A Market Segmentation Approach Based on Life Events

EXTENDED ABSTRACT - Numerous segmentation approaches have been suggested in the marketing literature ranging from simple demographics to behavioral, attitudinal, and lifestyle variables. While subjective segmentation bases are very popular among consumer researchers, many practitioners still use basic demographic variables such as age and income. Despite merit, such approaches leave much to be desired. For example, some segments are often too large and heterogeneous (e.g., baby boomers, matures), suggesting the need for further segmentation. Furthermore, there is less than adequate theoretical explanations for the expected differences in the behavior of the different groups.

Citation:

Anil Mathur, Euehun Lee, and George P. Moschis (2002) ,"A Market Segmentation Approach Based on Life Events", in AP - Asia Pacific Advances in Consumer Research Volume 5, eds. Ramizwick and Tu Ping, Valdosta, GA : Association for Consumer Research, Pages: 234.

Asia Pacific Advances in Consumer Research Volume 5, 2002      Page 234

A MARKET SEGMENTATION APPROACH BASED ON LIFE EVENTS

Anil Mathur, Hofstra University, U.S.A.

Euehun Lee, Sejong University, Korea

George P. Moschis, Georgia State University, U.S.A.

EXTENDED ABSTRACT -

Numerous segmentation approaches have been suggested in the marketing literature ranging from simple demographics to behavioral, attitudinal, and lifestyle variables. While subjective segmentation bases are very popular among consumer researchers, many practitioners still use basic demographic variables such as age and income. Despite merit, such approaches leave much to be desired. For example, some segments are often too large and heterogeneous (e.g., baby boomers, matures), suggesting the need for further segmentation. Furthermore, there is less than adequate theoretical explanations for the expected differences in the behavior of the different groups.

The present research advocates the use of life events as bases for market segmentation. This approach is based on life-course research, which has recently developed as an interdisciplinary program for studying various aspects of behavior. The life course approach is based on the proposition that behavior at a given point in time is the product of responses to changing life conditions (such as events, changes or transitions) and the way the individual or other units have adapted to social and environmental circumstances. Based on main life course perspectives, it is expected that changes consumers make in response to changing life conditions will also affect their consumption habits. Because life-course perspectives suggest that the experience of certain life events and their timing may affect people in a similar way, we expect these experiences to be manifested in different consumption patterns. Specifically, it is expected that people who have experienced or anticipate to experience similar life events and circumstances in their lives show similar consumption patterns and responses to marketing stimuli. These life-event experiences are hypothesized in this study to be better predictors of market behavior at a given point in time than competing segmentation models.

Eight hundred and sixty-six adult consumes ages 21 to 84 were used for the study. The sample responded to mail questionnaires sent to 10,000 randomly chosen names drawn from all 50 states. Respondents were grouped into segments using K-means cluster analysis (SPSS). The following variables were used to form the clusters: whether a person had experienced 25 life events, the length of time since each of the 25 events was experienced, and whether and when the person anticipated to experience 14 life events in the next few years. Consumers also responded to 24 consumption-related variables by indicating whether and when they initiated or changed their behaviors (e.g., "bought more gifts than usual"). A total of 78 variables were included in the cluster analysis, composed of 25 variables representing life events experienced, 25 variables representing the timing of such experiences, 14 variables representing anticipated events, and 14 variables representing anticipated timing of such events. Theory and research suggested that the actual experience of an event may have a different effect on the person’s behavior than the anticipated experience of the same event. Similarly, the timing of an event might have a different effect on the person’s behavior. The cluster analysis suggested four clusters as an optimal number. These clusters were profiled using demographic data.

Responses to the 24 consumer behavior variables varies significantly across the four clusters. However, our main interest was in the power of this segmentation model relative to other commonly used models in predicting consumer behavior. We, therefore, formed two additional sets of clusters: one based on age groups using ten-year age intervals, and another based on main cohorts: Generation X (born after 1964), Baby Boomers (born between 1946 and 1964), War Babies (born between 1940 and 1945), Depression Generation (born between 1930 and 1939) and GI Generation (born before 1930).

Following the approach used by previous researchers to compare segmentation models, regression analysis was used to compare the three types of segmentation schemes. Three separate regression models were tested for each consumer behavior using each set of the segmentation groups as dummy variables. R² values across the 24 consumer behaviors for each of the three segmentation model were compared to determine the model’s ability to account for variation in consumer behavior.

A general comparison of the three models suggested the superiority of event-based segmentation over cohort-based and age-based methods. Regression models combining event-based segments with age and cohort membership provided additional support for the event-based model. The inclusion of event-based segments in the age-based and cohort-based segments added significant explanatory power to the latter models.

In sum, the results of present research suggest the viability of the life-events segmentation basis, suggesting the need for additional research. Future research should include a large number and variety of consumer behaviors, longer lists of life events, and more detailed measures of timing of events and perhaps sequence of events.

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