Psycographic-oriented research of consumer lifestyles based on surveys for collecting the data is losing favour among marketers and researchers. Descriptors of consumer lifestyles are applied especially for segmentation by means of statistical clustering methods and other approaches (e.g., latent class modelling). Identifying lifestyle segments has been recognized as a strategic instrument for marketing planning because this kind of segmentation has been helpful and insightful in explaining variation in consumer behaviour where “dry” demographic descriptors cannot reach the deeper intricacies. But with the drop in response rates to surveys over the years, even on the Internet, and further problematic issues in consumer responses to survey questionnaires (by interview or self-administered), lifestyle research using psychographic measures is becoming less amenable, and that is regrettable.
The questionnaires required for building lifestyle segmentation models are typically long, using multi-item “batteries” of statements (e.g., response on a disagree-agree scale) and other types of questions. Initially (1970s) psychographics were represented mainly by Activities, Interests and Opinions (AIO). The measures cover a wide span of topics or aspects from home and work, shopping and leisure, to politics, religion and social affairs. But this approach was criticised for lacking a sound theoretical ground to direct the selection of aspects characterising lifestyles that are more important, relevant to and explanatory of consumer behaviour. Researchers have been seeking ever since the 1980s better-founded psychology-driven bases for lifestyle segmentation, particularly social relations among people and sets of values people hold to. The Values and Lifestyles (VALS) model released by the Stanford Research Institute (SRI) in 1992 incorporated motivation and additional areas of psychological traits (VALS is now licensed to Strategic Business Insights). The current version of the American model is based on that same eight-segment typography with some updated modifications necessary to keep with the times (e.g., the rise of advanced-digital technology) — the conceptual model is structured around two prime axes, (a) resources (economic, mental) and (b) motivation or orientation. Scale items corresponding to the AIOs continue to be used but they would be chosen to represent constructs in broader or better-specified contexts.
Yet the challenge holds even for the stronger-established models, how to choose the most essential aspects and obtain a sufficient set of question items consumers are likely to complete answering. Techniques are available for constructing a reduced set of items (e.g., a couple of dozens) for subsequent segmentation studies relying on a common base model, but a relatively large set (e.g., several dozens to a few hundreds of items) would still be needed for building the original model of lifestyle segments. It is a hard challenge considering in particular the functions and limitations of more popular modes of surveys nowadays, online and mobile.
Lifestyles reflect in general the patterns or ways in which people run their ordinary lives while uncovering something of the underlying motives or goals. However, ‘lifestyles’ have been given various meanings, and researchers follow different interpretations in constructing their questionnaires. The problem may lie in the difficulty to construct a coherent and consensual theory of ‘lifestyles’ that would conform to almost any area (i.e., product and service domain) where consumer behaviour is studied. This may well explain why lifestyle segmentation research is concentrated more frequently on answering marketing questions with respect to a particular type of product or service (e.g., banking, mobile telecom, fashion, food). It can help to select more effectively the aspects the model should focus on and thereby also reduce the length of the questionnaire. The following are some of the concepts lifestyle models may incorporate and emphasise:
- Values that are guiding and driving consumers (e.g., collectivism vs. individualism, modernism vs. traditionalism, liberalism vs. conservatism);
- In the age of Internet and social media consumers develop new customs of handling social relations in the virtual world versus the physical world;
- In view of the proliferation of digital, Internet and mobile communication technologies and products it is necessary to address differences in consumer orientation and propensity to adopt and use those products (e.g, ‘smart’ products of various sorts);
- How consumers balance differently between work and home or family and career is a prevailing issue at all times;
- Lifestyles may be approached through the allocation of time between duties and other activities — for example, how consumers allocate their leisure time between spending it with family, friends or alone (e.g., hobbies, sports, in front of a screen);
- Explore possible avenues for developing consumer relationships with brands as they integrate them into their everyday way of living (e.g., in reference to a seminal paper by Susan Fournier, 1998)(1);
- Taking account of aspects of decision-making processes as they may reflect overall on the styles of shopping and purchasing behaviour of consumers (e.g., need for cognition, tendency to process information analytically or holistically, the extent to which consumers search for information before their decision).
Two more issues deserve special attention:
- Lifestyle is often discussed adjacent with personality. On one hand, a personality trait induces a consistent form of response to some shared stimulating conditions in a variety of situations or occasions (e.g., responding logically or angrily in any situation that creates stress or conflict, offering help whenever seeing someone else in distress). Therefore, personality traits can contribute to the model by adding generalisation and stability to segment profiles. On the other hand, since lifestyle aspects describe particular situations and contexts whereas personality traits generalize across them, it is argued that these should not be mixed as clustering variables but may be applied in complementary modules of a segmentation model.
- Products that consumers own and use or services they utilize can illustrate figuratively their type of lifestyle. But including a specific product in the model framework may hamper the researcher’s ability to make later inferences and predictions on consumer behaviour for the same product or a similar one. Therefore, it is advisable to refer carefully to more general types of products distinctively for the purpose of implying or reflecting on a pattern of lifestyle (e.g., smartphones and technology-literacy). Likewise, particular brand names should be mentioned only for an important symbolic meaning (e.g., luxury fashion brands, luxury cars).
Alternative approaches pertain to portray lifestyles yet do not rely on information elicited from consumers wherein they describe themselves; information is collated mostly from secondary databases. Geodemographic models segment and profile neighbourhoods and their households (e.g., PRIZM by Claritas-Nielsen and MOSAIC by Experian). In addition to demographics they also include information on housing, products owned (e.g., home appliances), media used, as well as activities in which consumers may participate. However, marketers are expected, by insinuation, to infer the lifestyle of a household, based, for instance, on appliances or digital products in the house, on newspaper or magazine subscriptions, on clubs (e.g., sports), and on associations that members of the household belong to. Or consider another behavioural approach that is based on clustering and “basket” (associative) analyses of the sets of products purchased by consumers. These models were not originally developed to measure lifestyles. Their descriptors may vicariously indicate a lifestyle of a household (usually not of an individual). They lack any depth in describing and classifying how consumers are managing their lives nor enquiring why they live them that way.
The evolving difficulties in carrying-out surveys are undeniable. Recruiting consumers as respondents and keeping them interested throughout the questionnaire is becoming more effortful, demanding more financial and operational resources and greater ingenuity. Data from surveys may be complemented by data originated from internal and external databases available to marketing researchers to resolve at least part of the problem. A lifestyle questionnaire is usually extended beyond the items related to segmentation variables by further questions for model validation, and for studying how consumers’ attitudes and behaviour in a product domain of interest are linked with their lifestyles. Some of the information collected thus far through the survey from respondents may be obtained from databases, sometimes even more reliably than that based on respondents’ self-reports. One of the applications of geodemographic segmentation models more welcome in this regard is using information on segment membership as a sampling variable for a survey, whereof characteristics from the former model can also be combined with psychographic characteristics from the survey questionnaire in subsequent analyses. There are furthermore better opportunities now to integrate survey-based data with behavioural data from internal customer databases of companies (e.g., CRM) for constructing lifestyle segments of their customers.
Long lifestyle questionnaires are particularly subject to concerns about the risk of respondent drop-out and decreased quality of response data as respondents progress in the questionnaire. The research firm SSI (Survey Sampling International) presented recently in a webinar (February 2015 via Quirk’s) their findings and insights from a continued study on the effects of questionnaire length and fatigue on response quality (see a POV brief here). A main concern, according to the researchers, is that respondents, rather than dropping-out in the middle of an online questionnaire, actually continue but pay less attention to questions and devote less effort answering them, hence decreasing the quality of response data.
Interestingly, SSI finds that respondents who lose interest drop-out mostly by half-way of a questionnaire irrespective of its length, whether it should take ten minutes or thirty minutes to complete. For those who stay, problems may yet arise if fatigue kicks-in and the respondent goes on to answer questions anyway. As explained by SSI, many respondents like to answer online questionnaires; they get into the realm but they may not notice when they become tired or do not feel comfortable to leave before completing the mission, so they simply go on. They may become less accurate, succumb to automatic routines, and give shorter answers to open-end questions. A questionnaire may take forty minutes to answer but in the estimation of SSI’s researchers respondents are likely to become less attentive after twenty minutes. The researchers refer to both online and mobile modes of survey. They also show, for example, the effect of presenting a particular group of questions in different stages of the questionnaire.
SSI suggests in its presentation some techniques for mitigating those data-quality problems. Two of the suggestions are highlighted here: (1) Dividing the full questionnaire into a few modules (e.g., 2-3) so that respondents will be invited to answer each module in a separate session (e.g., a weekly module-session); (2) Insert break-ups in the questionnaire that let respondents loosen attention from the task and rest their minds for a few moments — an intermezzo may serve for a message of appreciation and encouragement to respondents or a short gaming activity.
A different approach, mentioned earlier, aims to facilitate the conduct of many more lifestyle-application studies by (a) building once a core segmentation model in a comprehensive study; (b) performing future application studies for particular products or services using a reduced set of question items for segmentation according to the original core model. This approach is indeed not new. It allows to lower the burden on the core modelling study from questions on product categories and release space for such questions in future studies dedicated to specific products and brands. One type of technique is to derive a fixed subset of questions from the original study that are statistically the best predictors of segment membership. However, a more sophisticated technique that implements tailored (adaptive) interviewing was developed back in the 1990s by the researchers Kamakura and Wedel (2).
The original model was built as a latent class model; the tailored “real-time” process selected items for each respondent given his or her previous responses. In a simulated test, the majority of respondents were “presented” with less than ten items; the average was 16 items (22% of the original calibration set).
Lifestyle segmentation studies are likely to require paying greater rewards to participants. But that may not be enough to keep them in the survey. Computer-based “gamification” tools and techniques (e.g., conditioning rewards on progress in the questionnaire, embedding animation on response scales) may help to some extent but they may also raise greater concerns for quality of responses (e.g., answering less seriously, rushing through to collect “prizes”).
The contemporary challenges of conducting lifestyle segmentation research are clear. Nonetheless so should be the advantages and benefits of applying information on consumer lifestyle patterns in marketing and retailing. Lifestyle segmentation is a strategic tool and effort should persist to resolve the methodological problems that surface, combining where necessary and possible psychographic measures with information from other sources.
Ron Ventura, Ph.D. (Marketing)
(1) Consumers and Their Brands: Developing Relationship Theory in Consumer Research; Susan Fournier, 1998; Journal of Consumer Research, 34 (March), pp. 343-373
(2) Lifestyle Segmentation With Tailored Interviewing; Wagner A. Kamakura and Michel Wedel, 1995; Journal of Marketing Research, 32 (Aug.), pp. 308-317.