Sychographic Segmentation of Indian Urban Consumers

Journal of the Asia Pacific EconomyVol. 13, No. 2, May 2008, 204–226

Psychographic segmentation of Indian urban consumers

Rohit Vishal Kumara,∗ and Amitava Sarkarb

aDepartment of Marketing & Finance, Xavier Institute of Social Service, Ranchi,Jharkhand, India; bWest Bengal University of Technology, Salt Lake City, Kolkata,

West Bengal, India

Since 1991, India is emerging as a key destination for marketers from acrossthe globe. However, little work of relevance has been undertaken to understandthe Indian Consumers. It is believed that the Indian metropolitan consumersare fairly homogeneous in nature. In this paper, we report the results of asurvey conducted amongst 560 respondents in the five metropolises. The ob-jective was to segment the metropolitan consumers on behavioral aspects andto understand their consumption pattern. The study, designed on the basis ofVALS, uses cluster analysis to segment the Indian metropolitan consumersinto six behavioral categories, namely Well Settled, Strugglers, Enjoyers, Con-servatives, Self Concerned and Realist. The segments have been profiled interms of their product ownership, Activities and Interests, Financial Invest-ment avenues and Media habits. Implications for marketing and governmentpolicy have been discussed.

Keywords: segmentation; psychographics; cluster analysis; lifestyle;marketing; India

JEL Classifications: R11, M31, C49, O53

Introduction

With the introduction of the New Economic Policy in 1991 and the adoptionof liberalization, privatization and globalization as the key drivers of the IndianEconomic Policy, a slew of economic reforms were undertaken by the Governmentof India. These reforms have far-reaching implications on how the Indian economyevolved over the next 15 years. A major thrust was on opening up of the Indianmarkets to competition from abroad. The offshoot was that the Indian consumer,who was until then, protected from the developments of the rest of the world,now suddenly found himself being courted by companies from around the globe.The steady growth of consumer finance options, lowering of interest rates andcurtailment of inflation at reasonable levels over the past decade and a half has

∗Corresponding author. Email: [email protected]

ISSN: 1354-7860 print / 1469-9648 onlineC© 2008 Taylor & Francis

DOI: 10.1080/13547860801923590http://www.tandf.co.uk/journals

Journal of the Asia Pacific Economy 205

resulted in general increase of income levels of the middle and the rich classesin India. This can be amply understood by the fact that India had about 20,000crorepati1 households in 2004, as opposed to only 5000 crorepati householdsin 1995–96 (Agnihotri 2004). The rapid increase in income levels in the handson ordinary Indians meant that over a short period of time, the Indian economychanged from a socialist oriented economy to a consumption-led economy. Thechanges can be aptly understood in the words of Kishore Biyani, the CEO ofPantaloons Retail India Limited (Biyani and Baishya 2007).

The changing demographic profiles, increasing income levels, urbanization,technology, globalization and a free flow of ideas from within and outside thecountry, has brought about a dramatic shift in consumer taste and preferences.Consumer segments [in India] are maturing faster than ever.

The rapid change in the Indian consumer’s attitudes and profiles, made itdifficult for organizations to cater to the consumer needs. Indian companies –more used to licensing policies and under-production during the pre-liberalizationperiod – faced the uphill task of catering to the changing consumer taste andpreferences. This lead to a rash of research to understand the Indian consumer.Unfortunately most of the work undertaken in the Indian context remained in thedomain of commercial marketing research agencies and was focused on specificorganizational and brand requirements. For example, the now defunct magazine –Advertising & Marketing – carried out various researches on profiling consumerbehavior in the Indian context (Jacob 1997; Prabhakaran 1996; Shirali and Singh,1997). MTV, in collaboration with IMRB International, conducted a study on18–34 years old in India and segmented the Indian youth into six segments –Cultural Misfits, Style Bhai’s, Middle Class Majnu’s, Main Bhi NRI, Rich Bratsand Nerdy Nandu’s (Businessworld Editorial Team 2005). Similarly, the Outlooknews magazine, in collaboration with NFO Research, carried out a research on thesexual orientation of Indian women and classified the modern Indian women intofive segments (Deb 2003). Pantaloons Retail classified India into three segments –India I, India II and India III (Biyani and Baishya 2007) for its own internal usageetc.

All such researches, mentioned above, suffered from the limitation that theywere conducted to meet the marketing requirement(s) of a particular organization.Academically, India lacked a major study which profiled the Indian consumers.Some work of note had taken place in the Indian context; but except for thestudy of National Council of Applied and Economic Research (NCAER), theother studies have been more region-specific in nature. As such, a growing needwas felt to conduct an academic study of the Indian consumers and to provide aprofile of the Indian consumers that could be used irrespective of the brand or theorganization concerned. The current study attempts to fill in the gap amongst theurban consumers of India.

In this paper we propose to study the metropolitan urban consumers of Indiawith the following objectives: (1) to segment the metropolitan urban consumersinto distinct behavioral groups, and (2) to understand the lifestyle pattern of the

206 R.V. Kumar and A. Sarkar

identified behavioral groups and to understand the implications for marketing ofproduct and services.

This paper is organized as follows: in the next section we review the existingliterature. We do not propose to give an exhaustive review of literature but providea brief overview of international studies and concentrate in some detail on theIndian literature. In the third section we provide an outline of the methodologyfollowed and in the fourth section we profile the identified segments. In the fifthsection we take a look at the consumption pattern of the various segments in termsof product ownership, computer and internet usage, avenues of investment andmedia habits. In the sixth section we provide our observations and discuss somemarketing implications for companies operating in India.

Review of literature

In order to understand the consumers and their preferences, various demographicand lifestyle studies have been carried out across the world. For example, signif-icant studies have been carried out try and understand the correlation betweensocial class income and buying behavior (Slocum and Mathews 1970) and so-cial class and lifestyle as a predictor of shopping behavior (Rich and Jain 1968).Studies have also been carried out to understand the importance of Life Statusand Consumer Preference (Andreason 1984) and the importance of consumerbehavior and cultural values (Henry 1976). In addition, there are studies relat-ing to the influences of reference groups on consumer brand purchase (Stafford1966) and on the influence of the reference group on product and brand purchase(Beardon and Etzel 1982). Studies have also been conducted to understand theimpact of role of sex on purchase decisions (Qualls 1987); on working wives andexpenditure on service (Bellanti and Foster 1984) and the use of advertising formaking purchase decisions (Eighmey 1997). Similarly, studies have been con-ducted to understand consumer profiles for various products. Notable amongstthem are studies on credit card usage and lifestyle (Plummer, 1971; Slocum andMathews, 1969), VCR ownership and lifestyle (Potter et al. 1988), drinking anddriving amongst young males (Lastovick et al. 1987), heterogeneity between thesenior citizens market (Day et al. 1987) etc. Research has also been carried outto understand the profile the women in the USA, UK and France (Doughlas andUrban 1997). Lifestyle research has also been extended to the new medium of theInternet (Eighmey 1997).

In the Indian context, the studies have been comparatively fewer in number.Deepa Kapoor studied the interrelationship between religiosity, caste prejudices,and conservatism and life styles among the four major faith groups: Hinduism,Islam, Sikh and Christianity. The research was carried out under the assumptionthat the individual’s stable and persistent beliefs and ideologies – social, economic,political and region – determined the lifestyle of the individual. It concluded thatreligiosity had a lasting impact on all the other aspects and was a major influence onmaintaining the caste prejudices and conservative outlook; which in turn influenced


the lifestyle pattern of an Indian consumer. The study was carried out in the cityof New Delhi (Kapoor 1985).

One of the most comprehensive studies conducted in India to date has been thestudy of National Council of Applied Economic Research (NCAER) conductedin 1993–94. The study had a sample size of 2.81 lakh (1 lakh = 1 × 105) house-holds (65% urban and 35% rural) and covered 410 districts across the length andbreath of the country. It mapped the ownership and purchasing patterns of 281,768consumer durables and 18,730 consumer non-durables. The study classified theconsumption behavior by using real disposable income corrected for regional im-balances. Factor and Cluster analysis was used to segregate the Indian populationinto five major groups based on the real disposable income and the ownershipof durables and consumption of non-durables: Very Rich (6 million), ConsumingClasses (150 million), Climbers (275 million), Aspirants (275 million) and Desti-tute (210 million). According to the study, Indian population would blossom intoan inverted pyramid with the Very Rich and the Consuming Classes driving theIndian market (Rao and Natarajan 1996). The population projections by the studymatch the Government of India’s population estimates by age classes.

Seema Kapur (1995) tried to understand the linkages between the ownershipof two-wheelers and the behavior of their owners. Adite Chatterjee (1995) con-ducted a study to understand the brand association of Denim brands with lifestyleattributes. The study was qualitative in nature and used only focus groups to arriveat its findings. Another study, by the same author, was conducted to provide an un-derstanding of changes in personality traits, family relationships, personal goals,attitude toward advertising and favored products among Indian consumers agedbetween 15–45 years. The study (again based solely on focus groups) concludedthat Indian consumers were different and could not be classified into any knownnorms (Chatterjee 1996).

Shirali and Singh (1997) carried out a study on status symbols for Indian malesand tried to identify similar lifestyle segments based on the classification of thestatus symbol. The study surveyed 623 respondents in the top four metropolitancenters in the country – all with at least one car at their disposal. The studyfound that there was concordance amongst the males in the top four metros asregards what they considered as status symbols and classified Indian Males intofour different segments – Yuppies, Already Arrived, Getting There and Won’t Be.

Some other studies have also been carried out in the Indian context. Sharmaand Chaubey (2004) carried out a study with the aim of assessing the consumers’awareness and their attitude towards different mobile service providers in the cityof Lucknow. Another study attempted to segment the consumers in Kolkata marketusing their brand preferences for toothpaste brands. The study concluded that theconsumers in the Kolkata market could be segmented into five groups based ontheir toothpaste brand choices (Kumar and Mitra 2004). Similarly, studies havealso been taken up to understand the effect of psychographics on the purchasefrequency amongst college goers in Kolkata (Roy and Goswami 2007). Studieshave also been carried out to study various aspects of disease and substance abuse


(Medhi et al. 2006) and for identifying means of better governance in rural andurban areas (Bhagat 2005). Biases present in the Indian society have also beenstudied (Sudha and Rajan 1999).

Thus, it can be clearly seen that there is a significant lack of work of a pan-Indianature. This paper, based on the study of urban metropolitan consumers in the topfive metropolis of India, is an attempt to fill the lacunae to some extent.

Outline of the study

The present study is based on the premise that the metropolitan urban consumersin India can to treated as a fairly homogeneous segment. This claim is not withoutfoundation as researches conducted in the USA have shown that there does exist ahigh degree of integration between the urban youths of various countries (Hawkinset al. 2002; Tully 1994). In the Indian context, we are not aware of any work that hasbeen undertaken to either prove or disprove the claim; but it can be safely assumedthat the growth of communication technologies, travel and transport and openingup of new job opportunities in various services sectors has led to an interminglingof the Indian urban population leading to more homogeneous views and opinions.This claim is also augmented by the fact that the work was conducted on SEC Aand B households. It is widely believed in marketing circles that SEC A and Bhouseholds have a higher propensity to consume high value items (McDonald andDunbar 2004) and thereby would be more aware and integrated with fashion, fadsand trends in domestic and international markets. It must be mentioned here thatthe SEC A and B segments have been treated as a single homogeneous mass, andsuch city level analysis has not been conducted on the data.

In traditional economic literature, consumption is looked upon in terms ofproduct and price. A product is conceived of as a bundle of attributes that yield aparticular benefit on consumption and is purchasable at a given price. Given thatall the consumers in a particular market face the same product, it is but obvious thateconomists tend to take price as the differentiating variable. However, marketingpractitioners generally believe that price is one of the many factors that influencesconsumption. For example, marketers believe that even though a blue collar and awhite collar worker may earn the same level of income, differences of social classwill compel them to spend the income in different ways (Schiffman and Kanuk1997). The identification of the ‘attitudinally affluent’ consumers – consumers whodo not have the income needed to be considered affluent yet desire to have the best– proves the inability of income or price to be a causative factor. These consumersbuy less, but buy better quality and they achieve this by assigning priorities to theobject of desire and gradually working their way towards possession of the desiredproduct (Rodkin 1990).

In marketing literature, various methods have been proposed to study consumersegments and behavior. However, the most unique of these is psychographics inwhich the notion of lifestyle is embedded. The term stands for psychological traitsthat a marketer ties to consumption. Psychographics is measured by questionnaires


resembling personality interviews along with a wide range of items measuringattitudes and issues related to lifestyle preferences. It is generally believed thatpsychographics tends to give a greater insight into why products and services arepurchased (Wells 1975). As the objective of our study was to segment the urbanmetropolitan population into distinct behavioral groups and to understand theirlifestyle’s for marketing purposes; we decided to use the psychographic approachto segmentation. Within the psychographic domain, Values and Lifestyles (VALS)(Mitchell 1984) and Attitudes, Interest and Opinions (AIO) (Wells and Tigert1971) are the two predominant approaches. Our study is based on the VALSmethodology in principle, to study the urban metropolitan consumers.

The methodology

The study focused on the metropolitan population of India residing in the citiesof Mumbai, Kolkata, Delhi, Chennai and Bangalore, thereby covering 19.10%of the total urban population of India and 50.51% of the population residing inmetropolitan cities as per the census of India 2001. The target respondents for thestudy were individuals belonging to SEC A and B households, between 21 to 70years of age.

A total of 560 interviews were conducted in the five metropolises. To maintainrandomness, the sampling frame was determined using a mix of pin code area andthe Electoral Registers. At the first level, each metropolis was defined in termsof the pin code area and subsequently the desired numbers of pin code areaswere selected randomly. For each of the selected areas, the electoral registers wereprocured and used as a sampling frame. In case the pin code area was bifurcatedby more than one electoral zone, the electoral zone with the largest coverage wasused. Not more than ten completed interviews were conducted in a single pin codearea. The interviews were conducted using the in-home interviewing technique(Robertson and Conway 2003) using a structured questionnaire.

The questionnaire contained 60 statements on attitudes, opinions and interestson various aspects of Indian economy and culture. In the first iteration, a list ofabout 100 statements was drawn up by the authors by consulting the statementsof the VALS framework (Mitchell 1984) and other sources, such as newspapersand magazines. This list of 100 statements was given to senior professors ofmarketing at the University of Calcutta and other management institutes in andaround Calcutta. They were asked to identify independently all those statementsthey thought would be of relevance to the urban metropolitan consumer in thecurrent Indian context. From this list, 65 statements were chosen. Care was takento include all those statements on which the majority of experts were in agreement.In the second iteration, a small pre-test questionnaire was developed and wasadministered to 35 people in Calcutta chosen at random. Out of the 65 statementsit was found that three statements were giving completely skewed data and, as such,were dropped from the study. Of the remaining 62 statements a few statementswere found to be difficult to understand because of wording-related issues. The


wordings of such statements were simplified and a total of 60 statements retainedfor final input.

The breakup of the statements and the broad grouping is as follows: IndianBusiness (seven statements), Culture (five statements), Education (eight state-ments), Politics (five statements), Products (five statements), Social Issues (sixstatements), Self Belief (12 statements), Women’s Dignity (five statements) andFuture Prospect (seven statements). Each statement was rated on a six-point scaleDisagree Strongly (1), Disagree Mostly (2), Disagree Somewhat (3), Agree Some-what (4), Agree Mostly (5) and Agree Strongly (6). A DK/CS point was alsoprovided to record non-response or refusals. However, the respondents were notmade aware of the DK/CS point.

Segmenting the dataset

In a bid to segment the urban consumers we decided to use cluster analysis on thestatements. There has been much discussion in the literature of cluster analysisregarding choice of variables in cluster analysis – however three broad areasof practice can be outlined. Some researchers prefer to use the entire battery ofindividual items. Others tend to transform the data to principal components orother factor scores. Still others prefer to choose a subset of the original dataset andrun cluster analysis on the subset of individual items. However, it has been shownthat using all the variables in cluster analysis tends to provide sharper between-cluster differences, internal reproducibility and external discrimination (Fiedlerand McDonald 1993). Thus, it was decided to use the all-variable approach forcluster analysis. As such, 52 statements – which had less than 10% DK/CS – wereused in the final k-means cluster analysis.

However, one of the biggest drawbacks of cluster analysis still remained – thatis, determining the actual number of clusters (Dunham 2005). The problem ofdeciding on the number of clusters has been of great significance in the literatureof cluster analysis. Intuitively, the problem of deciding on the number of clusterscan be looked upon as a problem of maximizing the dispersion of cluster centers(between cluster distances) and minimizing the dispersion within cluster members(within cluster distances). The earliest work can be attributed to Ball and Hall(1965) and continues even today. Various rules – known as the ‘cluster index’ –have been proposed by various authors to decide on the final number of clusters(Ball and Hall 1965; Calinski and Harabasz 1974; Friedman and Rubin 1967;Fukunaga and Koontz 1970; Ratkowsky and Lance 1978; Scott and Symons 1971;Wedel and Kamakura 1998) .

Studies have been carried out to measure the ability of the cluster indexes topredict accurately the number of clusters. For example, Milligan (1981) carriedout a Monte Carlo simulation of 30 cluster indexes and concluded that ‘Calinski–Harabasz’ index to have the best predictive power. Subsequently Weingessel et al.(1999) carried out an exhaustive review of 14 cluster indexes on datasets knownto have a given number of longitudinal and elliptical clusters. Following the study


of Weingessel et al. (1999) we decided to use the following five cluster indexes,namely Calinski and Harabasz (1974), Ratkowsky and Lance (1978), Scott andSymons (1971), TraceW (Edwards and Cavalli-Sforza 1965) and the LikelihoodIndex (Wedel and Kamakura 1998) to identify the number of clusters in our dataset.

The cluster indexes were computed using the “R Software for Statistical Com-puting” (R Development Core Team, 2005) using the cclust package (Dimitriadou2005). The likelihood ratio was computed using WEKA (Witten and Frank 2005).

A detailed analysis of the cluster indexes suggested that there may be fiveto seven clusters in the dataset. However, that still left us with a wide variety ofchoices. To decide on the final number of segments the data were analyzed usingthe outputs of the cluster analysis on 5, 6, and 7 segments respectively. The analysissuggested that the six-segment solution provided the best overall segmentation interms of identifiability, substantiality, accessibility, stability, responsiveness andactionability (Vriens 2001). As such, the six-segment solution was selected forfurther understanding the urban metropolitan consumers.

The urban segments

After deciding on the number of segments, the next task was to study the segmentsand provide a descriptive name to these segments based on the manifest demo-graphics and psychographic characteristics. Table 1 provides the demographicprofile of the segments and Table 2 provides the mean scores on the overall state-ment groups.

In order to identify the significant means, Tukey’s Honest Significance Dif-ference Test at 5% level of significance was computed for Age, number of jobchanges and family size. It is the preferred choice of post-hoc comparison becauseit has greater power than other similar tests and is readily available for computation

Table 1. Demographic profiles of the segments.

Well Settled Strugglers Enjoyers Conservative Self-Concerned Realist(Segment 1) (Segment 2) (Segment 3) (Segment 4) (Segment 5) (Segment 6)

1. Average Age (years) 41.32 37.10 37.57 44.60 33.31 35.962. Family Size (nos) 3.14 3.10 3.22 3.74 1.84 2.713. MHI (Rs. pm) 16,859 14,289 23,587 14,550 21,977 19,7694. Job Changes (Nos) 0.99 1.79 1.31 2.61 3.39 3.005. Marital Status (%age)

Married 72.22 53.17 61.48 70.00 28.89 41.82Bachelor 19.75 36.15 35.25 20.00 66.67 36.36Widower 6.17 7.94 1.69 10.00 4.44 9.09Divorced 1.95 2.38 1.14 — 4.44 12.73

6. Occupation (%age)Private Sector 21.62 25.00 39.13 26.32 42.86 35.90Public Sector 20.27 9.62 23.19 10.53 10.71 10.26Self Employed 56.76 63.46 34.74 57.89 42.86 53.85

7. Working for (years) 15.84 10.36 12.83 19.82 8.18 12.628. Percent of Population 28.93 22.50 21.79 8.93 8.03 9.82


Table 2. Mean scores on psychographic statements (overall).

Well Settled Strugglers Enjoyers Conservatives Self-Concerned Realist(Segment 1) (Segment 2) (Segment 3) (Segment 4) (Segment 5) (Segment 6)

Women Issues 5.43 4.81 5.12 5.48 4.25 5.19Social Issues 5.44 4.91 5.39 5.54 4.04 5.25Products 4.88 4.47 4.80 4.98 4.07 5.09Future 4.96 4.47 4.67 4.43 4.04 4.31Education 4.83 4.40 3.95 5.21 3.75 3.51Culture 4.86 4.36 4.51 4.66 3.69 3.39Business 4.75 4.44 4.45 4.82 3.77 4.11Themselves 4.73 4.46 4.34 4.39 3.59 3.65

(Keppel and Wickens 2004). Table 3 shows the computed probability values ofthe significance difference test along with the means, which are different. As canbe seen from the table, segments seemed well separated in terms of Age, numberof job changes and family size.

A review of the demographic profile of the segments reveals that Segment 4 isthe oldest segment in terms of age and has one of the lowest monthly householdincomes. It also has the largest family size and has been in the workforce forabout 20 years. Segment 5, on the other hand, is the youngest in terms of age,smallest in terms of family size and has the one of the highest monthly household

Table 3. Tukey’s HSD probabilities.

Well Settled Strugglers Enjoyers Conservatives Bachelors RealistMean (Segment 1) (Segment 2) (Segment 3) (Segment 4) (Segment 5) (Segment 6)

AgeSegment 1 41.320 —Segment 2 37.100 0.060 —Segment 3 37.560 0.137 1.000 —Segment 4 44.600 0.605 0.006* 0.013* —Segment 5 33.310 0.003* 0.523 0.393 0.000* —Segment 6 35.960 0.076 0.994 0.972 0.007* 0.906 —

Job ChangeSegment 1 0.990 —Segment 2 1.790 0.226 —Segment 3 1.310 0.927 0.790 —Segment 4 2.610 0.024* 0.658 0.138 —Segment 5 3.390 0.000* 0.008* 0.001* 0.786 —Segment 6 3.000 0.000* 0.047* 0.000* 0.984 0.968 —

Family SizeSegment 1 3.140 —Segment 2 3.100 1.000 —Segment 3 3.220 0.993 0.977 —Segment 4 3.740 0.036* 0.030* 0.139* —Segment 5 1.840 0.000* 0.000* 0.000* 0.000* —Segment 6 2.710 0.253 0.381 0.124 0.000* 0.009* —

The figures marked with * are significant at 5% level of significance.


incomes. The number of job changes is high – with roughly four job changes inabout 8 years of work experience. This segment is also dominated by bachelors,with about 67% of the people in the segment having not married to date. Segment6, which is the next youngest segment, has a moderate family size and a fairlylarge monthly household income. It has been in the workforce for about 12 yearsand has had about three job changes. This is the only segment that has a sizabledivorced population.

Segment 1 is a fairly old segment with a well to do monthly household income.The characteristics of the members of Segment 1 are that they have the least numberof job changes and the highest number of married people. They also constitute thelargest segment of the population. In this segment, it is also seen that about onein every five people are in the government sector. Segment 3 has fairly old peoplewith the largest monthly household income. It should be noted that Segment 5and Segment 3 have a sizable percentage of people employed in the private sector.Segment 2 is the segment with the lowest monthly household income and themajority of its members are self-employed.

Based on the demographic profiles and the analysis of their responses tothe psychographic statements, the segments were classified under the followingnomenclature.

Segment 1

This segment has been named the ‘Well Settled’ segment. This segment couldbe thought of as comprising people who have established themselves in theirrespective fields. They are slightly older and have been in employment for thepast decade and a half. They are not keen to change jobs, they normally have onekid and have a decent monthly household income. They have moderate to strongopinions on various issues, are happy with the outcome of their lives, and they areslightly conventional in nature but are open to various other influences.

Segment 2

This segment has been named the ‘Strugglers’ segment. This segment could bethought of as comprising people who could not quite make it in life and are nowstruggling to make ends meet. With an average family size of three and the lowestincome per month, this segment is mostly self-employed and has been workingfor about a decade. They are not willing to speak up on various issues and tendto play safe. Slightly conservative in nature, not too confident, nor willing to takerisks, they tend to spend more than they would like to save and often feel left outof the things going around them.

Segment 3

This segment has been named the ‘Enjoyers’ segment. This segment could bethought of as the apex segment in terms of urban consumers. They have the


highest monthly household income and are employed mostly in the private sector.They have a liberal attitude and have strong to moderate opinions on various issues.They are happy with the outcome of life and are willing to take risks and want tolive life to the fullest. They are satisfied with themselves and try to enrich theirlives. They believe in consumer rights and protection of the environment and arestrong believers in modern technology.

Segment 4

This segment has been named the ‘Conservative’ segment. This segment couldbe looked upon as people who have given in to the fates of life. The oldest ofthe lot, they earn the least in terms of income. The family size is the largest.They are mostly self-employed or engaged in the private sector. Conservative innature, they do not prefer to take any kind of risk in terms of work and life.Financially, they like to save money. Somewhat confident, they don’t see a brightfuture for themselves. They believe in government participation in the economy.

Segment 5

This segment has been named as the ‘Self Concerned’ segment. This segment isthe youngest segment identified by the study. Dominated by bachelors, they haveamong the highest incomes, and the smallest families to support. This segmentis characterized by the highest number of job changes – approximately 3.4 jobchanges – in the short employment career. This segment is characterized by astrange reluctance to voice opinions. They showed a marked indifference to thevarious issues.

Segment 6

This segment has been named as the ‘Realist’ segment. This segment had a sizablepresence of divorced people. It has the second highest number of job changes,and earns a sizeable income This segment is mostly employed in the private sectoror is self-employed. This segment has liberal views. Financially, it likes to savemoney. Social Status is an important part of this segment. These people believein conservation of natural resources and environment friendly policies. Responseson various statements were more on the practical side; hence the nomenclature.

In subsequent sections, we shall use the segment nomenclature to identify thesegments.

Patterns of consumption

In this section, we undertake to look at the impact on consumption of products andservices by these segments in a bid to better profile these segments. This sectionreports the differences between the various segments on the following aspects:


Ownership of Products, Activities and Interests, Financial Products and Servicesand Media Consumption habits.

Ownership of consumer durables

In terms of consumer durables, an interesting picture emerged. Table 4 shows theownership pattern of major household consumer durable goods.

Color Television, Fridge, Gas Stove and Water Filter seemed to be the consumerdurables that were common across households in the segments. In the ‘Conserva-tive’ segment, there seems to be an absence of any communicating device, such asthe Mobile Phone and the Telephone. The decline of the telephone as a preferredmedium of communication can be seen from the table as, in the ‘Self Concerned’

Table 4. Ownership of consumer durables.

Well Settled (Segment 1) Strugglers (Segment 2) Enjoyers (Segment 3)

Color TV (93) Color TV (91) Color TV (89)Music System (82) Gas Stove (76) Gas Stove (87)Telephones (71) Fridge (70) Fridge (84)Fridge (69) Music System (69) Food Processor (79)Food Processor (63) Water Filter (53) Telephone (73)Gas Stove (62) Washing Machine (36) Mobile Phone (73)Mobile Phone (59) Telephone (34) Water Filter (71)Washing Machine (51) Mobile Phone (33) Music System (63)Microwave (37) DVD / VCD (27) Washing Machine (52)Water Filter (31) Food Processor (23) DVD / VCD (45)Air Conditioner (20) — Computer (40)— — Microwave (36)— — Walkman (35)— — Geyser (34)— — Air Conditioner (34)

Conservative (Segment 4) Self Concerned (Segment 5) Realist (Segment 6)

Gas Stove (98) Mobile Phones (82) Music System (81)Color TV (94) Music System (80) Color TV (78)Fridge (94) Color TV (77) Gas Stove (78)Water Filter (46) Gas Stove (72) Fridge (71)DVD / VCD (40) Fridge (64) Mobile Phone (65)Food Processor (38) Food Processor (50) Water Filter (64)Washing Machine (36) Washing Machine (45) Food Processor (58)Music System (30) Water Filter (41) DVD / VCD (40)Microwave (26) DVD / VCD (34) Telephone (31)VCR / VCP (20) Microwave (34) Microwave (29)— Telephone (32) Air Conditioner (27)— Air Conditioner (35) Computer (27)— Digi-cam (25) —

(Figures in parenthesis represent percent of ownership)


and the ‘Realist’ segments roughly 30% had access to telephones, and the use ofmobile phones was dominant in these two segments. Music Systems fared fairlyhigh on the durables list of all segments except the ‘Conservatives’. The presenceof Air-Conditioners, Microwaves, DVD Players, Washing Machines etc featuredprominently in the hands of the well-to-do segments. Their presence was also seenin the ‘Conservative’ and the ‘Strugglers’ segments, but in fairly low numbers

The ‘Enjoyers’ segment had the maximum durable ownership. It also was thelargest owner of exercising equipments, answering machines, electronic cookingranges etc. The ‘Self-Concerned’ segment showed a preference for ownership forDVD players, digital cameras, camcorders, microwave ovens, computers etc. The‘Realist’ segment showed a similar ownership pattern. The ‘Conservative’ and the‘Struggler’ segments seemed to have much fewer consumer durables.

Computer and internet usage

Another interesting aspect was the usage of computers as reported by the study(Table 5). Seventy-nine percent of the ‘Enjoyers’ said that they used computersregularly, followed by the ‘Realist’ (46%), ‘Self-Concerned’ (29%) and the ‘Well-Settled’ (28%) segments. The hours per week spent on computers and the internetwere again quite good. The least amount of time spent on computers was reportedby the ‘Conservative’ segment (7.9 hours/week) and the highest was reported bythe ‘Realist’ segment (43.9 hours/week). Spending time on the internet is seento be a fast emerging activity. The ‘Strugglers’ segment spent the least amountof time on the net (4.4 hours/week), while the highest time was reported by the‘Self-Concerned’ segment. Email, chatting, computer games and web browsingemerged as the most conducted activities on the internet across the segments.

The figures regarding regularly use of computers are an eye opener. It hasbeen reported that roughly 2.9% of all Indians regularly use computers as opposedto 35.5% in Europe and 68.8% in America. It can be seen that some of thesegments rival European countries in terms of usage; thereby breaking a myth thatIndian consumers are not adept at computer usage. The growth of computer andinternet usage amongst the urban segments can well be attributed to the growth

Table 5. Usage of computer and internet amongst segments.

Regularly Using Computer Usage Internet UsageSegment Computer (%age) (hours/week) (hours/week)

1. Well Settled 21.60 18.50 9.202. Strugglers 16.80 8.10 4.403. Enjoyers 44.30 17.00 10.004.Conservative 14.00 7.90 5.805. Self Concerned 22.20 33.00 20.506. Realist 31.50 43.90 16.10


of cyber-cafe’s, where the charge for using a computer or the internet is low –roughly Rs. 10 per hour – and the facilities are available throughout the majorpart of the day (Haseloff 2005).

Activities and interests

Table 6 presents the top 10 activities undertaken by the different segments.The Indian urban consumers showed a marked disinterest in taking part in any

kind of physical activity; especially in participation in sports and games. Walkingwas the only physical activity that was common across the various segments.Except for the ‘Self-Concerned’, ‘Enjoyers’ and ‘Realist’ segments, waking as anactivity does not figure prominently in other segments. Other sports that seemed tofigure in the segments were playing cricket, badminton, and football. In the Indiancontext, it must be kept in mind that cricket, badminton and football are commonactivities and can also be played in a disorganized and ad-hoc fashion. As such, itis highly unlikely that these activities contribute significantly to the health of thesegments. Playing basketball and going to gym for a workout were some of the newactivities that were seen in the ‘Realist’ and the ‘Self Concerned’ segments. Other

Table 6. Games and other activities undertaken by the segments.


Listening to Music (69) Listening to Music (79) Walking (67)Walking (43) Eating Out (54) Listening to Music (59)Gardening (32) Walking (37) Eating Out (56)Cooking Activity (29) Cooking Activity (34) Computer Games (40)Playing Football (24) Playing Cards (26) Visiting Amusement Parks (38)Eating Out (23) Visiting Stores (20) Going to Theatre (31)Playing Cricket (19) Badminton (20) Visiting Stores (29)Visiting Stores (16) Gardening (16) Cooking Activity (25)Going to Theatre (15) Swimming (13) Practicing Yoga / Reiki (22)Visiting Amusement

Parks (14)Playing Chess (13) Playing Cards (21)


Eating Out (98) Walking (93) Listening to Music (86)Listening to Music (86) Eating Out (93) Walking (73)Walking (45) Listening to Music (73) Eating Out (59)Cooking Activity (20 Cooking Activity (45) Cooking Activities (41)Playing Cricket (20) Visiting Shopping Malls (30) Playing Board Games (30)Gardening (18) Backpacking / Trekking (23) Visiting Shopping Malls (29)Playing Cards (16) Computer Games (17) Swimming (23)Visiting Stores (12) Practicing Yoga / Reiki (16) Computer Games (20)Swimming (12) Giving Dinner Parties (11) Going to Gym (14)Playing Badminton (10) Going to Gym (11) Playing Basketball (14)

(Figures in parenthesis represent percent of ownership).


common activities were mostly indoor activities, such as playing cards, cooking,playing computer games etc – which are mostly sedentary in nature. Gardeningwas an important activity in the ‘Well Settled’, ‘Strugglers’ and the ‘Conservative’segments.

In terms of Hobbies and Interests, it was seen that listening to music, shoppingand baking or cooking were universal across segments. Visiting the theatre wasan option in the ‘Well Settled’ and the ‘Enjoyers’ segments. Going to the gym,practicing reiki or yoga were some of the newer activities which were seen in the‘Self Concerned’ and the ‘Realist’ segments. Visiting an amusement park was acommon activity amongst the ‘Strugglers’, ‘Enjoyers’ and the ‘Realist’ segments.

The analysis of the activities and hobbies showed that the segments havedistinct and diverse activities, which helps in demarcating the segments in a bettermanner.

Avenues of investment

In the aspect of investing, the Indian urban consumer seems to be a risk averseperson (Table 7). The traditional mode of savings, such as Fixed Deposits orTerm Deposits with banks, National Savings Certificates (NSC), Kisan VikasPatra (KVP), Post Office Savings Schemes and Jewelry were some of the mostpreferred avenues of investments. It should be noted that all the investment schemesmentioned above are schemes launched by the Government of India to encouragesavings by providing a guaranteed rate of return; this rate is much lower thatwhat an investor will get from investing into various market-based funds or stocksand shares. Stocks and shares are, however, mainly seen in the ‘Realist’, ‘Self

Table 7. Investment avenues amongst the segments.


Fixed Deposit (78) Fixed Deposit (69) Fixed Deposit (89)Jewelry (18) Jewelry (16) Jewelry (42)NSC KVP (15) NSC KVP (14) Post Office Savings (31)Real Estate (10) Post Office Savings (13) NSC KVP (26)Post Office Savings (9) Stocks & Shares (6) Public PF (15)— — Stocks & Shares (15)


Fixed Deposit (68) Fixed Deposit (67) Fixed Deposit (98)NSC KVP (24) NSC KVP (44) NSC KVP (56)Stocks & Shares (16) Jewelry (24) Jewelry (50)Public PF (12) Stocks & Shares (18) Stocks & Shares (40)Post Office Savings (10) — Post Office Savings (23)Infrastructure Bonds (10) — —

(Figures in parenthesis represent percent of use).


Table 8. Insurance coverage amongst the segments.

Insurance Well Settled Strugglers Enjoyers Conservative Self-Concerned RealistCategory (Segment 1) (Segment 2) (Segment 3) (Segment 4) (Segment 5) (Segment 6)

Life Insurance 70.20 56.80 82.80 62.00 84.40 100.00Medical Insurance 9.30 8.00 36.10 6.00 44.40 65.45General Insurance 3.10 – 19.70 2.00 4.40 3.60Automobile Insurance 6.20 8.00 9.00 4.00 33.30 41.80Individual Pension Plan 0.60 1.60 6.60 4.00 2.20 3.60

(All figures are in percentages).

Concerned’ and the ‘Enjoyers’ segments. The emergence of infrastructure bondsfloated and guaranteed by the Government of India again reflects the risk-averseattitude of the conservative segment. It is not surprising that jewelry constitutes amajor form of investment for an Indian – the idea of procuring gold as a safeguardduring bad times being the motivation behind the tendency to hoard gold in formof jewelry. Investments into real estate are an emerging option amongst the ‘WellSettled’ segment.

If we take a look at Loans from Financial Institutions we see an interesting pic-ture emerge. Home Loans have been taken by the ‘Well Settled’ (5%), ‘Enjoyers’(8%) and the ‘Realist’ (4%) segments. The ‘Strugglers’ and the ‘Conservative’show a negligible presence of home loans. This is a reflection of the real estateboom in the Indian economy owing to the lowering of rates of interest by theGovernment of India during the early part of the decade. The increase in homeloans can also partly be attributed to the generous income tax benefits providedby the Government. The two-wheeler loans (for the purchase of motorbikes andscooters) were a maximum in the ‘Realist’ segment (9%), followed by the ‘WellSettled’ (3%) and the ‘Self-concerned’ (2%) segments. The ‘Enjoyers’ segmenthas the maximum car loan (8%) followed by the realist (7%). A DEMAT Account(for transacting in shares and stocks) was present with about 15% of the ‘Realist’segment.

In terms of insurance products and services, Life Insurance was the most cov-eted insurance product with the ‘Enjoyers’, ‘Self-Concerned’ and the ‘Realist’segments (Table 8). It was also present in the other segments but to a lesser de-gree. A similar pattern was seen for medical insurance. Insurance against theftand natural calamities was seen only in the ‘Enjoyers’ segment to any significantdegree. Investment into pension plans for post retirement benefits was again neg-ligible. It is hypothesized that this is because of the low exposure of the Indianmarket to insurance concepts and that as the market grows so will the penetrationof insurance products and services.

Media habits

The study found interesting media habits amongst the urban consumers (Table 9).In the case of print media, newspapers tended to dominate the Indian media


Table 9. Media consumption pattern of the urban segments.

Media Well Settled Strugglers Enjoyers Conservative Self-Concerned RealistCategory (Segment 1) (Segment 2) (Segment 3) (Segment 4) (Segment 5) (Segment 6)

Newspaper 91.40 81.00 98.40 72.00 93.30 92.70Periodicals 33.30 34.10 79.50 30.00 48.90 32.70Television 97.50 99.20 98.40 100.00 73.30 85.50FM Radio 24.10 46.00 59.00 16.00 26.70 40.00NewspapersEnglish

Newspapers59.30 53.60 100.00 38.90 73.80 76.90

VernacularNewspapers

52.70 70.10 100.00 69.40 42.90 73.10

MagazinesEnglish (General) 72.2 53.50 91.80 26.70 81.80 61.10English (Business) 11.1 9.30 15.50 6.70 – 16.70Vernacular 37.0 55.80 100.00 73.3 36.40 72.20

(All figures in percentages of regular readers).

scenario. Similarly, in the case of non-print media, television viewership was al-most universal. It was also found that approximately one third of all the segments,except the ‘Enjoyers’ segment, preferred to read some kind of magazine or period-ical. The ‘Enjoyers’ segment showed an exceptionally high periodical readership.The ‘Enjoyers’ and the ‘Realist’ segments preferred to read both English andvernacular newspapers. The ‘Self-Concerned’ prefer English newspapers whereasthe ‘Conservative’ and the ‘Strugglers’ prefer to read vernacular newspapers.Similar trends can be found between English and vernacular magazines.

In the non-print segment, television viewership is universal; whereas FM Radiolistenership showed some interesting patterns. For example, FM radio listenershipwas low amongst the ‘Well Settled’, ‘Self-Concerned’ and the ‘Conservative’ seg-ments. However, it was high in the ‘Enjoyers’ segment followed by the ‘Strugglers’segment. The high presence of FM radio listenership amongst the ‘Strugglers’ seg-ment does not come as a surprise. The low cost of FM radio sets, coupled with thefact that most FM stations in India play popular movie songs, make them a perfectentertainment product for this low-income segment.

To understand better the television viewership, the various television channelswere segregated into seven groups Sports, News, Music, Movies, Knowledge,Religious and General. It was found that knowledge, news and movie channelstend to dominate the ‘Well Settled’ and ‘Enjoyers’ segment. The ‘Realist’ and ‘SelfConcerned’ segment prefer watching the news, movies and knowledge channels.Movie channels were popular in the ‘Strugglers’ and ‘Conservative’ segments. Thegrowth of religious channels was a phenomenon that was particularly interesting,with the ‘Strugglers’ dominating the viewership. It is hypothesized that religiouschannels viewership will only grow in the future – as demonstrated by the increaseof religious channels from nil to over five in the last half a decade (Rajshekhar2006).


Discussion

The research raises several interesting points about the Indian urban consumer. Thesurvey started out with the assumption that the urban consumers are homogeneousin nature. To maintain this homogeneity, SEC classification was used to identify theappropriate respondent. SEC, which is based on the highest level of education andoccupation of the chief wage earner, inherently assumes that a person belongingto a particular SEC would have a similar behavioral pattern. However, this doesnot seem to hold true in the Indian context, as we discovered six new behavioralsegments.

If, on the vertical axis, we plot the monthly household income, and on thehorizontal axis we plot the age (Figure 1), it can be seen that the Indian urbanconsumer is blossoming into an hourglass figure.

It can be seen that the top of the hourglass is dominated by the ‘Enjoyers’and the ‘Self Concerned’ segments. These segments have the highest disposableincome on their hands but demonstrate different behavioral traits. Even thoughthey are extremely lucrative to the marketers, the strategies the marketers need tofollow would be quite different. Even though both segments would be interested inhedonic purchases, their buying behaviors may differ significantly. The ‘Enjoyers’

Figure 1.


could be more involved in hedonic purchases of products and services; whereasthe ‘Self Concerned’ would be more willing to purchase products and services thatfit in with their style of life. These two segments have shown an increasing interestin new age sports, such as back-packing, outdoor adventure etc. The consumptionpatterns regarding the media seem to suggest that these segments would be welltargeted by newspapers and television. Low readership of magazines in the ‘SelfConcerned’ segment may well hamper effective marketing campaigns. These twosegments could aptly be called the Hedonic Consumption Class of the Indianeconomy.

At the neck of the hourglass could be found the ‘Realist’ and the ‘Well Settled’segments. These segments have significant disposable income on their hands buthave different family size characteristics, which could affect their consumptionpattern of products and services. For example, the ‘Realist’ segment has a smallerfamily size (2.71) compared with the ‘Well Settled’ segment (3.14); however, thepercentage of married to singles differs significantly amongst these segments. The‘Well Settled’ class is mostly married while the ‘Realist’ class is mostly single,either by choice or compulsion. Both the segments have significant possession ofconsumer durables and almost similar investment patterns. However, the ‘Realist’segment seems to be a more risk taking segment, as it has a significant exposurein stocks and shares; which is conspicuously absent in the ‘Well Settled’ segment.The insurance portfolio between the two segments differ significantly, with 100%life insurance in the ‘Realist’ segment but only 70% in the ‘Well Settled’ segment.In terms of a marketing strategy, these segments would be looking for hedonicproducts and services but would weigh their decisions on practical aspects. Hence,these two segments could aptly be called the Hedonic-cum-Utilitarian Consump-tion Class of the Indian urban consumers.

The bottom of the hourglass is dominated by the ‘Strugglers’ and the ‘Conser-vatives’. Both segments have a similar income but tend to differ in terms of age,with the ‘Conservative’ segment being the oldest of the lot. Both the ‘Conserva-tive’ and the ‘Strugglers’ segments have a low possession of consumer durables.These segments have a large family size and, as such, their purchase patterns wouldbe driven by bargains and value for money. Thus, these segments could be calledthe Utilitarian Consumption Class of the urban consumers.

From the perspective of marketing, opportunities seem to exist in almost allavenues. For example, in the Insurance sector, we see a much lower presence ofgeneral insurance and medical insurance. Similarly, the urban market has yet towake up to the benefits of pension plans. In terms of savings, we have seen thatthe majority of the savings are in risk adverse areas such as government bonds,fixed deposits, public provident funds etc. Stocks and shares, and investments intoreal estate and mutual funds are some of the avenues that a marketer can hope totap. Computer and internet usage seems to be higher than the rest of India in theurban sector; however, there is scope for growth as the economy expands. In theconsumer durables market, color televisions, refrigerators, telephones, and mobilephones seem to have reached saturating proportions. Other consumer durables


have also penetrated significantly into the urban markets. This seems to suggestthat the consumer durable manufacturers need to look towards newer markets,for example in the rural sector. Scope however remains in the urban sector forhigh end electronic items, such as DVDs, camcorders, laptops etc, and other suchitems that may be considered as a luxury by the people in the lower half of thehourglass.

However, all does not seem to be well with the urban class. Besides theabove, the research also provides some implications that may be a cause for socialconcern in the coming decade. The Indian government has always assumed thatthe urban consumers belonging to SEC A and B would be the drivers of Indianeconomic growth. This is the class that has been christened the ‘Great IndianMiddle Class’ and on which lies the burden of fuelling demand and industrialgrowth in the country. However, the picture that emerges is somewhat different.Roughly, the urban consumers could be broken into a 3:4:3 ratio, with the top 30%of the population being comfortably well off, the middle 40% being fairly welloff, but the bottom 30% are struggling to stay afloat. The bottom 30% consistsof the ‘Conservatives’ and the ‘Strugglers’. The conservatives, already in theirmid 40s and early 50s may soon be out of employment and, unless they havemanaged to secure the future of their children or earnings, may soon slid offinto poverty. The ‘Strugglers’, in their late 30s, are already at the bottom of theheap: how long can they sustain their struggle is anybody’s guess. These twosegments may well be headed for urban poverty and add to the ever-growing woesof the government. The general belief that Indian seem to be earning more thanbefore is also contradicted. Even if we consider the highest earning segment, themonthly household income is close to Rs. 22,000. Given the increasing cost ofliving in the urban cities, the income may just insufficient to meet the expenses ofliving.

In terms of limitations, we would like to stress the point that the study hasconcentrated only on SEC A and B; and as such cannot be taken to be a gener-alized model of all people residing in urban towns and cities in India. More so,the study cannot say much about the SEC C, D and E classes, as they were notincluded in the study. The authors are aware that a segmentation study, withoutthe ability to classify a new respondent, is of limited use for practical implemen-tation. The authors hope to rectify the situation shortly. Nevertheless, within thelimitations of the study, it has achieved a significant milestone by identifying newbehavioral segments amongst the Indian urban consumers; thereby providing newinsights into the consumption patterns of these segments and (possibly) suggest-ing more efficient ways and means for effective marketing and focused policydevelopment.

AcknowledgmentCrorepati in Hindi refers to an individual or household that has Rs. 1 crore or more asdisposable income; where a crore is equal to Rs. 10 million (1 crore = 107).


ReferencesAgnihotri, A., 2004. Crorepatis: India’s Super Rich Multiply. Pitch Magazine.Andreason, A.R., 1984. Life status changes and changes in consumer preferences and

satisfaction. Journal of Consumer Research, 11, 184–190.Ball, G.H., and Hall, D.J., 1965. ISODATA, a novel method of data analysis and pattern

classification. Melno Park, Stanford Research Institute.Beardon, W.C., and Etzel, M. J., 1982. Reference group influence on product and brand

purchase decisions. Journal of Consumer Research, 9, 183–194.Bellanti, D., and Foster, A.C., 1984. Working wives and expenditure on service. Journal of

Consumer Research, 11, 700–710.Bhagat, R.B., 2005. Rural urban classification and municipal governance in India. Singapore

Journal of Tropical Geography, 26, 61–73.Biyani, K., and Baishya, D., 2007. It Happened in India. New Delhi: Rupa, and Co.Businessworld Editorial Team, 2005. The Marketing Whitebook 2005. New Delhi: Busi-

nessworld.Calinski, R.B., and Harabasz, J., 1974. A dendrite method for cluster analysis. Communi-

cations in Statistics, 3, 1–27.Chatterjee, A., 1995. Personality ploy. Business Today.Chatterjee, A., 1996. Marketing to new Indian consumer of generation – I. Business Today.Day, E., Davis, B., Dove, R., and French, W., 1987. Reaching the senior citizen market(s).

Journal of Advertising Research, 27, 23–30.Deb, S., 2003. She - 2004. Outlook.Dimitriadou, E., 2005. cclust: Convex Clustering Methods and Clustering Indexes.Doughlas, S.P., and Urban, C.D., 1997. Lifestyle analysis to profile international markets.

Journal of Marketing, 41, 46–53.Dunham, M.H., 2005. Data Mining: Introductory and Advanced Topics. Patparganj, New

Delhi, India: Pearson Education (Singapore) Pte. Ltd.Edwards, A.W.F., and Cavalli-Sforza, L., 1965. A method for cluster analysis. Biometrics,

21, 362-375.Eighmey, J., 1997. Profiling user responses to commercial websites. Journal of Advertising

Research, 37, 59–66.Fiedler, J., and McDonald, J.J., 1993. Market figmentation: clustering on factor scores versus

individual variables. AMA Advanced Research Techniques Forum.Friedman, H.P., and Rubin, J., 1967. On some invariant criteria for grouping data. Journal

of the American Statistical Association, C, 917–923.Fukunaga, K., and Koontz, W.L.G., 1970. A criterion and an algorithm for grouping data.

IEEE Transaction on Computers, C, 457–468.Haseloff, A.M., 2005. Cybercafes and their potential as Community Development Tools in

India. The Journal of Community Information [Online]. Available from: http://www.ci-journal.net/viewarticle.php?id=68 [Accessed June 14, 2006].

Hawkins, D.I., Best, R.J., and Coney, K.A., 2002. Consumer Behaviour – Building Market-ing Strategy. New Delhi: Tata McGraw Hill.

Henry, W.A., 1976. Cultural values do correlate with consumer behaviour. Journal ofMarketing Research, 13, 12–17.

Jacob, S., 1997. Kinetic Honda: a case study. Advertising, and Marketing.Kapoor, D., 1985. Religiosity, Caste Prejudice, Conservatism and Lifestyles in Four Faith

Groups : A Psychometric Study. University of Delhi, India, Department of Psychology.Kapur, S., 1995. Life style on two wheels. Business World.Keppel, G., and Wickens, T.D., 2004. Design and Analysis: A Researchers Handbook.

Upper Saddle River, NJ: Pearson.Kumar, R.V., and Mitra, S., 2004. Toothpaste usage and consumer profiles. Organisational

Management, XX, 5–11.


Lastovick, J.J., Murry, J.P., Msthales, E.A.J., Bhalla, G., and Scheurich, J., 1987. A lifestyletypology to young male drinking and driving. Journal of Consumer Research, 14,257–263.

McDonald, M., and Dunbar, I., 2004. Market Segmentation: How to Do It, How to Profitfrom It. Oxford, UK: Elsevier Butterworth-Heinemann.

Medhi, G.K., Hazarika, N.C., and Mahanta, J., 2006. Correlates of alcohol consumptionand tobacco use amongst tea industry workers in Assam. Substance Use and Misuse,41, 691–706.

Milligan, G.W., 1981. A Monte-Carlo study of thirty internal criterion measures for clusteranalysis. Psychometrika, 2, 187–199.

Mitchell, A., 1984. The Nine American Lifestyles. New York: Warner Books.Plummer, J.T., 1971. Lifestyle patterns and commercial bank credit card usage. Journal of

Marketing, 35, 35–41.Potter, W.J., Forrest, E., Sapolsky, B S., and Ware, W., 1988. Segmenting VCR owners.

Journal of Advertising Research, 28, 29–39.Prabhakaran, P., 1996. The denim and the Darzi. Advertising, and Marketing.Qualls, W.J., 1987. Household decision behaviour: the impact of husband and wife sex role

orientation. Journal of Consumer Research, 14, 264–277.R Development Core Team, 2005. R: A Language and Environment for Statistical Comput-

ing. Vienna, Austria.Rajeshekhar, M., 2006. Broadcasting Nirvana - The Economics of Religious Chennels.

Available from: http://www.businessworldindia.com/MAY2906/indepth04.asp [Ac-cessed 4 July 2006].

Rao, S.L., and Natarajan, I., 1996. Indian Market Demographics – The Consumer Classes.National Council for Applied Economic Research (NCAER).

Ratkowsky, D.A., and Lance, G.N., 1978. A criterion for determining the number of groupsin a classification. Australian Computer Journal, 10, 115–117.

Rich, S.U., and Jain, S.C., 1968. Social class as predictors of shopping behaviour. Journalof Marketing Research, 5, 41–50.

Robertson, E., and Conway S., 2003. Face-to-face interviewing. In: R.J. Birm, ed. TheHandbook of International Market Research Techniques. New Delhi: Kogan PageIndia Private Limited, 153–176.

Rodkin, D., 1990. Wealthy attitudes wins over health wallets: consumers prove affluence isa state of mind. Advertising Age, S4–S6.

Roy, S., and Goswami, P., 2007. Psychographics and its effect on purchase frequency – astudy on the college-goers of Kolkata, India. Decisions – Journal of IIM Calcutta, 34,63–93.

Schiffman, L.G., and Kanuk, L.L., 1997. Consumer Behaviour. New Delhi, India: PrenticeHall of India Private Limited.

Scott, A.J., and Symons, M.J., 1971. Clustering methods based on likelihood ratio criteria.Biometrics, 27, 387–397.

Sharma, L.S., and Chaubey, D.S., 2004. Consumer Behaviour towards mobile serviceproviders – a case study of consumers of Lucknow City. Survey, 44, 35–49.

Shirali, A., and Singh, I., 1997. Status symbols: an A&M ORG-MARG survey. Advertising,and Marketing.

Slocum, J.W., and Mathews, H.L., 1969. Social class and commercial bank credit cardusage. Journal of Marketing, 33, 71–78.

Slocum, J.W., and Mathews, H.L., 1970. Social class and income as indicators of consumercredit behaviour. Journal of Marketing, 34, 69–73.

Stafford, J.E., 1966. Effects of group influence on consumer brand preference. Journal ofMarketing Research, 3, 68–75.


Sudha, S., and Rajan, S.I., 1999. Female demographic disadvantage in India (1981–1991):sex selective abortion and female infanticide. Development and Change, 30, 585–618.

Tully, S., 1994. Teens. Fortune.Vriens, M., 2001. Market Segmentation: Analytical Developments and Application Guide-

lines. Austin, Texas: Millward Brown IntelliQuest.Wedel, M., and Kamakura, W.A., 1998. Market Segmentation: Conceptual and Method-

ological Foundations, Dodrecht, The Netherlands: Kluwer Academic.Weingessel, A., Demitriadou, E., and Dolnicar, S., 1999. An Examination of Indexes for

Determining the Number of Clusters in Binary Data Sets. Austria: Vienna Universityof Technology.

Wells, W.D., 1975. Psychographics: a critical review. Journal of Marketing Research, 12,196–213.

Wells, W.D., and Tigert, D.J., 1971. Activities interests and opinions. Journal of AdvertisingResearch, 35.

Witten, I.H., and Frank, E., 2005. Data Mining: Practical Machine Learning Tools andTechniques. San Francisco, CA: Morgan Kaufmann.

Documents

Sychographic Segmentation of Indian Urban Consumers