Many marketers are ill positioned to take advantage of the most important consumer segment groups to rise over the next 20 years, and beyond – senior citizens. It is important that marketers and advertisers do not fail to connect with their older audiences. Not only are baby boomers holding a large proportion of economic wealth – 80% of all financial wealth in the UK and Canada, and over 50% of discretionary income in the USA – they are also major buyers of luxury products such as cars, alcohol, vacations and financial products.
Nonetheless, marketers continue to aim promotions at and cater to younger segments. Critics cite an eagerness to use mainly young characters in advertisements, and a tendency to portray old age as undesirable, as evidence of advertisers’ ageism. In general, age discrimination has been given a low priority, but this is changing as demographics demonstrate growing numbers of older people in the population who have reason to protect and promote their value in society. One reason suggested for the disinterest shown towards older people is the youthful profile of many of those working in advertising.
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The reluctance of the marketing industry to target the older population is in part due to outdated and stereotypical notions of age and ageing. The over-fifties today form a very different profile from the ‘elderly’ of the past; healthier diets, improved life expectancy and a widespread desire to feel younger for longer mean that they neither look nor feel old, and certainly do not want to be viewed as such by marketers and advertisers. A lack of empathy among many marketers and advertisers for the needs of older consumers has manifested itself in communications that are often inappropriate for this new generation of over-fifties.
Senior citizens should not be seen as one homogeneous mass. As with all types of consumer, there are many ways to segment senior citizens, one being based upon age. Using ‘age’ as a segment descriptor, senior citizens can be divided into four segments. There are the so-called older adults ranging from 55 to 65 years of age. The second market segment, the elderly, is made up of those aged 65 to 74. The aged, those from 75 to 84, and the very old, those 85 and over, constitute the other two segments.
A closer look at the older adult group reveals that they are interested in maintaining a youthful appearance and are major targets for exercise equipment, health programmes, diets, cosmetics, cosmetic surgery, sports clothing, designer wear, and a wide array of personal services that improve appearance. An increasing number of older adults opt for early retirement or move into new careers and part-time jobs. The elderly group comprises those who have been retired for some time. They tend to take a keen interest in health and nutrition and to be concerned with diet, salt intake, cholesterol, fried foods, and calories.
They often drink less alcohol than the younger population and are a good market for skin care products, prescriptions, vitamins and minerals, health and beauty aids, and medicines that ease pain and promote the performance of everyday activities. The aged group often has health and mobility problems and hence requires health care services and special care facilities. The very old need help in their day-to-day tasks. They find it difficult to get around and need regular medical and hospital care. Again, they represent a large market for health care facilities.
While the classification of the mature market into these four segments has been useful, another classification, and perhaps a better one for advertising purposes, is based on attitudes towards advertising. These segments could then be profiled in terms of psychographic variables. A major concern of advertisers targeting the aged consumer has been the way in which the older population utilises and evaluates information from advertising to make purchasing decisions. One study by Davis and French explored aged consumers’ use of advertising as a primary source of information in purchase decisions.
The respondents were clustered based on attitudes towards advertising. Psychographic profiles were developed for each of the derived segments. A database of annual lifestyle surveys was used to obtain a sample of 217 married female respondents aged 60 and over who were not employed outside the home. Respondents were asked to rate their degree of agreement with each of the 200 AIO (activities, interests, opinions) statements on the survey. Respondents were also asked to rate four attitudinal statements measuring information usage and beliefs about advertising, as well as the credibility of the source of advertising.
Identical information obtained from a previous study was used for replication purposes by Davis and French. The data on the four statements (shown in Table 1) measuring attitudes towards advertising were analysed used Ward’s method of clustering. Three clusters – Engaged, Autonomous, and Receptive consumers – were identified. Mean scores for each cluster are presented in Table 1. To test stability, replication of the cluster analysis was undertaken using the data obtained in the previous study. Ward’s method of clustering was used to analyse the data from the previous study.
Again, three clusters were obtained. Cluster means obtained by Davis and French on each of the clustering variables for the replication sample (previous study) are also shown in Table 1. To determine the psychographic differences among the three clusters, two additional steps were taken. First, one-way ANOVA was carried out to determine the discriminating variables. The three segments formed the grouping or the independent variable, and each psychographic statement served as a dependent variable. Forty-one of the original 200 psychographic statements were found to be statistically significant.
With the realisation that some of these significant variables were probably measuring the same characteristics, a principal components factor analysis was carried out, with four factors (accounting for 60. 3% of the variance) extracted in a varimax rotation. Factor scores were computed for each of the three segments by Davis and French, and Table 2 shows these scores, along with the variables that loaded highly on these factors and the variable means. This information can be used to provide psychographic profiles for each of the three segments identified in cluster analysis.