Page 94 - MYM 2015

Generational In uences:
Big Data Facts and Fallacies
Don E. Schultz PhD Martin P. Block PhD
MIntroduction
uch has been written (Ward, 1974; Solomon, 2009; Yalch and Spangenberg, 1990), researched (Solomon, 2009)
and discussed (Yalch and Spangenberg, 1990; Solomon, 2009) about the
in uence and impact of age on consumer’s buying decisions (Solomon, 2009) with authors often citing various generational examples. While there is no widely agreed upon de nition of a “generation”, (Kertzer, 1982; Edmunds and Turner, 2005) the concept that persons of a general age cohort, often called “social generations”, are similar in many ways, including thinking, hobbies, purchasing patterns and the like is often, therefore, widely used as a segmentation variable. That has
been well documented in the literature. (Kertzer, 1982; Spitzer, 1973) The most well-known and studied of these “generations” from a U.S. marketing perspective have been the “Baby Boomers”. “Baby Boomers” are those persons born in the U.S. shortly after the end of World War II, and who, because of their number and economic impact on the overall U.S. economy, have been widely studied and researched. Thus, Baby Boomers and their generational cohorts have dominated much American marketing thought for the past 60 or
so years. (Wey Smola and Sutton, 2002) Most recently, the identi cation of a new generation of young persons who are extensively using the new digital technologies has resulted in another research “boom”. (Jones, et
al, 2010) Terminology such as “Millennial” (Howe and
Abstract: Generational data is widely used, and misused in marketing management today. In this exploratory paper, it is argued that big data (longitudinal studies of age cohorts as they change and evolve over time) is a more useful method of understanding generations than the single, snapshot-in-time research approaches often used today. Five postulates are developed and demonstrated to support the view offered and demonstrations of their value are discussed. Managerial implications are provided along with study limitations.
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Strauss, 2009; Lenhart, et al, 2010) or the “Digital Generation” (Montgomery, 2007) is widely found in current research studies.
These “generational tags” seem to assume that most persons born within a certain calendar-period or those using various cultural icons provide suf cient information to be able to categorize them for marketing purposes. Therefore, people having certain lifestyles, media habits, consumption patterns and the like are used to reference fairly large groups of a common population. (Kahle and Valette-Florence, 2012) While some of the assumptions used have proven right, others are beginning to show aws as the generations to which they have been assigned ages. It is a review, test and projection of these generational facts and fallacies, using newly available big data, which are explored in this paper.
Based on this, the following paper has been developed. As will become apparent, this discussion is exploratory and directional and will need further veri cation to be accepted. There is, however, enough solid, big data evidence presented so that a reasonable set of research postulates can be developed. Those are presented in the following paragraphs.
Background
Historically, the most common approach to researching various generational groups has been to use traditional