A
Summary of “Viewer Preference Segmentation and Viewing Choice Models for
Network Television”
In
1990 broadcast television network advertising revenue was approximately $25.5
billion a year, with General Motors alone spending $598.4 million on network ad
time (Rust et al. 1992). As such, both
television networks and advertisers have a common interest in understanding why
people choose to watch television, and why they prefer one program over
another. In the pursuit of this understanding, it is useful to group television
consumers into segments if possible in order to make decisions based on the
shared preferences of large amounts of people. Segments can assist networks and
advertisers with the forecasting necessary in deciding who will watch what
program. In this way, advertisers can spend their ad dollars more effectively,
and networks can maximize profits by designing shows that the most valuable
demographics will consistently want to watch.
The purpose of the
paper is to design models that can address the following questions accurately:
1) Do identifiable
segments exist?
2) How do viewers
decide when to start and stop watching television?
3)
How do consumers decide which television programs to watch (Rust et al. 1992)?
Previous
research on this topic primarily falls into three general types. The first type is “structuring viewing
alternatives”, which attempts to group television programs according to their
similarities differences. These groups
of programs can be defined a priori, without supporting data. They can also be grouped according to
scientifically gathered viewer data.
Both of these two methods must make the assumption that these homogenous
program categories actually exist in the first place. The third method of
grouping does not rely on this assumption, but uses multidimensional scaling in
order to show the relative differences among different shows (Rust et al.
1992).
The second type of previous research is “segmenting
television viewers”, which is similar to the previous type in that viewer
segments are often formed a priori from demographic data. Empirically derived segmentation
on the other hand assumes that watching a television program gives a certain
benefit to the consumer, and that similar programs will provide similar
benefits (Rust et al. 1992).
The third type of previous research is “viewing choice
models”, which attempts to predict which shows consumers will watch. These
models are the most difficult to construct and as a result are much less common
than the other two types. One phenomenon that complicates matters and needs to
be considered here is “audience flow”, which is the way previous conditions
(such as TV on or off, or what the consumer was watching previously) affect
consumers’ future choices (Rust et al. 1992).
Rust
et al. (1992) contend that their model is superior to previous research
because: it combines the three types of existing models into one comprehensive
model, it estimates the preference functions in addition to simply identifying
them, it is open to the possibility that people’s preferences can be different
for reasons other than “simple socio-demographic differences”, and finally that
their model includes the decision to watch television in addition to simply
deciding which program to watch.
The
Rust et. al (1992) model shows the relationship between programs using Nielsen
viewer data and multi-dimensional scaling. They are arranged by preference of
similar audiences rather than seeming to be similar programs. Sample data was
retrieved from 11,501 viewers and tested against the model. The results showed
various ways in which the actual data differed from assumptions often made a
priori.
Once
the characteristics of programs are defined, they can then be matched with the
preferences found in viewers. The closer the program is to the preference shown
via multi-dimensional scaling, the more likely the viewer will watch. The
results showed that this was in fact the case, was statistically significant,
and the model produced similar results when applied to another randomly
selected section of the data (Rust et al. 1992).
Lastly,
the model was used to predict when people will turn their televisions on or off
in the first place. It was found that
different portions of the populace will watch television at different times.
For example, the segment dubbed “Western” was less likely to watch on the
weekend.
This
model shows advantages over previous research, and provides a valuable tool for
analyzing an important body of information.
References
Rust,
Roland T., Wagner A. Kamakura, and Mark I. Alpert (1992), “Viewer Preference
Segmentation and Viewing Choice Models for Network Television,” Journal of Advertising, 21 (March),
1-18.
Questions
1.
In the Rust article, what assumptions did the Rust model make when segmenting
television programs? When segmenting
television viewers?
2.
In what ways would the Rust model apply or not apply if it was used to segment
data on webpages in place of television shows?
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