interpretive Communities”)
109
15
110
• For purposes of media
managment, both major
perspectives are correct
• Media audiences have preferences
that can be analyzed
y
-This is called“ Media Research“
• But these preferences can also be
influnced
-This is called “Media Marketing“ 111
113
The Late 1930s
• Study of modern
communications started.
• Became a new branch of
social sciences
Czitrom, Daniel. Media and the American Mind. Chapel Hill: University of North114
Carolina Press, 1983, p. 122-146.
Audience Preference Research
112
http://www.cba.unl.edu/about/publications//emag/Volume2/Issue1/images/ggallup.jpg
• The first
audience
studies were
performed
f
d bby
George Gallup
when teaching
psychology in
Iowa.
• This chapter deals with
“Media Research“
• Later, we will deal with
Media Marketing
Dennis, Wayne. Current Trends in Social Psychology. Pittsburgh: University of 115
Pittsburgh, 1948, p. 218-273.
16
Paul Lazarsfeld
• A central figure in
the development of
marketing studies
in the 1930s.
• Emigrated to the
United States and
started an institute
at Columbia to
research radio.
For Further Details see
Appendix H:
Behavioral Economics
http://www.fathom.com/feature/35683/1576_Lazersfeld_lg.jpg
Czitrom, Daniel. Media and the American Mind. Chapel Hill: University of North116
Carolina Press, 1983, p. 122-146.
117
For Details see
Appendix B:
Demand for Media:
Deeper Motivation
118
119
120
How Media Companies
Organize their Demand
Research
121
17
Viacom’s Research Focus (from
it’s Annual Report)
• Audience acceptance of programs
• Effectiveness of expenditures by
advertisers.
d
i
• Effectiveness of media co’s own
promotion
Source: Viacom 2006 report
122
125
I.
• Large media companies
engage in substantial
audience
di
researchh att every
step
• [Details]
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
123
• Seven distinct types of research
1. Concept testing
2. Positioning Studies
3. Focus group tests
4. Test screenings
5. Tracking surveys
6. Advertising testing
7. Exit surveys
Robert Marich, “Marketing to Moviegoers” Elsevier, “Distribution to Theaters”
ANALYSIS
and Tivo Box
• Auditing
VIII. CONCLUSIONS
126Need?
• Is This What Media Firms
II. Analytical
& Statistical
Models
124
127
18
And what do media
researchers do?
IV.5. PsychoPhysiology Testing
• We will now discuss a number
of techniques for analyzing
demand
demand.
128
• Approaches range from
• a hands-on physiological/medical
• to abstract statistical, analytical,
model building technique
129
131
Measuring the
audience’s
physiological
h i l i l
response to a
media experience.
132
A. Heart Rate (HR)
• On the one extreme, PsychoPhysiology
y
gy Testingg
http://josephhall.org/images/bp_hrt.jpg
130
Niklas Ravaha, “Contributions of Psychophysiology to Media Research: Review and
133
Recommendations, ” MEDIA PSYCHOLOGY, Vol. 6 No. 2, 2004, pp. 193–235.
19
B. Electrodermal Activity
(EDA)
•
Electrodermal Activity (EDA)
http://www.electrodermology.com/pics-new/biotronprobe-drop.jpg
• Skin conductance of electricity increases
when sweat increases due to arousal.
http://web.axelero.hu/lavender/kpt/hallgatokhoz/vassy/weboldal/H7KLFI1.JPG
134
Electrodermal Activity (EDA)
• Measures responses to various
stimuli (sudden noise,
emotionally charged visuals,
pain, anxiety, fear, guilt etc.)
http://www.bsu.edu/web/00t0holtgrav/317/physio.ppt#6
EDA measures of “before”, “during”, and “after” responses to an
137
emotional picture and a calm picture
Facial electromyography
(EMG)
• An electromyograph detects
the electrical potential
generated by muscle cells
when cells contract.
135
138
http://www.Wikipedia.org
D. Respiratory sinus arrhythmia
irregularity
• Index of parasympathetic nervous
system (PNS), that can be related to
emotion.
http://www.wearable.ethz.ch/education/sada/Emotion-Board
136
139
http://www.biosvyaz.com/Htm_En/Sl_En/Sl02E03.gif
20
F. Electroencephalographic
(EEG) Activity
• Measures
brainwaves using
electrodes.
l t d
• Usually, no single
psychophysiological method
is enough. Often several
methods are used to identify
diff
different
responses.
http://www.nexstim.com/images/prod_eeg_01.jpg
140
http://www.blackwellpublishing.com/abstract.asp?aid=161&iid=4&ref=0956-7976&vid=10
143
Electroencephalographic
(EEG) Activity
• Emotions can be observed by
frontal EEG activity
• On the other extreme from these
pphysiological
y
g
experimentation
p
is
statistical model building
http://membres.lycos.fr/choppin/research/emotexprinterf.gif
141
144
http://www.blackwellpublishing.com/abstract.asp?aid=161&iid=4&ref=0956-7976&vid=10
• The first 3 of these
measures are easily
applicable
li bl andd mostt
commonly used in media
research.
142
Analytical & Statistical
Models
A.
A
B.
C.
D.
Statistical Interference
Econometric Modeling
Conjoint Analysis
Diffusion Models
145
21
Reasons for sampling instead
of doing a population census
146
–Cheaper
–Faster
–More ppractical
• But:
–Incomplete coverage
–Respondents could be
unrepresentative of
population
149
Population: The entire
group we are interested in
Example: US Households
Sample: Smaller group
selected for observation
Example: Nielsen panels
II.1. Statistical
Inference
147
Audience Research Methods
1930: Methods
developed by Paul
Lazarsfeld,, Bureau of
Applied Social
Research, Columbia
University; and Frank
Stanton, CBSDied December 2006
150
How Do We Get From a
Sample to an Estimate of the
Overall Population Parameter?
•Suppose one takes 3 independent samples of the
same population.
•Question: Did you watch last week the “Golden
Golden
Age” Channel?
•But the samples may not be representative.
Paul Lazarsfeld,
Columbia
Population: 300 Million people
Sample 2
148
Frank Stanton, CBS
5000 people
Sample 1
Sample 3
5000 people
5000 people
151
22
Percent Watching
GYC
Sampling Statistics
• Sampling results would differ
slightly, “luck of the draw”
• But one would expect that all
three samples
p would yield
y
a
similar estimate because drawn
from the same population
- Sample 1: p = 25%
- Sample 2: p = 27%
- Sample 3: p = 24%
152
n=10
n=5
States that the distribution of a variable found
in a sample approaches a “normal”
“
distribution
as the number of samples increases
n=15
155
But need to consider the
probability of a sampling error
Central Limit Theorem
n=2
p̂ = sample
proportion
n = sample
p
size
x = positive
1250
response
pˆ =
= 0.25 or 25%
5000
x
pˆ =
n
n=40
153
Case Discussion
• How many viewers tuned into
the “Golden Years Channel”
last week? The Nielsen panel
has 5000 households and
1250 of them say they
watched at least some of GYC
last month.
154
p = pˆ ± e
•Where
p̂ : audience share in sample
p: audience share in the population
e: margin of error
156
Sampling Error
•Sampling error (e)
–gives us some idea of the
pprecision of our statistical
estimate.
157
23
Potential Error in Estimate
• (e) = potential error,
due to sample being
“off”
• z-score: indicates how
far an item is deviated
from its distribution
mean
•Population is • (p) = proportion that
large compared answered positively
to sample size • q=(1-p) those who 158
answered negatively
e=z
pq
n
• Only the sample size has any
effect on the margin of error
• The larger the sample size, the
smaller the potential for error
e=z
e decreases
pq
n
Case Discussion GYC…
e = 1.96
.25 × .75
=.012 or 1.2%
5000
161
Estimated Audience
159
• Assume 100 million HH in the
US, then the number of
American HHs that watched
GYC last month
–With 95% certainty
–Lies between 23.8 and 26.2
million (25 mil ± 1.2 mil) 162
160
163
If n increases
Suppose these are the
parameters
p=.25 (25% of sample watched)
q=.75 (75% did not watch)
nn=5000
5000 (sample size)
z=1.96 for a 95% probability
24
For Details see
Appendix
ppe d C:
Sampling
From such relatively simple
statistical tools with a simple
variable as a yes/no binary choice
were expanded to multivariable
analytical methods
164
167
II.2.
Econometric
Demand
Estimation
165
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
and Tivo Box
168
Econometrics is Estimation of
Statistical Relations of Several
Variables
• Method requires cross-section
over multiple data points or
time series analysis
• Auditing
VIII. CONCLUSIONS
166Need?
169
25
• Synthesize large amounts of info
in an effective way
• provides framework for
systematic thought
– assumptions explicit
170
Ordinary Least Squares (OLS)
• Use linear regression models
to quantify linear relationships
among variables
• Can estimate OLS regression
using statistical software
packages (STATA, SAS,
EXCEL, Minitab, etc.)
173
http://www.chass.utoronto.ca/~murdockj/eco310/F03_310_six.pdf
• Can use numerous variables
• Identify, track, and model key
variables (price, competition,
etc.) that affect demand, and
put them together in different
scenarios
171
Typical Regression Analysis
Unit sales = a + b1 price + b2 advertising +
bi other variables + e
or
Market share = a + b1 lagged market share
+ b2 price + bi other variables + e
Thomas T. Nagle & Reed K. Holden, “The Strategy and Tactics of Pricing: A Guide174
to
Profitable Decision Making,” Second Edition 1995
Other Control Variables
• Adding variables that might
affect sales, such as
–Growth in GNP
–Growth in population
–Season
–Income level
172
to
26
• Demographic characteristics
include age, education, gender,
marital status
• Psychographic characteristics are
concerned with the individual’s
lifestyle preferences- their
activities, interests and opinions,
which marketers refer to as
consumer AIOs.
176
Lots of Different Models For
Logarithmic Models
Sales=
1
2
a (price) b(advertising) b (other variables)
Which is the equivalent of
ln sales =
ln a + b ln price + b advertising + b ln other + u
1
2
i
[ln is the “natural logarithm”]
179
http://www.amosweb.com/images/ElDm33c.gif
177
• The coefficients of the
logarithmic models are the
elasticities
l ti iti (here
(h off sales
l with
ith
respect to price, advertising
expenditures, etc.) and to
other variables
178
• OLS
• Inverse
Stone-Geary
Geary
• Stone
• Quadratic
• Stochastic
• Discrete
• Dynamic
• Inter-temporal
•Engel
g
•Log-linear
•Semi-log
•Constant elasticity
•2 stage least
square
•Etc., etc.
180
A. Estimation of
Demand Curves
Measuring Price Sensitivity
181
27
Example: Demand Estimation
for Newsprint (paper)
- For newspapers,
directories etc
etc.
http://homepage.mac.com/albertkwa
n/Chronicle_Blog/C1258471436/E1
867671640/Media/newspaper%20ro
ll.gif
http://www.andrewdegrandpre.com/newspaper_roll_centered1.jpg
Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,”
182
supported by Fisher Center for the Strategic Use of Information Technology.
• Of great importance to
newspaper companies:
- What will be the price of
newsprint paper?
• Also of great importance to paper
and forestry companies which
must make long-term
investments in new trees.
183
Approaches to Forecast
Newsprint Demand
1. The classical model: (FAO model)
(UN’s Food & Agriculture
Organization)
g
) estimated demand for
newsprint as based on income levels
(GDP)
• Since GDP is rising, demand is also
rising
184
Trends
• But in fact the newsprint
demand turned negative after
1987, despite rising GDP.
• So FAO model
did not predict
well
http://unadorned.org/morningpaper/images/papers/mp_200
30707_2.jpg
185
A Second Model: the
“Regional Plan Association
(RPA) Model”
186
• “Print media price index” –
calculates the impact of changes
in print industry input prices,
which affects the printing and
publishing industries, and in
turn newsprint demand
187
28
Here is how the two
models describe the past
and project the future
188
3rd Model:
ln(d news ,t ) = γ 0 + γ 1Δ ln(circnews ,t ) + γ 2 ln(d news ,t −1 ) + μt
191
• A 1% increase in newspaper
circulation would lead to a very
large increase (3
(3.1%)
1%) in demand
for newsprint
Figure 1. US Newsprint Consumption Projections: FAO (1995-2010 and
RPA (2001-2020)
189
3rd Model Type
Newspaper Circulation Model
• Looks to newspaper
circulation to explain changes
i the
in
th newsprint
i t market.
k t
• Since 1987, there has been a
decline in the volume of
newspaper circulation.
190
Lauri Hetemäki & Michael Obersteiner, “US Newsprint Demand Forecasts to 2020,
”
192
• Several variants of the
(M d l #4
(Models
#4,8,9)
8 9) explain
l i
demand still better
193
29
Lauri Hetemäki & Michael Obersteiner, US Newsprint Demand
Forecasts to 2020, p.30.
194
197
http://i93.photobucket.com/albums/l60/stoy17/Ted/TedSaluteSlideSho.jpg
Demand for Live
Entertainment
• Model: Ui= f(Lei, OGi, zi)
• Ui is the utility of the person i
• LE is the “vector of live entertainment
purchased in the market
market.”
• OG is the “vector of other goods
purchased in the market.”
• Z is the overall tastes pattern of the
people.
195
Cameron, Samuel. “Determinants of the Demand for Live Entertainments: some survey198
based evidence.” Economic Issues 11, no. 2 (2006): 51-64.
Demand for Live Entertainments
Econometric Example
#2 Li
#2:
Live Entertainment
E t t i
t
196
30
Price
• The findings for
price were
interesting.
• The coefficient
for price was
negative for
males but positive
for females.
http://new.krcgonline.com/uploadedImages/Shared/Shows/Price_Is_Right_Logo.jpg
Dependent Variable = 1 If attend > 12 or more events per year; 0 otherwise. Estimation method: ML
Coefficient
Standard Error
LEEDS (dummy = 1 for Leeds)
Variable
-.940
1.405
TVHRS (hours of TV watched per week)
.036
.032
RADIOHRS (hours of radio watched per week)
-.009
.022
ALONE (dummy=1 if regularly attends events alone)
-.515
1.616
NUMPARTY (number of people in a party for an evening out)
.076
.108
URGE (maximum
(
i
price
i would
ld ever pay ffor a ti
ticket
k t di
divided
id d by
b
-.005
005
.005
005
RSNPRICE)x100
RSNPRICE (idea of a reasonable price for a ticket for an evening out)
FEMALE (dummy=1 if female)
-.172
.100
-17.915
7.928
1.355
SINGLE (currently single)
1.658
GROSSINC (gross income of family unit)
.000
.000
NOCCUP (no current occupation)
-.611
1.300
DEGPLUS (highest qualification is a degree)
-.351
.875
AGE
-.272
.158
AGESQ
.003
.002
Demand for Live
Entertainment
• Findings: income effects were not
noticeable; going alone or in a large
party did not have an effect
effect.
• Age did not have a significant effect
either.
• As people get older they may go to
less rock concerts but to more operas
204
Econometric
Example #3 What are
the Effects of General
Economy on
Advertising Volume?
205
31
• Print media most affected by
GDP
• 15% decline for 1% decline of
GDP on average
GDP,
–in US lower effect of GDP,
only 5.5% for newspapers,
2.5% for magazines
206
• An econometric study of 8
major countries (Picard 2001)
fi d that
finds
th t advertising
d ti i
spending declines 5% for
each 1% reduction in GDP.
209
• Electronic media less affected
–4% TV (US, 3%)
–8% radio (US, 2.5%)
http://wifinetnews.com/images/reciva_net_radio.jpg
http://images.amazon.com/images/P/B00061ZNV
E.01.LZZZZZZZ.jpg
207
210
Effects of General Economy of
Advertising (cont.)
• Strong correlation found for
Germany, Spain, Italy,
Fi l d
Finland
• Moderate correlations: UK
France
• Low correlation: Japan
208
211
32
Variables of the Demand Model
•
•
•
•
•
•
•Econometric
Example #4:
Competing Video
Games
212
Qit – firm i’s demand at time t;
Pit – firm i’s price at time t;
Ait – firm i’s advertising expenditures at time t.
α – parameter for brand-specific effects
η and β – own price and advertising elasticities
ε and γ – cross-price and cross-advertising
elasticities.
Shankar, Venkatesh and Barry L. Bayus. “Network Effects and Competition: An
Empirical Analysis of the Home Video Game Industry”, January 2002.
Nintendo and Sega
Parameters
• Assume both Nintendo and Sega
are competing in the home video
game industry.
industry Either Nintendo
Nintendo’ss
or Sega’s demand is determined
by both firms’ current prices and
advertising expenditures.
• βit < 1, diminishing marginal
returns to advertising
• γit < 1,
1 diminishing marginal
returns to advertising
ηit > 1, εit > 1, own price elasticity
The Demand Model
Price Sensitivity
• In a situation of two competing
home video game firms, the
demand model for each firm is:
• Sega’s price sensitivity is
relatively smaller than Nintendo’s,
because customers are more
willing to pay more for a product
technology supported by a large
network of users.
33
Advertising Effectiveness
• Similarly, Sega’s advertising is
also more effective compared to
that of Nintendo
Nintendo, because bigger
company can maintain its demand
with less advertising expenditures.
• Studios estimate film’s revenues
based on previews, the
performance of previous movies
into the same genre, with the same
talent, similar characters, etc.
• Models based on life-cycle of
221
similar movies.
Computer Models for
Predictive Film Success
Strategic Interaction
• This kind of competition between
two firms contains strategic
interaction So both firms may
interaction.
want to actively manage and
leverage its customer base.
• Motion Picture
Intelligencer
g
((MIP))
• MOVIEMOD
• Many others
http://www.adangio.com/galleryImg/large/movie175.jpg
Daniel B. Wood, “Can Computer Help Hollywood Pick Hits?” The
222
Christian Science Monitor, January 3, 1997, p.1
•
Example #5: Modeling
Film Box
Bo Office
Tool to help
strategy based
on the ticketb i
buying
behaviors of
past movies
http://www.nyjet.com/move%20tickets.jpg
220
Daniel B. Wood, “Can Computer Help Hollywood Pick Hits?” The
223
Christian Science Monitor, January 3, 1997, p.1
34
• Models to predict which movie
scripts will be hits and which will
be flops
“Revenge of the Nerds’ Part V: Can Computer Models Help Select Better Movie
Scripts?”Knowledge@Wharton, 29, November 2006. University of Pennsylvania
224
How do the models work?
• The methods behind the
models are proprietary and
unisclosed.
i l d
“'Revenge of the Nerds,' Part V: Can Computer Models Help Select Better Movie Scripts?”
Knowledge@Wharton. 29 November 2006. University of Pennsylvania.
• MIP tries to factor in
advertising expenditures,
number of theaters used in a
release, time of year of the
release or competition from
release,
other movies.
• Based on ticket-buying
behaviors for past movies.
225
Behavioral Representation of Consumer
Adoption Process in MOVIEMOD
Eliashberg, Jehoshua; Jonker, Jedid-jah; Sawhney, Mohanbir S.; Wierenga, Berend.
“MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation
of Motion Pictures.” Marketing Science, Summer2000, Vol. 19 Issue 3, p226-243, 18p, 9 226
charts,
1 diagram; (AN 3623791)
227
MOVIEMOD
• Unlike other forecasting models for
films, MOVIEMOD needs no actual
sales data.
– But surveyy data from focus
groups
Mhanbir S. Sawhney, Jehoshua Eliashberg, Jedid-Jah Jonker, Berend Wierenga. “MOVIEMOD: An Implementable 228
Decision
Support
System
for Prerelease
Market
Erasmus
Universiteit
Rotterdam, December
1997 Evaluation of Motion Pictures” in Marketing Science. Vol. 19, No. 3, 2000
MOVIEMOD
• Subjects are exposed to
different sets of information
stimuli and are actually shown
th movie.
the
i
• They fill out post-movie
evaluations, including word-ofmouth intentions.
229
Mhanbir S. Sawhney, Jehoshua Eliashberg, Jedid-Jah Jonker, Berend Wierenga. “MOVIEMOD: An Implementable Decision
Support System for Prerelease Market Evaluation of Motion Pictures” in Marketing Science. Vol. 19, No. 3, 2000
35
MOVIEMOD
• These measures are used to
estimate the word-of-mouth
parameters and other behavioral
factors, as well as the moviespecific parameters of the
model.
233
Mhanbir S. Sawhney, Jehoshua Eliashberg, Jedid-Jah Jonker, Berend Wierenga. “MOVIEMOD: An Implementable 230
Decision
Support System for Prerelease Market Evaluation of Motion Pictures” in Marketing Science. Vol. 19, No. 3, 2000
MOVIEMOD
• The heart of MOVIEMOD is an
interactive Markov chain model
describing the macro-flow process.
– allows to account for word-ofmouth spreaders in the population.
“MOVIEMOD: An Implementable Decision-Support System for Prerelease Market
231
Evaluation of Motion Pictures.” Marketing Science, Summer2000, Vol. 19 Issue 3,
p226-243, 18p, 9 charts, 1 diagram; (AN 3623791)
Claims: The Dutch Application of
MOVIEMOD
• Managers used MOVIEMOD to identify
a final plan that resulted in an almost 50%
increase in the test movie’s revenue
performance
• The box-office sales resulted from the
final plan were within 5% of the
MOVIEMOD prediction
“MOVIEMOD: An Implementable Decision-Support System for Prerelease Market
232
Evaluation of Motion Pictures.” Marketing Science, Summer2000, Vol. 19 Issue 3,
p226-243, 18p, 9 charts, 1 diagram; (AN 3623791)
Problems of Econometric
Demand Estimation
• Data
–Often insufficient
–Often unreliable
234
• Need to assume a specific
mathematical model for the
relationship between price and sale.
• If specification is incorrect, the
results will be incorrect
• Predicting the future requires
assumption that behavior is like the
past.
to
36
Demand Estimation
• Econometric problems
–Serial correlation
–Multicollinearity
Multicollinearity
–Homoscedasticity
–lags
–exogeneity
236
Demand Estimation
239
Case Discussion:
• Results
–statistically significant?
–conclusion
conclusion justified?
–Can one claim causality
–stable over time, for
forecasting?
• How can Viacom use
econometric techniques to
estimate the demand for its
Golden Years Channel?
237
240
• A simple demand model could
be specified like this:
Likelihood of watching the Golden
Years Channel=
α + β1 ln age
g + β 2 ln income + β 3 ln education +
γ 1 adventure +γ 2 romance +γ 3 sports +
γ 4 documentaries/news + y1 primetime +
y2 daytime + y3late night + u +
e median age in zip code + f i other
238
241
37
• The coefficients that are estimated
are
βi = own-price elasticities to age, income,
education
δ= cross elasticity to other types of
channels
g
e = “network effect”
f = effect of other factors z
u = error term
242
Y = time of day
Measuring the Price
Elasticity of Demand:
this is discussed in
detail in the Chapter on
“Pricing.”
245
• Some of the “other factors”
could be dummy variables for
yes/no of some factors, such
as “rural location,” “Latino” or
“living single.”
243
For Details see
Appendix D:
Econometric
Estimation
246
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
244
and Tivo Box
• Auditing
VIII. CONCLUSIONS
247Need?
38
• Researcher asks respondent
to make choices between
different levels of two
product attributes
attributes.
II.3. Conjoint
A l i
Analysis
248
Trade-off Analysis – Conjoint
Analysis
• Helps disaggregate a product
into the value given for each
attribute
tt ib t by
b consumers.
Thomas T. Nagle & Reed K. Holden, “The Strategy and Tactics of Pricing: A
Guide to Profitable Decision Making,” Second Edition 1995
249
251
• Permits the researcher to
identify the value (utility)
that a consumer attaches to
each product attribute
252
• The value of a product is
equal to the sum of the utility
the consumers derive from all
the attributes of the product.
• Developed initially by Paul
E. Green and Vithala R. Rao,
“Conjoint Measurement for
Quantifying Judgmental
Data,” Journal of Marketing
Research 8 (August 1971)
250
253
P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php
39
Golden Media
• This enables the researcher
to predict the prices which
the consumer would pay for
a product of various
combinations of attributes.
• How could Viacom make use
of conjoint analysis for its
“G ld Y
“Golden
Years”” channel?
h
l?
• There are computer packages
(i.e. ACATM, Adaptive Conjoint
Analysis) that generate an
optimal set of trade-off
questions
i
andd interprets
i
results.
l
255
Example #1: Attribute-Importance
Study For MP3 Player
(Scale 1-10)
Attribute:
Quality:
8.24
Styling:
6.11
Price:
2.67
User Friendliness:
7.84
Battery Life:
4.20
Thomas
T. Nagle & Reed
K. Holden, “The Strategy and Tactics
of Pricing: A Guide256
to
Customer
Service:
5.66
257
254
Golden Media
• A cable company is considering
which package to offer to its
customers aged 65+. These vary
in:
– Price of package ($30-50)
– Movie frequency (1-4)
– Golden Media channel (yes/no)
– Other channels (10-40)
258
Cable TV Package Options
Levels of attributes measured in survey
Attribute
Movie frequency
Level
1 per day
2 per day
Golden Age
channel
Yes
No
Price of package
$30
$14
$50
10 channels
20 channels
30 channels
Other channels
Source: According to P&B LLC DBA POPULUS
http://www.populus.com/techpapers/conjoint.php
3 per day
4 per day
40 channels
259
40
Conjoint Tasks
Example: Cable TV Packages
• Once data have been collected,
participants are given to choose from
pairs of cable channels (conjoint
tasks)
tasks).
• Each profile describes 2-4 attributes.
Participants are asked which of the
two channel descriptions they prefer
more.
260
P&B LLC DBA POPULUS http://www.populus.com/techpapers/conjoint.php
Respondent’s utilities for selected packages II
Package Configuration
Nr. Other
channels
Utilities
Overall
Utility
Golden Movie aired Price
frequency
Age
channel
1
4 channels Yes
2 per day
$14
.471 + .769 + .271 + .035 =
1.546
2
4 channels No
3 per day
$12
.471 + .231 + .311 + .217 =
1.230
3
3 channels Yes
1 per day
$12
.403 + .769 + .103 + .217 =
1.492
4
3 channels No
4 per day
$12
.403 + .231 + .315 + .217 =
1.166
5
2 channels Yes
4 per day
$10
.125 + .769 + .315 + .738 =
1.947
6
2 channels No
3 per day
$10
.125 + .231 + .311 + .738 =
1.405
7
1 channel
2 per day
$10
.001 + .769 + .271 + .738 =
1.779
8
1 According
channel toNo
per day
$10
Source:
P&B LLC3DBA
POPULUS
.001 + .231 + .311 + .738 =
1.281
263
Yes
Computation of utilities
• Utilities are then calculated
by a statistical program.
Source: Kotler (1997), Marketing Management
• First package would have
been the most attractive in
t
terms
off content,
t t but
b t the
th price
i
is too high.
261
Respondent’s utilities for
selected packages I
264
• The configuration package number
5 with the lowest price, 20 extra
channels, the Golden Age channel,
and a movie frequency of 3 per day
is the most preferred, and most
likely to be chosen by the senior
consumer.
• For each package the overall
utilityy is calculated.
• Overall utility = Sum of all
weighted average utilities
262
265
41
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
266
For Further Details
see Appendix
pp
E:
Conjoint Analysis
267
and Tivo Box
• Auditing
VIII. CONCLUSIONS
269Need?
II.4. Diffusion
M d l
Models
270
Generally, adoption of a new
product follows an S-Curve
Pattern
268
271
42
S-Curve Pattern of Adoption
• The S-Curve helps to illustrate and
to predict how a new product will
be accepted by the population
• The S-shaped curve of adoption
rises slowly at first when there are
few adopters
• Also known as an “epidemic
model.” A “logistic” function
y(t)
(t) = N{1+0 exp [-kt]}
[ kt]}
272
General Formula of the SCurve
a
Cumulative sales =
1 + be− kt
where t is time and a, b and k
are constants.
275
• Example: Adoption of
Blue-Ray DVD
• Example: knowledge of a
hit movie
McBurney, Peter, Parsons, Simon & Green, Jeremy. “Forecasting market demand for
new telecommunications services: an introduction.” Telematics and Informatics 19,
273no.
3 (2002): 225-249.
276
Viral Marketing operates on an
S-Curve
• Knowledge of the given thing will
spread like a “virus” epidemic
Wilson, Ralph. “The Six Simple Principles of Viral Marketing.” WilsonWeb. 1 February
2005. Last Accessed on 31 May 2007 at http://www.wilsonweb.com/wmt5/viral- 274
principles.htm.
• With different parameters,
different S-shapes occur
• One
O hhas tto ddetermine,
t
i from
f
early data, what the parameters
are, for a projection of the rest
of the S-curve.
277
43
Market Growth Curves
Example #1: DVD vs. VHS
• Can the diffusion of DVD
be compared to the
diffusion of VHS?
278
Problems
281
• VHS is in 95% of US HHs in
2008 (= Maximum Market
Demand);
–DVD penetration was 75%, in
2008.
• finding acceleration point
• finding
fi di th
the “saturation
“ t ti level”
l l”
challenges and alternatives.” New Infotainment Technologies in the Home. Demand279
282
• comparison of the product to
be forecast with some earlier
pproduct that is believed to
have been similar
280
283
44
75 x 100
= 79%
–Thus, the HDI =
95
– Thus, the DVD market is
still 21% below its potential.
• Problems with the diffusion
approach: There are too
many differentiating
diff
ti ti variables
i bl
to make comparisons among
products have a strong
predictive value.
284
287
• VCR reached 75% after 12
years. DVD took only 6 years.
Hence DVD penetration rate is
2x faster than that of VCR.
• Since VCR took 3 years to rise
from 75% to 95%
- hence, DVD is likely to take
only 4/2 =2 years to reach 90%285
• For Blu-ray DVD, can one
make similar comparisons to
DVD
• But, maybe consumers do not
value HD much over SD
quality?
286
Case
Discussion:
289
http://www.bestchoicecare.com/library/images/tvcouple.jpg
45
Modeling the Market (III)
Case Question: How would
“Golden Years” estimate and
measure its audience?
3. Aggregate TV Hours by Cohort (# of
average TV hours/day x cohort)
Million
People
Aggregate TV Minutes/day By Age
1100
1000
450-
500
0
http://www.cdc.gov/communication/images/tv2.jpg
Million
People/
yr
4 Mil
Classics
2 Mil
1 Mil
20
‘85
30
‘75
40
50
60
70
80
293
Advertisers Value Age Cohorts
Differently
Boomers
10
30
290
1. Identify Audience Age Cohorts
‘95
20
http://www.outsidein.co.uk/photos/sunray%20watching%20TV.jpg
Modeling the Market (I)
0
10
40
50
60
70
80
‘65
‘55
‘45
‘35
‘25
291
• Younger audiences preferred
• Longer payback for investment
in customer acquisition
• Less rigid consumption routines,
greater susceptibility to
advertising
294
Modeling the Market (II)
Modeling the Market (IV)
2. Identify TV Viewing (Minutes/Day) By
Age
4.
Value of TV Hours to Advertising by
Cohort (CPM x# of ads x# of hours)
Advertising Value of TV Audience by Age
Advertising minutes= 20% of TV minutes
Average CPM= 13$= 1.3¢/person/ad minute
CPM for 65: $8
CPM for 25-45: $16
Aggregate TV Min.
Average TV advertiser value of viewer/year= $200
Total TV advertiser value of US audience= $60 Bil/yr.
Total TV advertiser value of US Pop. 65+ years= ~ $4.2 Bil/yr.
300
200
100
0
10
20
30
40
50
60
70
80
Advertising Value of
Audience
292
0
10
20
30
40
50
60
295
70
80
46
• Each channel has a peak age
cohort A where it is viewed the
most. audiences declining at a
rate B away from the peak
cohort.
• The media firm can control A
and B through programming
decisions. C is the size of the
audience, and is a function of A,
B, and the presence of other
channels.
Modeling the Market (V)
5. Competitor Analysis
Aggregate TV Minutes for various Channels
by cohort (Schematic)
T
V
M
i
n
u
t
e
s
Cartoon
Nick Jr.
Nickelodeon
MTV
10
20
ABC
CBS
ESPN
CNN
0
296
Fox
Potential
advertising
value of
audiences
30
40
History
50
60
70
Age Cohort
80
299
Modeling the Market (VI)
6. Analysis of Under-Served
Niches
• Where are niches?
•Look
Look for:
297
T
V
Audience, older & younger
(represented by the triangle)
Modeling the Market (VII)
7. Estimating market shares
• Make assumptions
- e.g. competitors that target
the same cohort share that
cohort equally.
• But that the share declines
with distance from the
target cohort
M
i
n
u
t
e
s
C
B
Age
A
A. No domination by a strong brand
(e.g. Nickelodeon)
–Low peak of audience triangle (e.g.
History Channel)
B. Distance of competitors
300
from target cohort
298
301
47
• Audience for a channel
depends on its positioning of
its peak at cohort i, with other
channels j in the market.
• For each cohort, its share is
determined by the distance of
that cohort from its peak
audience cohort
302
i = age cohort
n = number of competitors
j = competitor j
b = coefficient of audience
specialization
(defines decline of % share by
distance of a channel’s peak
cohort)
(а can be measured for existing
channels; it is high for age- specific
channels, lower for inter305
generational channels (e.g., ESPN))
Model of Market Share:
• Repeat this for every cohort i
% Share in cohort i byy a channel =
• Total estimated ad revenues T
for channel:
S = ∑ TVi
i
100
∑ (1 − a ( PeakCohort
j
j
− Cohort i ))
j
i
303
• The channel’s
audience is
the sum of its
share in each
cohort, times
the TV hours
of that cohort
Ti =∑ S %i x (# TV hours) i x CPMi
306
Management Decision Process
http://www.awesomebackgrounds.com/templates/tv-channel-changer-01.JPG
304
How to optimize Revenues T:
• Choose a combination of
–target
g ppeak audience cohort i,
– and the extent of audience
specialization (coefficient b)
» how steeply peaked the
audience triangle will be
307
48
• This is what analytical or
statistical modeling is about.
–Interprets data
• Good analysis requires good
data & its interpretations.
• This is the next topic: Getting
the Data
• This model makes it possible
to check out multiple niches,
and find the optimal niche,
andd therefore
th f
the
th optimal
ti l
specialization
308
311
309
312
• The important point is to
think systematically and
b k ddown th
break
the question
ti off
channel strategy into smaller
elements
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
310
and Tivo Box
• Auditing
VIII. CONCLUSIONS
313Need?
49
III. Empirical
p g of
Sampling
Audience/
Consumers
314
III.1. Sampling
Methods
A.
B.
C.
D.
E.
F.
Personal Interviews G.
Mail and Phone
H.
Surveys
I
I.
Focus Groups
J.
Psycho-Physiology
Testing
K.
Test Marketing
Internet Surveys
Mall Interviews
http://www.infonet.st-johns.nf.ca/providers/nhhp/newsletter/spring00/02_photo.gif
317
Major Players
Retailer Surveys
Conjoint Analysis
Delphi Surveys
Trendsetters &
Opinion Leaders
Automatic
Metering
315
• Personal surveys usually conducted
by market research firms, e.g.,
–Simmons
Simmons
–Dun & Bradstreet
–Arbitron
–NFO
318
–Gallup
A. Personal Interviews
•In-home
316
http://www.ska-pr.com/personal%20interviews.htm
http://www.directionsmag.com/companies/images/logos/1252.jpg
319
50
–
–
–
–
http://www.wealthnationusa.com/xSites/Agents/wealthnationusa/Conte
nt/UploadedFiles/dun_and_bradstreet_logo.gif
320
Can be indepth
Expensiveneed reliable
team
Sample
often biased,,
selfselection
Follow-up
research is
timeconsuming
323
Problem with Personal Surveys
http://www.dmwmedia.com/images/Arbitron.jpg
321
Personal Interviews Pro & Con
• The problem with most
surveys is that people will lie.
–about
about their income
–their taste
–Their actual viewing (or
they will be forgetful)
324
Mick Underwood, The Communication Studies Project, “Audience Measurement”
Other Problems With Personal
Surveys
“Interviewer effect”
-Age, gender, attractiveness,
pronunciation intonation
pronunciation,
intonation,
gestures etc.
- respondents might try to
impress the interviewer
322
325
51
• Futile to ask consumers what
they would be willing to pay for
a product.
• Direct questioning makes
consumers typically
t i ll state
t t a lower
l
price than they would actually
pay (bargaining behavior)
–or, a higher price to please
interviewer
326
Thomas T. Nagle & Reed K. Holden, “The Strategy and Tactics of Pricing: A Guide to Profitable Decision Making,” Second
Edition 1995
Mail and Mailed Surveys
• Low-cost
• Greater anonymity increases
candor
• Low response rates lead to
bias
• For written surveys, no
probing or clarification
329
• Often used for new magazine
concepts, even before the
magazine
i is
i actually
t ll
published in order to validate
concept and to get feedback
on price and features
327
B. Mail and Phone
Surveys
330
Sample Test Mailing Grid
for Magazine
Mail pitch
Approach
Price
Offer
Content
(http://www.onesystem.com/)
328
A
A
Mailing Mailing
$10
$15
C
Mailing
$15
D
Mailing
$20
E
Mailing
$25
Soft
Soft
Hard
Soft
Soft
Broad
Narrow
Broad
Narrow
Broad
331
52
For more details
see Appendix F:
Direct Mail Test
Grid
332
• The firm’s original intended price
was $500
• But survey showed that even at
$2000, 49% of the firms said
they
h would
ld have
h
bought
b
h the
h
package.
• Demand found to be highly
inelastic at high prices (see figure335A)
Thomas T. Nagle & Reed K. Holden, “The Strategy and Tactics of Pricing: A Guide to
Example #1: Telephone
Survey for Office Software
• A software firm developed a
product for law firms that
would manage storage and
billing for legal documents
http://images.amazon.com/images/P/
B00005B0C6.01.LZZZZZZZ.jpg
333
• A random sample of 603
attorneys was contacted by
phone and asked for the
likelihood of purchase at either
$
$2000,
$4000,
$
$6000,
$
or $8000
$
• About 150 responses per price
point.
334
336
• Price increase from $4000 to
$8000 did not change much
the proportion of law firms
willingg to buy,
y, but raised
sales revenue substantially
(Figure B).
337
53
• But problem: prices of
competing products are a
constraint
–can’t charge $8,000 if
competitor offers similar
product at $500
• Still the willingness to pay is
revealed
338
to
339
342
• Based on those survey
figures, what should the firm
charge?
h
?
C. Focus Groups
Preliminary Conclusion:
• Recruited audience
- demographic makeup is
either random or selected
• Charge $8,000
• And also try to have a lowerquality product at about
$4,000
340
343
http://www.ctinfocus.com/images/foc.JPEG
Friedman, Motion Picture Marketing
54
Focus Group:
• Film previews
- 2 Types
¾Production previews: to help
managers and filmmakers
fine-tune the movie
¾Marketing previews: To
study audience’s reactions to
completed films, and assess
marketing strategy
344
Friedman, Motion Picture Marketing
• Originally, Glenn
Close’s character in
“Fatal Attraction”
survived but
audiences hated her
her,
and the ending was
changed to see her
die.
Bay, Willow. “What if ET died? Test audiences have profound effect on movies.” 347
Entertainment Weekly. 28 September 1998.
Test Audiences
• Test Audiences are used by
film companies to gauge
reactions
ti
to
t movies.
i
348
http://www.funworldmagazine.com/2003/Jun03/Features/Larger_Than_Life/images/A13Screen.gif
346
349
55
• Originally, ET died rather than
getting home in “ET”
• Originally Julia Roberts
dropped Richard Gere in
“P tt Woman.”
“Pretty
W
”
Director’s Perspective
“It’s much easier to embrace
the whole testing process
when you know that you
ultimately control the final cut
on your movies. Buy it’s
frightening if you’re in a
position where you’re going to
show the movie at a preview
and somebody else is going to
take the results of that
preview re-cut the film based
on that, maybe consulting you
or maybe not. That’s
terrifying.”
http://i.imdb.com/Photos/Events/4357/RonHoward_Grant_7604965_400.jpg
-Director
Ron Howard
Bay, Willow. “What if ET died? Test audiences have profound effect on movies.”
353
Pretty Woman
National Research Group (NRG)
• NRG: film testing for Hollywood
distributors and producers
–Test screening of movies
–does most film testing
354
Test Audiences Do Not
Always Prevail
• With “the Wizard of Oz”
test audiences complained
that “Somewhere
Somewhere Over the
Rainbow” slowed down
the movie. But the song
stayed.
http://www.rxgetpaid.com/images/National-Research-Grouppaid-medical-research-logo.gif
355
56
Audience Perception
Analyzers
• These analyzers are little,
hand-held transmitters that
resemble
bl TV remote
t controls.
t l
Instead of buttons, they have
a big dial on them.
Smith, Denise, “INSTANT ANALYSIS TECHNOLOGY HELPS RATE
COMMERCIALS” St. Louis Post – Dispatch, Jun 3, 1996. pg. 03
356
Audience Perception
Analyzers
http://www.sphinxdevelopment.co.uk/Images/internetsurvey.jpg
359
Example: Nickelodeon
• Before production on a new version
• Linked to
software and
hardware that
registers the
responses and
their intensity.
Smith, Denise, “INSTANT ANALYSIS TECHNOLOGY HELPS RATE
COMMERCIALS” St. Louis Post – Dispatch, Jun 3, 1996. pg. 03
D. Using the Internet as
a Survey Tool
of the TV series “Rugrats” began,
Viacom quizzed fans about what
th wanted
they
t d
357
King, Tom, “Hollywood Journal: Nickelodeon Comes of Age --- At 20, Nick
Woos Big Stars, Takes On Old Studios; Building a Better 'Rugrat‘” Wall
360
Street Journal. Dec 1, 2000. pg. W.8
User-Level
Measurement
358
361
http://www.infosystem.gr/images/computer_user3.jpeg
57
The Data Meter
• In 1995, Media Metrix installed
the first meter of internet uses,
the “PC Meter,” into a consumer
sample
http://www.queensferry-pri.edin.sch.uk/nursery/photos/computer2.jpg
http://www.netprointer.com/image_file/seo_image/image021.gif
Steve Coffey, “Internet Audience Measurement: A Practitioner's View,” in
362
Journal of Interactive Advertising, Vol. 1:2, Spring 2001, p. 11.
• Requires user
cooperation.
• Incentives are
offered to users
who are willing
to use the
browser.
Internet Surveys: Pro & Con
• Self-selection
• May require the respondent to
install special software.
James H. Watt & Michael Lynch. “Using the Internet for Audience and
Customer Research,” in T.J. Malkinson (Ed.), Communicating jazz:
365
p.127. New Orleans: IEEE.
Other Technique: Mouse
Activity Measurement
http://www.heart-disease-bypasssurgery.com/data/images/incentive.gif
Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan, “Web
Usage Mining: Discovery and Applications of Usage Patterns from Web Data” in 363
SIGKDD Explorations, Vol. 18: 2, January 2002 p. 13.
• Mouse Activities
- number of clicks
- time spent moving the
mouse in milliseconds
- time spent scrolling
http://www.dalveydepot.com/DalveyBMS.jpg
Mark Claypool, David Brown, Phong Le, and Makoto Waseda, “Inferring
User Interest,” in IEEE Internet Computing, Vol. 5:6, November 2001, p.
366
35.
Major Tool: Cookies
• A standard programming device that
produces electronic files to tag individual
customers with a unique identification.
– Allows a website to recognize an
individual.
Deck, Cary A., “Tracking Customer Search to Price Discriminate.” Electronic Inquiry,
364
April 1, 2006.
367
58
Comb Analysis
Still Other
Types of
Surveys
• E.g. If Dell wants to know
why it is selling fewer
computers
t to
t the
th Best
B t Buy
B
retail chain than HP
368
http://www.sferaplus.hr/pr/hp/NotebookHPnc4000.png
371
Comb Analysis: 3 Steps
E. Expert
S
Surveys:
C
Comb
b
Analysis
• First step, researchers ask the
retailer to rate (e.g., on a 1-5
scale),
l ) the
th importance
i
t
to
t its
it
customers of various
purchase criteria.
Koch, Richard, The Financial Times Guide to Strategy. London: FT Prentice Hall, 2000, p.54-7, 193.
369
• “Comb Analysis”
- Technique for comparing
purchase criteria (“most
important reasons for product
selection”) with opinion of
producer
Koch, Richard, The Financial Times Guide to Strategy. London: FT Prentice Hall, 2000,
p.193.
372
Comb Analysis Example
Purchase Criterion Importance Score
Price
4.9
Strength of Brand
Name
4.5
Service
4.0
Product Innovation
3.6
Packaging
1.5
370
373
59
Comb Analysis:
1. Survey Retailers
Comb Analysis
Purchase Criterion
4.9
4
4.5
4
6
3.6
3
Importance Score
2
1
5
4
1.5
49
4.9
4.6
4.5
3.7
4.2
4
4
3.6
4
3
Packaging
Product
Inovation
Service
Strength
of Brand
Name
0
Price
e Score
Importance
Retail Distributor's Criteria and Dell's Score
5
Retailer's
Assessment
Dell's Score
2
1.5
1
0
Price
374
Strength
of Brand
Name
Service
Product
Inovation
Design
377
• Dell seems to
over-invest in
design, and
under-invest in
price cuts
cuts.
Comb Analysis – 2nd Step
• Ask the producer (Dell) to
score the same criteria.
http://www.2shoptheworld.com/media/Dell-primoffer.jpg
375
Comb Analysis
Purchase Criterion
Price
St
Strength
th off Brand
B d
Name
Service
Product Inovation
Design
Comb Analysis – 3rd Step
Importance
Score
Dell's Score
4.9
3.7
4.5
4
3.6
1.5
378
• Compare competing firms’
scores.
4.6
4.2
4
4
376
379
60
Comb Analysis
Purchase
Criterion
Dell’s
Score
Importance
Score
Comb Analysis
HP’s
Score
Price
4.9
3.7
5
Strength of
Brand
B
dN
Name
45
4.5
46
4.6
42
4.2
4
4.2
3.5
Product
Innovation
3.6
4
3.6
Design
1.5
4
Service
2
380
• If Dell lowers effort on
design (least important
purchase
h
criteria),
it i ) it could
ld
lower price to Best Buy and
become more competitive
with HP.
383
Comb Analysis: Competitor
Analysis
Retail Distributor's Criteria and Dell's v. HP's
Score
6
5
4
3
2
1
0
Importance Score
Dell's Score
Design
Product
Inovation
Service
Price
Strength
of Brand
Name
HP's Score
381
384
Comb Analysis
• Comb Analysis indicates that
Dell needs to lower its price
(the most important purchase
criteria) to be competitive
with HP.
• But can cut cost of design
382
F. Expert Surveys:
Delphi
385
61
Expert Surveys
Delphi Methodology
•Created in the
1950s by RAND
corp
corp.
•Goal: Reach
expert consensus Apollo’s Temple in Delphi,
Home of the Greek Oracle
by experts on a
386
certain topic
Delphi Methodology
Delphi Methodology
• First round of questions:
–Questions with answers of
scores 1-10
1 10
389
Delphi Methodology
• Combines quantitative and
qualitative data
p
: 15 - 20
• Groupp process
respondents
• Selected for their expertise and
experience
• Second and subsequent rounds:
–Participants are provided with:
¾Information on how the entire group
rated
t d the
th same item
it
¾Statistical feed-back related to their
own rating
¾Summation of comments made by
each participant
387
Delphi Methodology
390
Delphi Methodology
• Anonymity of participants
• Written responses to questions
• Direct communication between
respondents not allowed
388
•Given same questions again
•Delphi rounds continue until a
predetermined level of
consensus is reached or no new
information is gained
391
62
• Lord Rutherford, Nobel
Prize Laureate: 1933:
“Anyone who expects a
source of power from
transformation of these
atoms is talking
g
moonshine”
• The main benefit is that they
are quick and cheap.
• The negative is that they are
very highly speculative.
McBurney, Peter, Parsons, Simon & Green, Jeremy. “Forecasting market demand for
new telecommunications services: an introduction.” Telematics and Informatics 19,
392no.
3 (2002): 225-249.
But how good are expert
forecasts?
• Lord Kelvin, one of the world’s foremost physicists,
1895: “Heavier-than-air flying machines are impossible”
• Marechal Foch, leader of French military, 1911:
“Airplanes are interesting toys that are of no military
value”
395
Source: www.darvill.clara.net/nucrad/ images/rutherford.jpg
aJohn von Neumann, celebrated
scientist: 1956: “A few decades hence,
energy may be free, just like unmetered
air”
Source: www.ibm.com/ibm/history/exhibits/ chairmen/chairmen_4.html
393
Source: www.neuralmachines.com/ axon/signals.html
396
Source: http://www.afa.org/magazine/graphics/0600korea8.jpg
• Astronomer Royal Richard Wooley:
1956: “Space travel is utter bilge”
Source: http://www.everett.wednet.edu/schools/high/everett/EHS_Files/STUDENT_WORK/moonwalk.GIF
394
397
63
Case Discussion Golden
Years Channel: Delphi
Survey
398
401
G. Surveys of
Trendsetters and
O i i Makers
Opinion
M k
• Need to select the Experts
– Gerontologists
– Marketers specializing in
retirees
– Social workers
399
402
400
SIGMA, “SIGMA, SIGMA Global Sensor” 31 Mar 2005, <http://www.sigma-online.com/en/
SIGMA_GlobalSensor>, 13 Apr 2006
403
Delphi Sample Questions
• “On a scale of 1-10, do retirees
get enough TV shows?”
• “Would they resent such shows
since it reminds them that they
are old?”
• “How many hours a week would
they watch such shows on
average?”
64
Opinion Leadership
Trendsetters in the US
• “Affluent Progressives,” the
“Emancipated Navigators,”
and the “Aspiring
“
Acquirers.”
•Opinion leader is able to influence
others’ attitudes or behaviors.
Source: M Solomon, Prentice Hall (1996),Consumer Behavior
404
Surveying Trendsetters
407
Trendsetting in Europe
• “In Europe, the members of the
Upper Liberal Segment, the
g
and the
Postmodern Segment
Progressive Modern Mainstream,
are responsible for most of the
trends.”
• Identify trendsetters (ex:
celebrities, critics) and
d t
determine
i their
th i response
405
408
Trendsetting in Japan
• In Japan, the „Modern Rich“, the
„New Citizens “ and „Young
Urbanites “ are usually the origin
of trends.
406
409
65
Findings:
Technorati.com
• It is, however, a statistically
significant predictor of box office
performance for later weeks, and
f cumulative
for
l ti bbox office.
ffi
• Rates blogs.
• Ranks blogs
based on the
number of sites
that link to it.
http://www.customersarealways.com/uploads/technorati-thumb.gif
“How Does Technorati Work.” Last accessed on 18 June 2007 at
http://trailblogging.com/2007/03/28/how-does-technorati-work/.
410
Critics
Two alternative perspectives on
the role of critics.
1. Critics could be opinion leaders
who influence audience demand.
2. Critics could be predictors of
their respective audiences.
-Critics wired to act more as
leading indicators than as
opinion leaders.
411
Jehoshua Eliasberg; Steven M. Shugan, Film Critics: Influencers or Predictors
Journal of Marketing (Apr 1997)
413
• These findings do not support
the “opinion leader”
perspective, which would
predict that the greatest
influence of the review should
be immediately following the
review.
• But it does support the
M
414
“predictor” hypothesis
Research Study Findings:
• The % of positive and negative
critics reviews is a statistically
insignificant predictor of box office
performance
f
ffor th
the early
l
weeks(weeks 1-4).
412
415
66
Paul F. Lazarsfeld
• Applied mathematician from
Austria.
• Central figure in the growth of
empirical social science.
• Integrated market research
with psychological analysis.
H. Automatic
Audience
Metering
416
Audience Research
Purpose:
• To let broadcasters know who their
audience is, and how it responds
• To let broadcasters know hoe much
to charge for advertising
• To let advertisers know who they
are reaching
417
Lots of Money at Stake
Major TV Advertisers (2006)
•
•
•
•
•
•
•
•
•
•
Procter &Gamble
General Motors
Time Warner
Verizon
AT&T
Ford
Disney
Johnson & Johnson
DaimlerChrysler
GlaxoSmithKline
Source: Schiekofer, The Media Marketplace. New York: Mediacom
$4.6
$4.4
$3.5
$2 5
$2.5
$2.5
$2.4
$2.3
$2.3
$2.2
$2.2
418
Daniel Czitrom. “The Rise of Empirical Media Study: Communications Research as
Behavioral Science, 1930-1960.” In Media and the American Mind. Chapel Hill, NC:
UNC Press, 1982.
419
Early TV audiences: Diary
System
• Traditional Nielsen
methodology, especially for
local TV markets.
- used 4x a year during
“sweeps” periods for local
stations.
• viewers record TV viewing 420
1. Diary System
• opportunity for samples to lie
• misses responses from
children,, travelers,, and
TV viewing in bars
• difficult with channel surfing
421
67
Sample Bias
• In the past, response rates of 70%
for diaries.
• Today it is difficult to get 50%
response rate for a meter panel,
25% ffor a di
diary
• If the people who do not respond
view TV differently from those
that do, then the ratings are biased
and wrong.
422
2. Also used for TV “overnight”
ratings: Telephone Surveys
–Fast
–sample biased
–Respondents
d
run out of
patience
423
History: Dynascope
• 1.5 million pictures were
analyzed:
- When the TV was on,, 19% of
the time no one was in the room.
- 21% of the time the person was
engaged in a different activity
Erik Larson, “Watching Americans Watch TV,” The Atlantic Monthly,
425
March 1992
History: Infrared
Scanners
• Kiewit’s “hot bodies”
- scanned for people with an
infrared sensor.
- But Kiewit’s scanner
distorted by the “big-dog
426
effect.”
March
1992
http://homepage3.nifty.com/shibadog/Album2/Album32/wanloaf3.jpg
History: Dynascope
• 1965, the “passive audience
meter” called the Dynascope:
a movie camera that took
pictures of both the TV viewer
pictures,
and the TV show every 15
seconds.
424
March 1992
427
68
More Practical Solution:
The Nielsen People Meter
• placed on each TV set in a
sample household.
• an electronic system placed
in 5,000 randomly selected
households in the U.S.
428
431
People Meters: Con
People Meters: Pro
• Older people have higher
refusal rate to participate
• Young men most willing
illi to
employ meter
• instant measures
lying
• no “lying”
429
432
Also, the greater audience
fragmentation creates greater
relative unreliability of results
People Meters: Con
• children, travelers, and bar
viewing not captured
• nobody may be watching
• requires viewers to identify
themselves
• The % of standard deviation
tends to grow as ratings
become smaller.
http://www.printphoto.com/contest_pics/finalist
0902/I'm%20Not%20Tired.jpg
430
433
69
• E.g.: a “true” ratings of 6, in
sample of 3,000, will show as
sample ratings between 5.2 and
6.8 (± .8) in 95% of samples.
–i.e. relative error is ±14%
• But same error for “true” rating
of only 2 (± .5) will have a
relative error of ±25%
434
• And for a small cable channel
with “true” rating of .3, ±.2, the
relative error is ±65%
http://www.webspin-design.com/assets/Newsletter/Sept03/nr-reach-trend-top.gif
435
Case Discussion:
People Make
for “GYC”
• In theory GYC could benefit from the fast and
relatively accurate TV ratings data via the
People Meter
Meter.
– would also show demographics
• In practice, its ratings will be too low to
register
436
Can ratings be
manipulated?
437
Japanese Rating Scandal
• In 2003 a producer of the
Nippon TV Network (NTV)
manipulated television ratings
for his show
“Heads Roll in NTV Ratings Scandal.” Japan Times Online. 19 November 2003. Last
438
accessed on 19 June 2007 at http://search.japantimes.co.jp/cgi-bin/nn20031119b6.html.
Japanese Rating Scandal
• The producer used money to find
out what specific household were
being observed by the ratings
agency Video
Vid R
Research
h Ltd
Ltd. and
d
got those homes to watch certain
shows by bribing the occupants
through various benefits.
“Heads Roll in NTV Ratings Scandal.” Japan Times Online. 19 November 2003. Last
439
accessed on 19 June 2007 at htt p://search.japantimes.co.jp/cgi-bin/nn20031119b6.html.
70
Broadcast Data System
(BDS)
3. Automated
Metering
• The BDS is still used today as the
“Nielsen BDS” and tracks over
1,000,000 songs
g each yyear.
• Radio/artist managers request
over 10,000 reports each day.
• Some songs are big on radio but
not in sales.
• The first mechanical
device to measure TV
demand was the
Audimeter, where a
stylus scratched out a
record of radio tuning
http://www.desmoinesbroadcasting.com/xtras/nielsen-audimeter-fullpix.jpg
440
March 1992
“About Nielsen BDS.” BDSonline.com. Last accessed on 15 June 2007 at
http://www.bdsonline.com/about.html.
443
• The chairman of Nippon
Television Network (NTV)
C
Corporation
ti was forced
f
d to
t
resign
441
Broadcast Data System
(BDS)
• Used for the
Billboard Top
100 Singles
• Tracks songs
played on the
radio
444
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
http://www.covenantdesigns.com/marketing/top_100_9surf.jpg
Poltrack, David. “Media Audience Research” Course. Columbia University Business
442
School. Fall 1998.
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
and Tivo Box
• Auditing
VIII. CONCLUSIONS
• Is This What Media Firms Need?
445
71
III.2. NewGeneration People
Meter: The Digital
Meter System
446
The Battle of the Meters
• Nielsen Local People Meter
(LPM) vs. Arbitron Passive
People Meter (PPM)
• Channel-based vs.
programbased
449
Source:ppm.arbitron.com
• Identifies audio and TV content
through active codes embedded
in the program itself and in the
commercial messages
• Search engines identify the
programs and the
advertisements that are
watched
447
• This enables real time reports
on watching or listening
• can meter broadcast, DBS,
PVR, digital cable, and radio
use.
Nielsen LPM Procedure
• A meter rests on top of every
TV in a Nielsen household and
each familyy member has an
assigned number.
John Maynard, “Local People Meters May Mean Sweeping Changes on
450
TV,” The Washington Post, April 28, 2005, A01.
http://www.nielsenadvertiserservices.com/images/box_4.gif
• Old local station system diaries
collected in “sweep” periods
• Nielsen initiates overnight
Local People Meter data
–Larger local samples
(8000 vs. 540 for diaries)
448
451
http://nbc.com/Friends/index.html
72
Nielsen Local People Meter
(LPM)
•$30 mil development
•Permits collection of
audience response in near
real time.
•Continuous measurements of
major local markets (not just
for 4 sweep periods)
•Includes demographics
•Launched in Boston, 2002
•Full-scale operation in 2006
http://www.nielsenmedia.com/lpm/images/people%
20meter-new.jpg
The Arbitron Portable People
Meter(PPM)
•Portable People Meter, is
worn by consumer, detects
and records programming
wherever consumer located
•And whatever the program
source
452
455
• Includes low resolution optical
meter that monitors how many
people are in the room, and
identification of members of
households
• Can determine fast-forwarding
through ads.
Arbitron PPM Page 513
453
http://digital-lifestyles.info/copy_images/arbitron_2-lg.jpg
456
PPM
•Expanded national sample
from 5,000 to 10,000.
• Portable people meter (PPM) tested
in Houston, in 2005/2006
• The PPM reads an encoded audio
message that is embedded into the
audio
di track
t k off every piece
i
off media
di
(including, for example, TV, radio
and the Internet) that has sound.
454
Besser, Charles N., PPM is the next big score for sports TV. Advertising Age, Vol.
457
76 Issue 26, p22-22, 6/27/2005. VOD,
73
The Portable People Meter
System in Action
III.3. Metering
Alternatives: Cable
Box and TiVo Box
458
461
• Arbitron PPM (worn by users) is
better able to keep up with
–Multiple TV sets in household
–Out-of home viewing
• But requires uses to wear the
device or have it nearby
• more expensive, but can be used
for radio, TV, Cable, and others.
459
• Alternative: use the digital settopbox (STB) of cable or satellite
TV
• Would increase sample size to
hundreds of thousands per
market
• Concept and technology
introduced in 1980s (CUBE
cable system) in Columbus, Ohio
462
Source: Broadcasting & Cable, 2/2002
Set Top Box
460
http://www.comcast.com/MediaLibrary/1/1/About/PressRoom/Images/
LogoAndMediaLibrary/Photography/DCT700DigitalCableBox2.jpg
463
74
• CUBE data used in litigation and courts.
– Columbus, Ohio pornography trial:
“Captain Lust” was shown to be one of the
most popular programs
– New Haven, CT: Least watched “You and
the Economy”
Economy (A Panel of Yale economics
professors was watched by 3 HHs)
• Cable industry decided not to collect STB
data, individually or in aggregate, to avoid
giving customers a feeling they are being
watched and monitored.
464
Most Popular Program in
Columbus, Ohio
• The media research agencies
utilize aggregated set top box
data which it acquires from cable
operators to provide
id a secondd by
b
second-by-second analysis of
viewing habits.
“MTV Networks Leverages Charter Data from TNS Media Research”,
Wireless News, August 10, 2007
467
• Shift from program ratings to
commercial ratings. Commercial
ratings is the ability to measure
how many viewers were tuned
when the commercial was
actually running.
465
http://www.moviegoods.com/Assets/product_images/1010/213997.1010.A.jpg
George Shabbab, “Not A Second to Lose,” MediaWeek, New York: July 23- July
468
30, 2007
TiVo Box
• First trial STB of multichannel real-time metering,
1997 Atlanta
466
• Enables real-time monitoring
and historical data for a
month
th
• Permits analyzing of time
shifting and zapping of
commercial ads
469
75
TiVo Box
Real time viewing
measurement for TV
programs
http://www.nytimes.com/images/blogs/tvdecoder/posts/1107/tivo-box.jpg
470
• Nielsen has also launched a new data
service Nielsen DigitalPlus which
integrates set top box data from cable
and satellite operators with TV
measurement data from Nielsen
Media Research, commercial activity
data from Nielsen Monitor Plus,
Plus
Retail and scanning information from
AC Nielsen and modeling and
forecasting information from Claritas,
Spectra and Bases.
DVR Page 526
http://www.timewarnercable.com/MediaLibrary/4/55/Content%20Manag
ement/Products%20And%20Services/imagesDVR/dvr-mainbanner.jpg
471
Media consumption tracking:
Nielsen’s plans
Cellphone Use for Media
Measurement
• Using specially adapted cell
phones to measure what
consumers listen
li t to
t andd see
– Provider: Integrated Media
Measurement Inc
Clark, Don, “Ad Measurement is Going High Tech.” Wall Street
Journal, Section B; Page 2, Column 3, April 6, 2006, Thursday.
Katy Bachman, “Nielsen to Roll Out DigitalPlus”, Mediaweek.com, February 474
12, 2007
• Nielsen intends to track consumers’
activities on the web, TV, mobile
and per GPS when shopping.
shopping
• They work with Ball State
University to observe people in
their homes.
472
Story, Louise. Nielsen Looks Beyond TV, and Hits Roadblocks. The New York Times, February 26, 2008
76
Media consumption tracking:
Nielsen’s plans
• Nielsen acquired firm to track
people’s eye movements, brain
waves and perspiration,
perspiration which can
be used for TV and internet
activity tracking.
479
Media consumption
tracking: Limits
• An alternative from gathering data
across all media from the same
consumers (demanded by customers
but facing resistance from
consumers) is merging data from
separate panels resulting in quality
loss.
Measurement
Technology Affects
Results Therefore,
Results.
Therefore it
is a Battlefield
480
Media consumption
tracking: Limits
• Not to lose the established panel
participants, Nielsen has to balance
their thirst for data with their
understanding and respect for
consumers’ privacy.
• The ideal of tracking consumers
across all media remains a dream.
Important Consideration
• Metering is not about
technology, but about money
• Any change in metering
procedure has economic
effects
481
77
Measurement Technology
Affects Results. Therefore, it
is a Battlefield
•Broadcasters vs. cable channel vs.
advertisers
•Nielsen in the middle
•For example, the effect of the
adoption of the People Meter, over
paper diaries, was significant
•And the shift to LPM does the same
482
CBS Lost 2.0 Points in change to
people meter
http://i.afterdawn.com/v3/news/cbs_logo.jpg
485
http://gr.bolt.com/oldsite/download/pc/action/battlefield_1942.jpg
Changes in Ratings Patterns
for Prime Time Before,
During and After the
Introduction of the People
M t
Meter
William Adams, Journal of
Media Economics, 7(2) 1328, 1994
NBC Lost 1.5 Points
483
Overall Effect of People
Meters on Ratings
• Permanently lowered overall
TV ratings in 1990 by an
average of about 4.5 points.
• CBS: lost 2.0 points:
NBC: showed avg. loss of 1.5
484
ABC: little effect
http://www.midnightchimesproductions.com/MCP/images/NBC-logo.gif
486
Effects on Programming
Categories
• Participation shows were
boosted 5 points in rating;
sitcoms
i
1.5; news 0.2:
• All other categories
dropped. Medical shows
showed highest drop; -4.1
487
78
Business Impact
• In 1990, each ratings point was
worth approximately $140
million/yr
• Decrease in ratings
g could cost
major networks between $400
and $500 million/yr.
• Cable: ratings gain of almost
20%.
488
• Cable networks fear contentspecific ratings less than TV
networks
t
k because
b
they
th are nott
as dependent on advertising.
• For Washington D.C., the
claimed undercount rates were
25% for Hispanic homes and
20% for
f African
fi
American
i
homes.
John Maynard, “Nielsen Delays Release of Local People Meters,”
491
Washington Post, Thursday, June 2, 2005, C07.
• Washington D.C. 2005 tryout
(600HH) showed not 650,000
HH watched local TV from 57PM, but only 526,000.
• Cable lost another 114,000
HH.
Lowry, Brian, “The Ratings: Inside and Out; Analysis: Networks seem to have decided
489
the ratings battle wasn’t worth the effort,” Los Angeles Times, July 12, 1997.
*
Impact of Local People Meters
• Here, too new metering has major
impacts on numbers
• In NYC, Fox 5, UPN 9 and WB
11 showed
h
d bi
big drops.
d
492
LPM Effects
• Fox TV network and several local
stations complained that LPM
undercounts minority viewers in
cities.
• Don’t Count Us Out, a group
funded by News Corp., generated
political pressures in Washingtong
John
Maynard,
“Local People
Meters May Mean Sweeping Changes on
and
NYC
on Nielsen.
493
TV,” The Washington Post, April 28, 2005, A01.
http://images.zap2it.com/2
0031016/fox_logo_240_00
1.jpg
490
79
• Minimum standards for
broadcast audience analysis
research have been established
by the Electronic Media
Ratings Council in New York,
York
which audits and accredits
rating services
• To mollify its critics Nielsen
agreed to a R&D fund to
improve its methodology.
methodology
• Creation of an Advisory
Council
Katy Bachman, “Nielsen Outlines Changes to Ratings Service,”
Mediaweek, February 21, 2005.
494
495
497
• Members:
–National Association of
Broadcasters
–Cable Advertising Bureau
–Television Advertising
Bureau
–Magazine Publishers of
America
498
For more details see
Appendix G:
A di
Audience
Measurement Firms
• Thus one can see that ratings
technology and ratings
methodology affect dollars,
Euros and Yens
Euros,
• It is therefore important that
the ratings agencies are
trusted by all sides
496
499
80
III.4. Audience
M ti
Metrics
500
We’ve looked at how to
measure audiences.
N t question
Next
ti is,
i how
h
to interpret and use
the data
501
I.
503
10 Audience Metrics
1.HUT
g
2.Rating
3.Share
4.GRP
5.CUMS
6. AQH
7. AF
8. CPM
9. Quads
10. Q
504
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
and Tivo Box
• Households are usually the base
unit, not people when measuring
audiences.
• Audience measures are usually
done in parts of days.
• TV rating services (ex: Nielsen) set
their own geographic rating areas.
• Auditing
VIII. CONCLUSIONS
502Need?
505
Television Ratings Lab. “Television Ratings.”
81
Important Television Ratings
Terms and Facts
• Ratings = (100 x Households
viewing program) divided by
((total households with TVs))
• Share of Audience = (100 x
households viewing program)
divided by (households using
TVs that instant)
506
Television Ratings Lab. “Television Ratings.”
2. “Share” (of Audience)
• The percent of TV sets in use (or
persons viewing) tuned to a
program.
SHARE =
–HUT: Households Using TV
actually watching at that time509
#1-3:HUT, Ratings,
Shares
Audience Metric #1: HUT
(Households using TV)
•Number of share
• example: 60 mil HH watch any
TV during CSI time slot.
(=HUT)
–Share = 20 mil HH x 100/60
mil HH (HUT) = 33.3
• Share > Rating
510
–since HUT < TV HH
507
Nielsen Media Research
Audience Metric #2
1.
Viewers x 100
HUT
Viewers of a program
TV HH
• In US ~105 mil TV HH
• Example:
–20 mil HH watch E.R.
Rating = 20× 100 =19.0
105
508
Broadcast TV: Nielsen Media
Research Top 10
(Week of May 12, 2008)
Rank
1
2
3
4
5
Program
American IdolWednesday
American Idol-Tuesdayy
Dancing With The Stars
CSI
Dancing W/ Stars Results
Network
FOX
Rating
14.6
FOX
ABC
CBS
ABC
14.4
11.9
11.2
11.1
*Measured in millions; includes all persons over the age of two.
http://www.nielsen.com/media/toptens_television.html
82
Nielsen Media Research Top 10 (Week of May 12, 2008)
Rank Program
6
Desperate
Housewives
Grey’s Anatomy7
Thu 9PM
8
Without a Trace
9
NCIS
10
CIS: Miami
Network Rating
ABC
10.7
10
House-Mon 9PM
ABC
10.5
CBS
CBS
CBS
9.6
9.5
9.1
FOX
9.1
Top Syndicated
Programs
Top Syndicated Program in the US since 1997
Rating
Wheel of Fortune (M-F)
Jeopardy (M-F)
Home Improvement (M
(M-F)
F)
Oprah Winfrey Show
Seinfeld
Simpsons
Xena Warrior Princess
Entertainment Tonight
Hercules, Journeys of
Wheel of Fortune (Wknd)
*Measured in millions; includes all persons over the age of two.
11.0
9.2
85
8.5
8.0
7.4
6.2
6.1
5.7
5.4
5.3
http://www.nielsen.com/media/toptens_television.html
Highest Ranked Regular
Program Series, US
1950-51
1951-52
1952-53
1953 54
1953-54
Texaco Star Theatre
Arthur Godfrey’s Talent Scouts
I Love Lucy
IL
Love Lucy
L
1991-92
1992-93
1993-94
1994-95
1995-96
1996-97
60 Minutes
60 Minutes
Home Improvement
Seinfeld
E.R.
E.R.
Share
61.6
53.8
67.3
58 8
58.8
21.7
21.6
21.9
20.4
22.0
21.2
Rating
81
78
68
67
515
Audience Metric #4
Gross Ratings Points, Reach
Frequency
36
35
33
31
36
35 513
516
Highest Rated Individual
Broadcast
Gross Rating Points
Top 10 US Telecasts 1960-1990
1 MASH Special
2 Dallas
3 Roots, PT VIII
4 Super Bowl XVI
5 Super Bowl XVI
6 XIII Winter Olympics
7 Super Bowl XX
8 Gone With The Wind, Pt. 1
9 Gone With The Wind, Pt. 2
10Super Bowl XII
Rating
60.2
53.3
51.1
49.1
48.6
48.5
48.3
47.7
47.4
47.2
Share
77
76
71
73
69
64
70
65
64
67514
• Rating point= 1 percent of
the potential audience
• Gross Rating Points (GRP)
– sum of ratings over a time
period
517
83
Example for CUME:
Radio Station #1
• If an advertiser uses four
different programs with
respective ratings of 15, 22,
19, andd 27, the
h weekly
kl GRP
becomes the sum, or 83 GRP
• Station with a CUME of 20,
000 (high) and an audience at
an Average
g Quarter
Q
Hour
audience (AQH) of 150 (low)
518
4. The Audience Metric #5 or
CUME
• Reach (or CUME)
http://www.all-businesslogo.com/images/update/29aug
2004/Z100__38930.gif
• measures # of viewers or
listeners per week of a channel
• viewers counted once per week
• Useful for cable channels or
519
radio stations
521
CUME: Radio Station #1
Interpretation: station attracts
large numbers of people in a
week but does not keep them
–few listeners at any given time
• Station promotes itself well, but
does not have good
programming to keep listeners
522
Example for CUME/AQH
Radio Station #2
Audience Metric #6
Average Quarter Home
Audience (AQH)
• Station with CUME 10,000
(low) and AQH of 2,500 (high)
• Average audiences for major
time periods of the day
• Shows how many people are
reached over a week
520
523
84
CUME: Station #2
–InSmall but loyal audience
–25% of overall listeners
are listening
g at anyy
moment
• creases the chance that ads
will be heard by continually
tuned-in audience
Average Frequency (AF)
• AF=AQH x Number of
Spots Per Week/CUME
• Number of Spots per Week=
{(AF x CUME)/AQH}
524
Audience Metric #7: Average
Frequency (AF) of Exposure
• Used to calculate how many
times an ad must be p
played
y so
the average listener will hear
it, for example, 3 times
527
Example for AF: Radio
Station #1
• Assume (AQH=150, CUME=20,000
• To obtain Average Frequency of 3:
{(3 x 20,000)/150}
={(60,000/150)}=400
Result: Needs 400 ad spots per week
to reach average listener 3x
525
Audience Metric #4
Gross Ratings Points, Reach
Frequency
526
528
Example for AF: Radio
Station #2
• Assume AQH = 2,500,
CUME = 10,000
• To obtain
b i average frequency
f
of 3 (AF): (3 x 10,000)
/2,500 = 30,000/2500 - 12.
529
85
Radio Station #2
• Need only 12 ad spots
per week to reach
average listener 3x.
• Will be
b muchh cheaper
h
because more targeted.
But Station 1 will
reach more people
(higher CUME)
• CPM={(cost of
advertising)x1,000}/Average
g) ,
}
g
Audience
http://ww1.prweb.com/prfiles/2005/02/25/2127
79/GManAngleMicTypeshade.jpg
530
533
Audience Metric #8
Cost Per
Cos
e Thousand
ousa d
(CPM)
531
Cost Per Thousand (CPM)
• the expenditure to reach
1,000 households or persons
with an ad
532
Bilotti, Richard, Megan Lynch, Ksenofontova Svetlana “Advertising Outlook
for 2005 and Beyond” Morgan Stanley, 2005
534
CPM for Major Networks
ABC
CBS
NBC
FOX
2000/2001
$18.82 $16.64 $23.32 $16.84
2001/2002
$16.59 $17.04 $22.33 $16.96
2002/2003
$17.42 $18.57 $24.12 $17.81
2003/2004
$20.40 $24.31 $29.94 $21.91
Bilotti, Richard, Megan Lynch, Ksenofontova Svetlana “Advertising
Outlook for 2005 and Beyond” Morgan Stanley, 2005
535
86
Cost Per Thousand Impressions
http://www.morganstanley.co
m/institutional/techresearch/p
dfs/emarketing.pdf
Web Banner Avg. Price
$4
Day Time TV
$5
Direct E-Mail
$20
Solo Direct Mail
$934
Shared Direct Mail
$40
536
CPMs for Various Media
• Prime Time TV
• Radio Network
Web Banner List Price
CPM (Cost per 1,000
Impressions)
$29
539
Different Online Ads
$16
$6
Magazines (niche) $70 – 190
Magazines (general) $5 – 190
537
CPM For Magazines
• Sports Weekly:
$8.75-28.38
• ESPN Magazine: $19.59-54.95
• Sports Illustrated: $19.59-75.17
• Sporting News: $18.71-73.62
• TIME Business Edition: $24.47
• Business Week, Fortune, Forbes:
538
$41.21
http://www.timeplanner.com/planner/editorial/t
argeted_editorial_editions/time
_business_reports_body.html
http://www.usatoday.com/me
dia_kit/sports_weekly/au_eff
icient_reach_men.htm
540
• Most newspapers calculate
their CPM as the single column
inch rate divided by their
circulation
circulation.
• Magazines determine their
CPM by dividing the cost of a
full page ad by their circulation
541
87
Why Are CPM Prices
Different For Different
M di ?
Media?
• The 3-D cube of advertising
value is a way to show average
CPMs for different media
based on three dimensions:
–Targetability
–Sensory intensity
–Interactivity
542
545
“Cable Advertising Revenue and Addressable Commercials” by Bill Harvey
1. Different Market Powers of
a Medium
• Different competition in different media
• Local newspapers usually have local
powers for manyy types
yp of local
market p
ads.
Local radio is competitive
• New York Times theater box ads: CPM
enormously high
The Cube of Advertising Value
543
546
2. Different Effectiveness of
Media
• Raises willingness to pay
• Based upon length and
quality of exposure, sensory
involvement, interactivity,
and ease of response.
544
3. Different Incremental Cost
of Media
• Print media must add paper,
printing, transportation.
• TV broadcastingg has no
incremental cost per viewer
547
88
Trends In CPM
• For Big 4 TV networks the CPM
increasing, because their value in
reaching national audiences
• For cable:
–Decline for broadbased N/Ws
–Increase for specialty N/Ws
–significant declines for 3rd tier
548
cable networks
551
Primetime Ad Prices
(30 sec, US)
Top
Average
1960
$30,000
1970
$65,000
1990 $400,000 (Cosby) $125,000
1998 $500,000 (Seinfeld)
2003
$455,000 (Friends) $115,799
Interpretation
• Advertisers looking for niche
demographic markets.
• Or, for national reach.
(http://search.corbis.com/default.asp?i=11328728&vID=1&rID=101)
549
And Bradley Johnson, Advertising Age, “Low CPM Can Spell Bargain
for Buyers” May 2003
552
Station “Rate Card”
• Prices of advertising time
offered by a station.
• Includes package plans,
plans
discounts, and policies
• Often starting points for
negotiations
550
89
Audience Measurement:
“Quads”
Media Metric
#9: Quads
554
Nielsen-Type Ratings Measure
Only The Number of Viewer
• It’s a quantity, not the quality
of viewing
• Does not determine the
intensity of preference of
audience.
555
• To measure qualitatively, not
just quantitatively, requires
“attitude measurement”
techniques:
–focus
f
groups
–in-depth interviews
• Tool used by TV networks to
study viewing behavior
• 2 factors taken into account:
–tuning length/episode
(program’s “holding power”)
–frequency of viewing
(“loyalty”) to program
557
Quads distinguish 4 viewer
types
• “Gold cards”:
–watch over 75% of an episode
–Watch
Watch over 55% of episodes
shown in analysis period
• “Occasionally committed:”
–watch 75% of program, < 50%
558
of episodes
• “Silver Sliders”
–watch less than 75% of
program, but regularly
• “Viewers
Vi
Lit
Lite”
–watch < 50% program, and
rarely
556
559
90
Advantages
• Holding power indicates
program liking, involvement,
and advertising
–Likely
Likely not to switch channels
during commercial breaks
560
563
Audience
Metric
#10: “Q”
561
564
“Q”
• Performer is rated on both
familiarity and how well s/he is
liked
• Cable networks have a more
fickle audience than TV
N t
Networks
k
562
http://www.davidandmaddie.com/images/100tv-people.jpg
Jennie L Phipps. ” Favorites are good buys” Television Week, Apr 21, 2003.
565
91
Performer “Q Score”
• Measure of how much an
audience “likes” a show or
performer
• Evaluations/TVQ Inc., developed
methodology
h d l
in
i 1964
• “Q” metric is a derivative of
ratings and overall
recognizability of the star, to
quantitatively assess actors
566
“Q”
James Gandolfini “The
Sopranos”
www.facade.com/celebrity/ James_Gandolfini
• Has a Q score of 36, above
th prime
the
i time
ti male
l average
of 19
569
Q and advertisers
• Q is a ratio of the "Favorite"
score to the "Familiar“ score
• “Familiarity” measures the
proportion
i off respondents
d
who
h
recognized the performer
• Respondents also indicate
which stars are their “favorites”
567
• High performer Q and high
program Q are related
• ppersonality
y appeal
pp raises a show’s
overall appeal.
• A high Q score for a show often
means that viewers watch more
of the commercials
570
• This means the Q rating can
be high if a performer is
extremely well-liked by a
core group
“GYC” Personalities
• GYC programs must have at
least a few identifiable stars
whom the 65+ population like
to watch.
–Mickey Rooney
–Oprah Winfrey
571
–Bill Cosby
568
Brian Lowry, “Q Marks Spot in the Hunt for What Sells”:. Los Angeles Times. Sep 12, 2001. pg. F.1
92
“GYC” Personalities
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
http://www.africanamericans.com/images2/BillCosbyTimeMag.jpg
572
and Tivo Box
• Auditing
VIII. CONCLUSIONS
575Need?
IV. Demand
Experiments
573
576
Demand Experiments
1.
2.
3.
4.
574
Test Marketing
Uncontrolled Studies
Controlled Studies
Laboratory Experiments
577
93
IV.1. Test Marketing
• Launch the media product
with a full marketing and
advertising
g plan
p in several
test cities
ii
–Film
–TV show
• Track consumer response
Example: TV Show in Small
Country
• The Dutch media producer Endemol
uses the entire Dutch market to test
shows for an international rollout.
rollout
578
Aris, Annet, “Value-Creating Management of Media Companies: Chapter 5”,
McKinsey & Company, Inc., 2003
581
Test Marketing
• Problems: Premature exposure
of the product to competitors.
• Done for films, with initial
limited roll
roll-out
out
–Incl. exit interviews
IV.2.
U
Uncontrolled
ll d
Studies
579
• Enable decisions about further
development, adaptations/finetuning, and discontinuation.
582
• uncontrolled:
–researchers
researchers
are only
observers
http://www.unesco.kz/culture/projects/whc/photos/Observers,%20Ms
.%20Kirillova,%20Khorosh%20and%20M.%20Rogozhinski.JPG
Aris, Annet, “Value-Creating Management of Media Companies: Chapter 5”,
McKinsey & Company, Inc., 2003
580
583
94
• In contrast, in controlled
research:
–researchers manipulate the
important variables to observe
their effect.
effect
» more accurate but more costly
and time-consuming.
584
Uncontrolled Research Using Past
Sales Data
1. Aggregate sales data of a
single company
2. Sales data for an individual
retail
t il outlet.
tl t
3. Panel data- individual purchase
reports from members of a
selected consumer panel.
• accumulate observations
more quickly
• One
O can correlate
l t price
i
sensitivity with demographic
classifications
587
Panel Data
• Purchases by panel
members can now be
recorded automatically by
s o e POS
OS sc
scanners
es
in-store
- customers could reveal
their demographics in
return for some store credits
or coupons.
http://www.lib.sfu.ca/about/services/checkout.jpg
585
Panel Data
• Marketing research companies
collect individual purchase data
from panels of several thousand
households.
• Each household keeps a daily
diary of items purchased and
their prices.
Panel Data Advantages
586
588
Examples
• Book stores
• Music stores
589
95
Case Discussion: Viacom
“Golden Years”
Golden Years Cable
Marketing
• Golden Years Media may conduct
demand experiments to identify
which
hich products
prod cts their viewers
ie ers buy
b
•Q
Question for Viacom
research: who advertises in
magazines that target the
age group 65 plus?
590
*
Golden Years Research
Who advertises to 65+?
1. Golden Years Media can obtain
data about their target households.
Such data can be used to analyze
price sensitivity, etc., with respect
to demographic variables.
• Insurance Companies
– Life
– Automobile
– Health
– Homeowners’
• Financial services
– Telecom, cable TV, internet
591
Who advertises 65+?
• Travel
– Travel agency
– Airlines
– Tour operators
• Pharmaceutical drug companies
• Food companies
Source: http://assets.aarp.org/www.aarp.org_/articles/benefits/fullbenefits.pdf
592
595
96
IV.3. Controlled
S di off Actual
Studies
A
l
Purchases
596
Experimentally Controlled
Studies of Actual Purchases
• Generate price variations
while holding
g constant other
variables, such as
advertising.
599
In-Store Purchase Experiments
• Such a study can easily
cost several million dollars
• Cost
C t off experimentation
i
t ti is
i high
hi h
because each additional factor
studied requires the inclusion
of more stores as control.
597
600
Controlled Experiments
• buyers are unaware they are
participating in an experiment
• Prices can be varied
• Can also be done for mail-order,
by special offers to a subset
http://www.answers.com/main/content/wp/en/thumb/b/b0/350pxSupermarket_check_out.JPG
598
• For example, when Quaker
Oats conducted an in-store
experiment that focuses on the
effect of price alone, the study
required 120 stores and ran for
three months.
www.quaker.fr/
601
97
• Also, charging lower prices
can become too costly for
large-expenditure such as a
TV set or computer
• This leads to the use of
laboratory experiments
602
Amazon’s Controlled
Experiment
• Amazon wants to find out
whether a new design of a
webpage
b
iincreases sales.
l
• Run a controlled experiment
with a Web page.
605
IV.4. Laboratory
Purchase
Experiments
Varian, Hal R. “Kaizen, That Continuous Improvement Strategy, Finds Its Ideal
Environment.” The New York Times, February 8, 2007.
Amazon’s Controlled
Experiment
• Amazon shows a different page
design to every hundredth
visitor.
visitor
• Determination of whether the
new design increases sales can
be made in only a few days.
Varian, Hal R. “Kaizen, That Continuous Improvement Strategy, Finds Its Ideal
Environment.” The New York Times, February 8, 2007.
606
• Using a
research
facility at a
shopping mall
- simulated
stores the size
of small
convenience
stores.
http://www.we-make-money-notart.com/xxx/FF_150_shoppers2_f%5B1%5D.jpg
607
98
Laboratory Purchase
Experiments
• Attempt to duplicate the
realism of in-store
experimentation without the
high cost and exposure to
competitors.
608
Laboratory Purchase
Experiments
Example for Experiment:
Magazine Test Marketing
• The researcher controls who
participates and can
manipulate prices etc.
609
http://campaignsolutions.com/hdcs/mail/accent.jpg
612
Magazines: Direct Mail
• Reward for participating is a
substantial discount
• The cost of laboratory
experiment is much smaller
than for in-store testing.
• Popular approach by
consumer electronics makers
• “Dry Test”
- the product is tested without
being published
- solicitation letters sent out to
potential readers
- the first issue may be years away
http://www.shoplet.com/office/limages/EB021980.gif
610
James Kobak “Testing a New Magazine Through Direct Mail,” How to Start a Magazine
613
99
• Also allows the magazine
company to determine which
combination of design, prices,
offers, advertising copy, and
mailing lists work the best.
http://www.ptarmigan.co.uk/New%20Pages/DM.html
614
• Combining test results with
demographic characteristics
helps a magazine to
determine best target zip code
set, and which other
characteristics to focus on
(Income? Race? Gender?
Optimal Age?)
615
617
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
and Tivo Box
• Auditing
VIII. CONCLUSIONS
• Is This What Media Firms Need?
618
V. Measuring
Actual Sales
619
100
Methods of Measuring Actual
Sales
• Books: Bestseller List
• Music: SoundScan
• Film box office
• RFIDs?
620
623
The List is Self-Fulfilling
V.1. Books
Bestseller List
621
• Determines book location inside
the store
–Substantial effect on book sales
• Determines whether or not the
book will be discounted
• Compiled from hundreds of book
stores
–identity and weight given to each
store is not disclosed
624
Bestseller List
• Measured by New
York Times,
Publishers Weekly,
Bookk Industry
d
Trends, Wall Street
Journal, USA
Today
http://images.amazon.com/images/P/044022165X.01.LZZZZZZZ.jpg
http://images.amazon.com/images/P/0451169514.01.LZZZZZZZ.gif
• System is basically a very big
sampling of retailers.
622
625
101
Manipulating Best-Seller Lists
to Create Audience
• Sampling system of New York
Times Best-Seller list is suspect
• “Padding”
Padding the List
–Publishers buy their own books
in bulk from stores around the
US to get their sales up for the
626
NY Times list
• Business consultants Michael Tracy
and and Fred Wiersema, authors of
The Discipline of Market Leaders,
spent $250,000 to buy 10,000 copies
g it a Bestof their own book,, making
Seller. The book spent 15 weeks on
the list.
627
Fred Wiersema
Michael Tracy
•
http://ecx.images-amazon.com/images/I/71Q44K6FSCL._SL500_.gif
Other’s Best-Seller Book List
• Wall Street Journal offers
“transparency” of tabulating
sources
–No “weighting”
“ i hi ”
–reflects raw sales with no
weight given to any source
• USA Today: point of sale
630
http://battellemedia.com/archives/old%20book%206.gif
USA hardcover fiction bestsellers 2004
• eventually sold over 250,000
copies.
•NY Times now places a
dagger
gg next to any
y title
when substantial bulk
sales are being reported at
individual stores
http://www.majoritynews.com/images/ny-times-logo-paper.jpg
Rank
#
Author
Publisher
# of
copies
Share
The Da Vinci
1 Code
Brown,
Dan
RANDOM HOUSE
3,218,535
19.5%
The Five
People You
Meet in
2 Heaven
Albom,
Mitch
LITTLE, BROWN &
CO PUB
2,065,165
12.5%
Angels &
3 Demons
Brown,
Dan
SIMON &
SCHUSTER
774,668
4.7%
Grisham,
4 The Last Juror John
RANDOM HOUSE
768,609
4.7%
The Rule of
5 Four
Caldwell,
Ian
RANDOM HOUSE
624,956
3.8%
6 State of Fear
Crichton,
Michael
HARPER COLLINS
PUBLISHERS
429,351
2.6%
Title
628
102
USA trade paperback fiction bestsellers 2004
Rank
#
Title
The Secret Life
1 of Bees
Author
Publishing
Conglomerate
# of
copies
Kidd, Sue
Monk
PENGUIN/PUTNAM
TRADE
865,600
7.0%
The Curious
Incident of the
Dog in the Night2 Time
Haddon, Mark
3 The Wedding
The Lovely
4 Bones
5 Life of Pi
RANDOM HOUSE
574,294
4.6%
Sparks,
Sparks
Nicholas
WARNER BOOKS
538,139
4.3%
Sebold, Alice
LITTLE, BROWN &
CO. PUB
523,596
4.2%
Martel, Yann
HARCOURT,
BRACE &
COMPANY
522,309
4.2%
HARPERCOLLINS
PUBLISHERS
508,381
4.1%
PENGUINPUTNAM
TRADE
500,338
4.0%
One Hundred
Marquez,
6 Years of Solitude Gabriel Garcia
7 The Kite Runner
Share
Hosseini,
Khaled
USA mass market paperback fiction
bestsellers 2004
Rank
#
Title
Author
Angels &
1 Demons
Brown,
Dan
SIMON &
SCHUSTER
2,194,249 13.4%
2 Deception Point
Brown,
Dan
SIMON &
SCHUSTER
1,024,273 6.3%
ST. MARTINS MM/
ST
HOLTZBRINCK
1,005,214 6.1%
4 The Notebook
Sparks,
Nicholas
WARNER BOOKS
671,147 4.1%
5 The King of Torts
Grisham,
John
RANDOM HOUSE
654,215 4.0%
6 Bleachers
Grisham,
John
RANDOM HOUSE
516,091 3.2%
Key of Valor: The
7 Key Trilogy
Roberts,
Nora
PENGUIN/PUTNAM
TRADE
489,838 3.0%
8 The Guardian
9 Blue Dahlia
635
Music Sales – POS System
Publishing
Conglomerate
Digital Fortress: A Brown,
Brown
Dan
3 Thriller
# of
copies
V.2. Music Sales
Share
Sparks,
Michael
WARNER BOOKS
Roberts, Nora
PENGUIN/PUTNAM
TRADE
431,930 2.6%
485,649 3.0%
10 The Last Juror
Grisham, John RANDOM HOUSE
399,925 2.4%
20 The Lake House
Patterson,
James
WARNER BOOKS
241,921 1.5%
To Kill A
21 Mockingbird
Lee, Harper
WARNER BOOKS
236,337 1.4%
The Catcher in
26 the Rye
Salinger, J.D.
WARNER BOOKS
215,191 1.3%
30 Full Blast
Evanovich,
Janet
ST. MARTINS
MASS
192,373 1.2%
37 1984
PENGUIN/PUTNAM
Orwell, George TRADE
178,699 1.1%
38 Fahrenheit 451
Bradbury, Ray
RANDOM HOUSE
175,725 1.1%
40 Safe Harbour
Steel, Danielle
RANDOM HOUSE
172,281 1.1%
50 Odd Thomas
Koontz, Dean
R.
RANDOM HOUSE
144,808 0.9%
http://www.savagebeast.com/images/best-buy-inlines.jpg
636
• Old systems: Selected retailers
(sample) were contacted filled
out forms, and returned them to
Billboard,, Magazine
g
–reporting often was
inaccurate, merely
rank-ordered
637
–Possible to manipulate
103
Improvement through “POS” [Pointof-Sale] SoundScan System
• SoundScan (by Sound Data) in 1987.
Computerized data collection system
with bar-code scanningg byy retailers
• SoundScan claims to measure 85% of
all music sales in US.
638
641
http://www.mixrevolutionblog.com/wp-content/uploads/2007/11/billboard_vinyl.jpg
http://www.whiteeaglerecords.ca/soundscan-logo.gif
• Point-of-sale purchases are
tabulated from 4,000 chain
record stores, 700 independent
retailers and 7,000 discount and
d
department
stores, and
d online
li
stores (`~14, 000 outlets in 2003)
639
• Billboard magazine uses Sound
Scan since 1991
• Billboard Top Album Lists tracks
the number of units sold and
popularity of particular songs
• Used also by performing rights
organizations (ASCAP, BMI) to
track royalties
640
ASCAP Page 676
http://gothamist.com/attachments/arts_jen/2007_08_arts_ascap.jpg
642
• SoundScan owned by Nielsen
• also offers BookScan and
VideoScan
643
104
The Mystery of DVD Sales
• DVD sales information is
important to actors, directors,
and writers for royalties and
profit
fi information.
i f
i
- distributors usually hype a film’s
initial DVD sales, but do not release
periodic sales information thereafter
V.3. Direct Sales:
Measuring Film
Audiences
John Horn, “DVD sales figures turn every film into a mystery,” Los Angeles Times, April 644
17, 2005, Calendarlive. 15 June 2005.
• In consequence talent agencies
and management firms have
created research teams to
check on DVD revenue and
costs
costs.
• Or specialized companies
–Adams Media Research
(AMR)
John Horn, “DVD sales figures turn every film into a mystery,” Los
Angeles Times, April 17, 2005, Calendarlive. 15 June 2005.
647
Film Ticket Data
• Exhibitor Relations Co.
–Collects box office attendance
from Studios
–Reports to the media every
week
645
www.cinedom.de
LA Times, “Tinseltown Spins Yarns, Media Take Bait”, David Shaw, Feb 12, 2001
648
Film Audiences
Sunday (am) –
theatres report
Fr/Sa ticket sales
Media - Monday Box
Office Report
Company collects
info from studios,
and reports to
media
Chosen theatres
in key markets
Studios
extrapolate
Fr/Sa data to
guess Su
646
Exhibitor Relations Co.
Extrapolate for smaller
markets estimate
649
105
Film Box Office Weekly Report
Weekend Top 30 Box Office
650
Movie Reporting Criticism
- To make sure theaters are
not misreporting the number
of tickets sold, distributors
employ
p y undercover checkers,,
who buy numbered tickets at
the first and last shows at
randomly selected theaters.
Epstein, Edward Jay, “The Big Picture, The New Logic of Money and Power in
653
Hollywood,” New York: E.J.E. Publications, Ltd., Inc., 2005
Direct Sales Data
• Film studios also receive direct
information from national and
regional multiplex chains in the
United States and Canada.
Canada
• Potentially Inaccurate
–The numbers are “made
up”—fabricated every
week” (Anne Thompson,
editor, Premiere magazine)
http://www.gjdc.org/images/Multiplex%20Cinema.jpg
651
Movie Reporting Criticism
• Potentially manipulative
–The studios extrapolate the
Sunday figures from the
Friday-Saturday
y
y figures,
g
, based
on experience.
–Want to have the number one
movie of the week.
–Exaggerate, to drive future
sales
652
654
• Studios also conduct exit
polls, to determine the
demographics of audiences.
655
106
RFID:
• Nielson National Research
Group (NRG) main tool for
film audience research but
others were catching up.
• A passive radio transponder
with view-ware that reflects
an integrating
i t
ti radio
di signal
i l
received
Dutka, Elaine. “Audience Tests: Plot Thickens.” 31 August 2003. Los Angeles Times. Last656
accessed on 4 June 2007.
659
RFID
• The RFID tag is a small integratedcircuit chip with a radio and
identification code embedded into it,
which can be scanned from a
distance
distance.
• likely to replace barcodes.
V.4. RFID
Tracking
657
More Refined Tracking: RFIDs
(Radio Frequency Identification
• As passive
(unpowered) RFIDs tap
prices
i
come down
d
to
t
pennies, it is on the
verge of becoming
major measurement
658
tool
http://www.pdcorp.com/healthcare/photos/chip_hand.jpg
660
RFID in tracking merchandize
•In 2005, Wal-Mart required its top 100
suppliers to apply RFID labels to all
shipments, so as to improve supply chain
management
•Next step to tracking at POS with potential
ID and profiling of use potential to
consumer’s home.
•Research tool for real time audience analysis
Source: IEEE Computer Society, RFID: A Technical Overview and Its Application to the Enterprise
http://doi.ieeecomputersociety.org/10.1109/MITP.2005.69
661
http://www.elektroniknet.de/topics/kommunikation/fachthemen/2003/0021/images/3190908_kl.jpg
107
RFID
• Samsung developed RFID fridge:
- suggest recipies based on what
you have in fridge or compiling a
shopping list…
• Same idea could be used for
music CDs- suggested play list
for the evening
VII. SelfReporting
–Could be linked to media company
for audience analysis
665
662
Tracking “Best of Golden
Years” DVDs
• An RFID tag will enable
“Golden Years” to track every
i di id l DVD purchased.
individual
h d
• This allows an accurate
measurement of all sales.
VII.1. Measuring
Circulation
• Producer Self-reporting
• Circulation Verfication
• Problems with Measuring
circulation
663
666
A. Producer SelfReporting
• Mainly used by newspapers,
magazines
• Each
E h media
di company sends
d
reports on circulation, ad sales
and other relevant information
to a central unit
664
667
108
Producer Self-Reporting
ABC Board
• The central unit compiles the
information and prepare
different reports
• The central unit also
responsible for auditing
668
Central Self-Report
Model
Magazine Z
ABC process
Advertisers
Reports
Central
Unit
Magazine W
Reports
Magazine Y
Specified Data
(Circulation, ads, etc.)
Magazine X
Audit Bureau of
Circulation (ABC)
• 12 advertiser and ad-agency
directors
• 6 daily newspaper directors
• 3 magazine
i directors
di t
• 1 director representing weeklies,
farm publications, business
publications and Canadian
671
periodicals
• Half yearly, newspaper
members supply publisher’s
statements that detail how
and where each copy sold.
• Once a year, ABC audits
sales
669
• Began in 1914
• formed to audit and verify
circulation
• Before ABC, advertisers had to
face boasts about sales.
• Led to overprinting and dumping
• Advertisers and ad agencies
create ABC to sort the mess 670
672
Publisher's Statements
• Twice a year, ABC requires
each magazine and newspaper
member to submit a statement
of their circulation -- known as
a Publisher's Statement.
http://www.accessabc.com/aboutabc/index.htm
673
109
Sample ABC Report
Issue: How to Define
Circulation?
674
677
Newspapers “circulation”
•Newspapers also conduct
telephone surveys(sampling)
–Simmons, (large consumer
research firm),
firm) conducts
newspaper reader research
• Circulation = paid subscriptions
+ newsstand sales
http://www.michaeljacksontalkradio.com/Journals/MJs_Journal04_0317.htm
675
Problems with Measuring
Newspaper Readership
•
•
Information about section or even
story readership difficult to obtain
D
Demographic
hi
information not
part of selfreporting
676
678
• How to count bulk copies to
hotels, businesses, hospitals?
–How steep can discounts be?
679
110
• The ABC specifies that a paper
must be sold for at least 50%
of its normal price to be
counted as paid circulation.
680
• Excluding third-sales the
average paid circulation of
USA Today and The Wall
Street Journal would have
dropped 2%.
JACQUES STEINBERG AND TOM TOROK, “Your Daily Paper,
683
Courtesy of a Sponsor,” The New York Times, January 10, 2005, C6
Mis-Reporting of Circulation
Numbers
• 2004: Belo Corp. (Dallas Morning News
and other papers, and 19 TV stations)
–Investigation on false numbers
–Counted unsold papers
–Overstated circulation 5.1%, Sundays
11.9%
• Refunds $23 Mil, loses advertiser
confidence
http://www.experientia.com/blog/uploads/2007/03/usa_today.bmp
681
Newspapers and Third-Party Sales
684
Belo Corp.
• Problems with counting papers
distributed for free by 3rd parties
• Over third-party sales to buys
by external companies that
distribute them for free (e.g.
hotels, airlines)
http://mowabb.com/aimages/archives/003933.html
JACQUES STEINBERG AND TOM TOROK, “Your Daily Paper, Courtesy of a Sponsor,” The New York Times,
682
January 10, 2005, C6
http://cache.daylife.com/imageserve/07kf7XU5UEcuB/610x.jpg
685
111
http://www.billnealonline.com/siteassist_images/DMNews.jpg
686
http://sadbastards.files.wordpress.com/2006/11/sun-times-small.jpg
689
Newspaper Circulation
http://www.cartoonstock.com/directory/c/circulation.asp
http://www.dyingwell.com/images/newsday.jpg
687
Mis-Reporting of Circulation
Numbers
• Other mis-reporting newspapers:
–Hollinger (Chicago Sun-Times)
–Tribune
Tribune Co. (Newsday, Hoy, etc.)
–Counted unsold copies not
returned
–Criminal investigation
–Overstated 40,000 copies,
688
Sunday, 60,000 copies
690
Redefining a Paid Paper
Many of the country’s largest newspapers have been counting
papers paid for by a third party, like an advertiser, as part of
their paid circulation. Here are some of the larger newspapers,
ranked by use of third party sales
Newspapers with circulation
of 250,000 or more
Publisher
Total paid
Circulation, six
months, ended March
2004
Third Party Sales as
percentage of total paid
circulation, 2004
USA Today (Fridays)
Gannett
2,635,412
18%
Thee Denver
e ve Post
ost
MediaNews
ed aNews
783,274
783,
7
13.2%
3. %
The Wall Street Journal
Dow Jones & Company
2,101,017
8.4%
The San Jose Mercury News
Knight Ridder
308,425
8.3%
The Houston Chronicle
Hearst Newspapers
740,005
8.2%
The Miami Herald
Knight Ridder
447,326
6.8%
The Philadelphia Inquirer
Knight Ridder
769,257
5.8%
The Boston Globe
The New York Times
686,575
4.4%
The Harvard Courant
Tribune
283,410
4.0%
Los Angeles Times
Tribune
1,392,672
3.8%
691
The New York Times, 10 January 2005.
112
Alternatives to ABC
•BPA International
((business magazine
g
in
20 countries)
•Mediamark Research
(consumer magazines)
692
• Other magazine circulation
reports:
–Folio 400 tracks newsstand and
subscription sales of top 400
magazines
–Magazine Publishers of
America - track circulation for
its 200 member magazines and
693
periodicals
695
I.
• Importance and Special
Problems of Media Demand
Estimation
• Case Discussion: Viacom
Golden Years Media
MODELS
• Statistical Inference
• Econometric Demand
Estimation
• Conjoint Analysis
• Diffusion Models
• Test Marketing
• Controlled Studies of
Actual Purchases
• Laboratory Purchase
Experiments
• Psycho-Physiology
Psycho Physiology Testing
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
• Next Generation People
Meter: The Digital Meter
New Problems: Multi-Platform
• How to measure audiences that use
multiple platforms?
–paper newspaper & online paper
–radio station over-the-air and online
Some online are the same people not
additional ones (for most newspapers
about 15% of visitors are not paper
subscribers).
ABC (Audience Bureau of Circulation)
2006 new “consolidated” product 694
ANALYSIS
I.
• Auditing
CO C
696
SO S
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
and Tivo Box
• Auditing
VIII. CONCLUSIONS
697Need?
113
VI.
Measuringg
Traffic
How do we
know that?
698
VI.1.
pp
to
3 Approaches
Measuring Internet
Audiences
699
Top Websites to US Internet Users
for April 2008
Rank
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Website
Google Sites
Yahoo! Sites
Microsoft Sites
AOL LLC
Fox Interactive Media
eBay
Wikipedia Sites
Amazon Sites
Ask Network
Time Warner –Excluding AOL
Unique Visitors (000)
141,080
140,613
121,213
111,277
87,527
80,903
58,812
58,057
54,086
700
52,544
701
Approaches to Measuring
Internet Audience
A. Site-Level
B. Ad-Level
C. User-Level
702
3 Approaches to Measuring
Internet Audiences
A. Site-Level
– Count website visits. Similar to actual
sales approach
B. Ad-Level
d
l
– Measuring clicks on ads when user is
transferred to advertisers. Similar to
actual sales approach
C. User-Level
– Built by 3rd parties from panel/meter
data, similar to TV ratings approach 703
114
A. Site-Level
Measurement
[
UniqueUsers = 3.2 1 − e (.004599−.090583*Hits )
http://kentaro.blog.ocn.ne.jp/kentarob
log/images/yahoo-search-thumb.jpg
http://www.politicalpuzzle.org/Photos/msn%20se
arch.jpg
]
http://news.bbc.co.uk/1/hi/business/1476
805.stm
704
Site-Level Measurement
• Basically, a self reporting
system by the website or visitor,
• Can potential identify users /
user types/countries, etc.
• Tabulations of page requests
• Most commonly used by
websites
705
707
Internet Measurement
Software
708
http://www.vioclicks.com/pics/signupbig.gif
Nielsen’s Ne Ratings software:
SiteCensus:
• Nielsen//Net Ratings (2003)
• Browser-based measurement tool
• Makes variety of data available to
media owners
Uses for Internet Ratings
• Total website hits can be used
as the basis for determining
unique
q users,, given
g
a
relationship between the two.
• Best fit: modified exponential
function:
• Paths followed
• Content viewed
• Location of access
706
• Includes requests from work,
school, and wireless
709
115
How to Individualize
Information about a Web-site’s
Audience
Server Level Collection
• “Packet sniffing”
–Monitors network traffic
coming to a website and
directly extracts usage data
from TCP/IP packets.
Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan,
“Web Usage Mining: Discovery and Applications of Usage Patterns from
Web Data” in SIGKDD Explorations, Vol. 18: 2, January 2002 p. 13.
710
http://www.krittersinthemailbox.com/
animals/dogs/bloodhound/sc1139.htm
Site-Level’s Systematic
Measurement Biases
• Registration requirements do
not workk well
ll
– Effort to users
– Privacy concern
– fear of spam
713
Major Tool: Cookies
• Overcounting
– repeat visitors
– counts not just people but also bots
and spiders
• Undercounts cached pages
• Can’t distinguish multiple users on of
same computer
711
• Cookies combine the control advantages of a
site-centric approach with the
individualization of the user-centric approach
• A standard programming device that
produces electronic files to tag individual
customers with a unique identification.
– Allows a website to recognize an
individual.
Deck, Cary A., “Tracking Customer Search to Price Discriminate.” Electronic Inquiry,
714
April 1, 2006.
Problems with Site-Level
• Knows IP address or technical
details, not user identity.
http://www.montanahope.org/graphics/bears%20and%20computers.JPG
712
715
116
Inflated Click Rates
B. Ad-Level
Measurement
716
• Creating fake clicks
• robot hits
• This has become a big
problem
• Fake clicks by people
has become a cottage
industry in India
http://ewic.bcs.org/images/robot.jpg
719
http://www.smarteque.com/
Click-Through (CTR)
Software
• Measures whether user clicked
on an ad to link to the advertiser
• Major Abuses of Pay-PerClick:
–“Click fraud” not illegal
–Portals like Yahoo have
disincentive to crack down,,
incentive to click fraud, through
sharing of PPC that are charges to
advertisers
–Attempts for techno-fixes have
failed
717
720
• Valuable to advertisers:
measures actual effect of web
advertisement; unique to
Internet
• Some per-click payments quite
high--$20!
• Usually < 1$
718
721
http://www.answers.com/main/content/wp/en/thumb/0/03/325px-Pop-up_ads.jpg
Click-Through (CTR)
Software
117
C. User-Level
Measurement
722
http://www.infosystem.gr/images/computer_user3.jpeg
User-Level Measurement
• A Sampling technique
• Drawn from TV audience sampling model
– Large panel of randomly selected users
– Software meter on user’s PC measures
b h i
behavior
– Meter reads the URL in the browser,
counts, and forwards data to web-rating
company
Source: Scott MacDonald
725
http://www.mediasmart.org.uk/images/photos/girl_on_computer.jpg
Advantages of User-Level
Approach
• Uniform measurement -->
comparability
•p
provides demographics
g p
• Counts pages actually received
• Measures actual behavior (not
self-reported)
• No conflict of interest
723
Data Processing
• The data are matched to
“dictionaries of the Internet,”
which categorizes the millions
of recorded URL’s
724
726
• Requires user
cooperation.
• Incentives are
offered to users
who are willing
to use the
browser.
http://www.heart-disease-bypasssurgery.com/data/images/incentive.gif
Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan, “Web
Usage Mining: Discovery and Applications of Usage Patterns from Web Data” in 727
SIGKDD Explorations, Vol. 18: 2, January 2002 p. 13.
118
The Data Meter
• In 1995, Media Metrix installed
the first meter of internet uses,
the “PC Meter,” into a consumer
sample
Methodology
• Sample randomly recruited by
phone and mail. Sample of
50 000
50,000.
http://www.queensferry-pri.edin.sch.uk/nursery/photos/computer2.jpg
http://www.netprointer.com/image_file/seo_image/image021.gif
728
Problems with UserCentric Measurement
Web Ratings War
• Nielsen, a news monopolist in
TV ratings but not in web
ratings
– 100 web ratings companies,
such as comScore, Hitwise
Johnnie L. Roberts, Newsweek, Nov 27, 2006
• Disadvantages to small sites
which may get only a few hits
and may be ignored or
undercounted
• Poor site diagnostics (no good
info on sites and what user does
there)
732
729
Web Rating Companies
Cookies
• Online retailers can use
cookies to post dynamic,
customer-specific
t
ifi prices.
i
(Nielsen)
Source:Web rating: Heavy traffic ahead, The Industry Standard 9/18/00
731
730
Deck, Cary A., “Tracking Customer Search to Price Discriminate.”
Electronic Inquiry, April 1, 2006.
733
119
Study Results
VI.2. Data Mining
734
Data Mining
• Total time spent on a Web page
and total time spent scrolling the
mouse is a reliable indicator of
interest.
• The number of mouse clicks is
not a good indicator of interest
Mark Claypool, David Brown, Phong Le, and Makoto Waseda, “Inferring User
Interest,” in IEEE Internet Computing, Vol. 5:6, November 2001, p. 37.
737
Web Usage Mining
• The Internet also
provides a powerful
tool for additional
analysis
• The capacity to track
users’ browsing
behavior
http://www.nada.org/Images/Technology_image3.gif
735
Mouse Activity
- number of clicks
- time spent moving
the mouse in
milliseconds
illi
d
- time spent scrolling
• Demand of internet sites can
be measured using web usage
mining.
• This
Thi process is
i a data
d t mining
i i
technique used to find the
usage data of web sites so
web applications can be used
better.
Srivastava, Jaideep, Cooley, Robert, Deshpande, Mukund, & Tan, Pang-Ning. “Web Usage
738
Mining: Discovery and Applications of Usage Patterns from Web Data.” SIGKDD Explorations.
1,
no. 2 (January 2000):12-22.
Web Usage Mining
• Pattern discovery is the usage of
algorithms to find usage patterns.
http://www.dalveydepot.com/DalveyBMS.jpg
Mark Claypool, David Brown, Phong Le, and Makoto Waseda, “Inferring
User Interest,” in IEEE Internet Computing, Vol. 5:6, November 2001, p.
736
35.
Srivastava, Jaideep, Cooley, Robert, Deshpande, Mukund, & Tan, Pang-Ning. “Web Usage
739
Mining: Discovery and Applications of Usage Patterns from Web Data.” SIGKDD Explorations.
1,
no. 2 (January 2000):12-22.
120
User-Centric
• Obtaining data from userlevel method of measurement
would
ld be
b helpful.
h l f l But
B t user
panels probably do not cover
GY’s older demographics
well
743
Case Discussion:
How to Measure the
Usage of the “Golden
Y
Years”
” Internet
I t
tP
Portal?
t l?
741
Ad-Centric
• Measuring Ad-clicks/hits from
GY’s website to advertising
sites helps Golden Years Media
in two ways:
–Raises advertising revenues
–Provides information on what
interests the visitor.
744
http://www.thrombosis-charity.org.uk/support.htm
Site-Centric
How Do We Know
How Many Internet Users
“Golden Years” Attracts?
How Many
Users Read
Its Ads?
742
• A website “hit” counter can
collect data on the number of
hits/clicks to GY Portal to
measure demand for the website.
Together with cookies, this
would provide good information
about GY’s online audience.
745
121
Tools
C
Covered
d
746
I.
ANALYSIS
Years Media
MODELS
•
•
•
•
Conjoint Analysis
Diffusion Models
• Test Marketing
Purchases
V. MEASURING SALES
•
•
•
•
Books: Bestsellers
Music Sales
Film Audiences
RFIDs
VII. SELF-REPORTING
and Tivo Box
• Auditing
VIII. CONCLUSIONS
747Need?
VIII.
C
Conclusions
l i
748
749
In this Chapter, we covered the
following Analytical (not
technical) Tools for Demand
Estimation:
• Statistical inference and
sampling
• Delphi and Comb analysis
• Audience model-building
• Econometric demand estimation750
Tools (cont.)
• Construction of Upwardsloping demand schedule
(Network effects)
• Design
i off surveys
• Paretian revenue distribution
• Conjoint Analysis
• Epidemic models of diffusion 751
122
Tools (cont.)
• AQH, AF, Qumes audience
metrics
• Relation of ad revenues to
macro-economy
• Controlled Experiments
• Panel data use
• Internet surveys
Issues
• Nielsen & Arbitron
methodologies
• People meters and PPV
• POS measurement
• Self-reporting methodology
• Click-counting
752
755
Issues
Tools (cont.)
• Psycho-physiological
techniques
• Statistical estimation of demand
• Forecasting methodologies
• Internet methodologies
• Etc., etc.
753
756
Issues
Issues
C
Covered
d
• Special Problems of Demand
Estimations
• Analytical & Statistical Models
• Econometric Models to Estimate
Demand and Related Problems
• Problems of Diffusion Models
754
757
123
Issues
• Nielsen & Arbitron methodologies
• Measure Internet Traffic: site-level
measurement user-level
measurement,
user level
measurement, and user-centric
measurement
• Internet Self-reporting
758
Case Discussion
Viacom “Golden Media”
Should Viacom survey potential
viewers? How?
761
Case Discussion:
Econometric Estimation
Issues
• Special Problems of Demand
Estimations
• Analytical & Statistical Models
• Econometric Models to Estimate
Demand and Related Problems
• Problems of Diffusion Models
• “Golden Years” VOD
–What
What price to charge?
–Need to find price elasticity
of consumers
759
762
• Need to specify a “model” for
statistical estimation
• Example:
• Q is the total number of VODπ
orders by subscribers
760
763
124
“GYC: Historical
Analogy
• “Golden Years” can forecast
GYC’s market penetration by
analyzing the growth of a
similar channel.
764
• And if there is no channel
dedicated to people 65+, it may
be possible to make estimations
based on the growth of a channel
targeted for a specific population,
such “Lifetime” television for
women, the N (teens), Spike TV
(men), Logo (gay), or BET
765
(African-Americans)
• Demand for its still now existent
products
• Characteristics of viewers/readers
• Willingness to pay
• Characteristics of non-buyers
• Interest by advertisers
• How to portion its products
• How to plan marketing strategy
• How to plan pricing strategy
• What the audience likes/dislikes about
767
• We now understand better the
potential actions and their
effectiveness.
768
To predict the audience for the
GY cable channel
• We asked the questions
- how can Viacom determine
demand and related
information for still non
nonexistent products
• Early planning: - personal surveys
- Focus groups
- Conjoint analysis
- Delphi Surveys
- Diffusion studies
766
769
125
Planning
For Golden Years Magazine
• Same as for GY Channel, thus
achieving synergies
- add: direct mail test grid survey
- add: actual rates data
- add: surveys of actual subscribers
- drop: people meters and cable box
• Content stage
- Focus groups
- Test marketing
- Psycho-physiological tests
770
Once channel is running
• Phone surveys
• People meters (if audience is large
• Cable
C bl box
b
• Econometric studies
771
For attracting advertisers to
audience
• phone surveys of viewers
• controlled
t ll d marketing
k ti researchh
for impact ads
772
773
For the Website “The GY
Postal”
• Use some of the same information
• Add: cookies (on user PC)
• Add:
Add click
li k data
d t (on
( ads)
d)
• Add: data on visitors (website)
774
• We can see that there are a large
number of approaches to collect
data
• In near future, the tools of online
and video tracking will permit a
real-time matching of audience,
including the choices of nonviewers in the target
demographics
775
126
•
•
•
•
Thus, strength in data collection
But how is the data used?
This is the weakness: research follow on
Current methodologies are pretty impractical
- econometrics (need data, must project past
into future)
- identification of references by sociodemographics
- epidemic model projections
- trade-off (conjoint)
Should media companies
use demand estimation
techniques, like a car
manufacturer or an
airline?
776
• No strong link to behavioral
models and analysis (psych,
sociological, behavioral
economics)
• This
hi is
i the
h challenge
h ll
– not just more data
–But more advanced “data
mining”
779
1. Should One Avoid
Forecasting on Practical
Grounds?
777
• Manyy are inclined not to
forecast at all before
launching a media product
because forecasts are so
inaccurate.
challenges
and alternatives.” New Infotainment Technologies in the Home. Demand780
So we covered a lot of ground.
But a last and important
question remains beyond
techniques, and technologies,
and technocratic
management: whether such
techniques are really what
media firms need
Critics of MBAs in News Media:
• “It is a fantasy to believe that a
newspaper can be designed and
packaged like a bar of soap or a
can of dog food or even like a
television news program.”
–Leo Bogart, retired executive VP of the
Newspaper Advertising Bureau
778
Doug Underwood When MBAs Rule the Newsroom: How the Marketers and Managers
Are Reshaping Today’s Media. New York: Columbia University Press, 1993, pp. 3-13.
781
127
The Limits of Conventional Research
for Newpaper Audience
• No longer viewed as a Panacea for
circulation problem;
• Often mere restatement of common
sense at the
h most
• OftenCommunication problem between
researchers and decision makers
2. Should Media
Companies Go Beyond
Short-Term Efficiency?
•Need for theoretical models editors can
follow
785
Philip Meyer “Limitations in Conventional Newspaper Research” The Newspapaer Survival Book, 782
Bloomington: Indiana University Press
The Limits of Conventional
Newpaper Research
783
Entertainment
• Disney ex-CEO Michael Eisner:
Research is good on past or present,
not on future.
• Audience
wants
originality,
up to a
point.
784
http://www.azcentral.com/arizonarepublic/news/gifs/0911eisner.jpg
•Do media owe its audience a
special
p
responsibility
p
y to ggo
beyond what its audience
wants ?
- unpopular news stories
- breaking taboos
786
Should one Avoid Measurements
on Principled Grounds?
Time, Inc. Former
Editor-in-chief Norman
Pearlstine:
Balance between seeing
readers what they want,
and what we think they
need.
http://image.pathfinder.com/fortune/conferences/globalforum/625.jpg
787
128
“There’s always been a balance
between educating your reader
and serving
gy
your reader… you
y
obviously balance telling them
what you think they ought to
read with giving them what they
want to read…”
• As often the case, both side are
partly right.
• Advertising, PR, and media
content itself shape
p public
p
• But audiences also reward
originality, and many do not
want to be pandered.
788
Recall the earlier question:
• Does the audience’s demand
shape the content supply?
• Or does supply—by large
media firms—shape viewer
preferences and demand?
791
• Creativity required not only in
the media product itself,
•But also in understanding the
audience’s needs,, tastes,,
preferences, desires, fears.
789
•These demand factors are often
subconscious, unarticulated by
792
audience
So, is demand analysis
• Are media demand-driven?
–As much of the audience
research techniques imply?
• Or are they supply-driven? As
marketing activities imply?
790
• “bean-counting” by uncreative
minds
• Tool for pandering to audiences
rather than of leadingg them?
793
129
• A manager should not make the
choice between judgment and
empirical estimation.
• Used effectively, they are
complementary
complementary.
to Profitable Decision Making,” Second Edition 1995
797
• The avant-garde media manager is
3 steps ahead of audience
To Conclude:
•Conventional media managers
follow the audience by one step,
l tti audience
letting
di
researchh make
k their
th i
decisions
• Determining and analyzing
demand for media is
i
increasing
i in
i its
it technological
t h l i l
sophistication
•The moderately successful media
manager: probably one step ahead,
using audience research
795
• The successful innovator: 2
steps ahead, creative
understanding of audience,
market and society
market,
society, plus
research to lower the risk
796
798
• We now have new technical
tools:
–Internet connectivity for media
consumption
–Local People
p Meters
–Measurement software
–Cookies
–RFID
–Watermarks and IDs
799
http://www.smwinc.com/news/img/03wn/rfid.jpg
130
• It is harder to estimate
demand for new products and
services in a rapid-change
environment, with
fragmented audiences, and
much greater choice, and
shorter attention spans
• These tools provide
enormously powerful
methods of instant
f db k
feedback
800
803
• Media firms will increasingly get
rapid audience data and act
rapidly on them, in the design of
their products, in marketing, and
in ppricing
g
• Thus, demand measurement of
media use will be increasingly
–real-time
–global
global
–large samples
–customized
http://images.google.com/imgres?imgurl=http://210.75.208.159/eolympic/xbj/txtx/image/txtx.jpg
801
804
• As sophisticated as the tools are
which have been reviewed, they are
probably just beginning of to develop
the next generation of tools utilizing
much more advanced
–Behavioral research
–Audience
Audience instant feedback
–Trendsetters
–Cross cultural sampling
–Statistical tools
–Online technology
• But even with these better
tools, it is much harder to do
demand research today
802
805
131
Demand Analysis Becomes
More Important
• The greater the uncertainty
• The greater the upfront
investment
• The greater the economies of
scale and network effects
• The more competitive alternatives
806
• The shorter the product cycle
• Reliance on the “gut feeling”
“intuition” of “single-minded
entrepreneurs and of internal
advocates can be the most
expensive way to learn.
And therefore, I
disagree with the
slogan that “Nobody
Nobody
Knows Anything”.
809
One can improve the odds
• Slightly, but that is enough
for a competitive advantage
807
• Suppose a film has
Cost = $50 mil.
(Probability) P = 20% to gross $250 mil.
(Expected Return) E (R) = .2 x 250 = $50 mil.
E (Profit) = $50 mil cost- $50 mil [E (R)]= 0
• If one can improve the odds from 20% to
22% by
b smarter ddemand
d analysis.
l i
E (Profit) = .22 x 250 = $55 mil
ΔE (Profit) = $5 mil
• Now profit expectation is positive
808
810
“Somebody Knows a Little
Better”
811
132
• Understanding One’s Audience
may be cheapest investment
with the highest return.
End of Lecture
812
815
• And Demand Analysis—
understanding the audience,
customers,
t
market,
k t is
i the
th key
k
to improve the odds.
• We are just at the beginning.
813
133

I: Why Demand Analysis - Columbia Institute for Tele

Transcription

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