Page 95 - MYM 2015

social science research approaches, that is, to
conduct snapshot, point-in-time research studies,
often with rather limited numbers of questionnaires or observations and then to project those ndings to the entire age cohort. (Stringer, 2013) More recently, big data, that is, large samples of captured behavioral and other data which can be related to various consumer groups has begun to be employed. (Manovich, 2011)
It is the increasing availability and potential analysis of this big data, when viewed over time, i.e., longitudinally, that has begun to expose some of the true facts and fallacies about generational beliefs marketers have developed over time. This paper reports on how and
in what way, big data can
provide more extensive
and enhanced views of a
population “generation” or
an “age cohort” than has
been possible in the past
and also exposes the lack
of support for others.
Increasingly, researchers and marketers are learning that, because of the shifting nature
and decisions can be made, in this paper it is argued that the generational classi cations, while useful, may often be misleading for four basic reasons: (a) they are often not necessarily representative of the entire age cohort discussed, that is the infamous “iceberg principle” where only the most visible tip
of the activities or events are being observed and reported, (b) most reports assume that observations taken today will continue into the future, that is, what the various generations or cohorts are doing now is what they will be doing in the future, (c) there is no common agreement of what comprises a “generation” in terms of years, and (d) nally, the assumption that
if the generation
is age speci c,
they will suddenly turn into another generation once a certain age marker
is reached, similar
to an automobile speedometer reaching a decile milestone.
The greatest challenge we have found in
our research is that when a generation
is de ned or an age group identi ed, it
is often assumed
by researchers that everyone in that same “generation” or cohort group, takes
of these generational
age groupings, their
initial ndings on what
generations actually do
and don’t do, can change
quickly and therefore
become outdated. Indeed
some generational data
is proving to be more fad
than fact. For example, it has only been within the past half-dozen years that social media, the current darling of most marketing managers, has been widely used commercially and has started to appear in the marketing and communication literature. (Covet and Saucet, 2014) Yet, much of the breathless prose being written in the popular press about the large groups
of young people who use these tools and techniques has often been based on rather limited, snapshot samples, speculation and often, only media hype by the technology organizations involved. (Covet and Saucet, 2014) Since social media is less than a decade old, we argue most assumptions and projections about this new digital or millennial generation have yet to stand the test of time, and thus, are subject to interpretation and adaptation as we learn more.
While there is clear evidence that age and age groups likely do create cohorts on which marketing analysis
We believe we are
entering a new
era of market and
marketing research.
Thus, we feel
new and different
methodologies will
be required.
on the same characteristics. After numerous research projects, we have often found that the proclaimed generations are often made up of quite different behavioral groups or units within that generation or cohort. In short, a generation is not a generation and a cohort is not necessarily a cohort for all those falling within a certain age group.
Structure of the Paper
In this paper, we mine the increasingly rich lode of consumer information found in big data resources and evaluate that data over time (longitudinally). We have found that this type of analysis can provide a richer and more useful view of the generations; that is, how they have been described and discussed in the past and the value that concept may provide for marketers going forward.
We start rst with a description of the data sets used in this analysis, most of which have come from a commercial
mind I95 your
marketing