For this study, SurveyZ.com provided access to a double-opt-in national ... Tracking showed that 13,175 of the respondents opened our email message and ...
E-Shopping Lovers and Fearful Conservatives: A Market Segmentation Analysis
ABSTRACT Purpose: To classify Internet users into holiday shopper and non-shopper segments, and to profile the demographic, psychographic, and computer use characteristics of each segment. Method: Self report data come from a national U.S. sample of online Internet users. Segments are customer revealed using traditional cluster analysis. Lifestyle measures are reduce to higher order measures using factor analysis. Profiles are analyzed via.descriptive statistics, graphs and radar charts. Results: Six important segments are identified in the data. Three of the segments characterize customers who resist online shopping, even though they engage in other online activities. Security fears and technological incompetence typically inhibit these users from engaging in electronic exchange. Some Internet users simply choose not to shop online. Three of the segments describe active e-shoppers who are driven by a unique desire to socialize, minimize inconvenience, and maximize value. Research Limitations: Data come from self-report questionnaires administered and collected electronically through the Internet. Focus is placed on holiday gift buying. Since holiday shopping is very important to e-retailers, results are managerially interesting, but might not be indicative of other shopping periods. Practical Implications: To be successful, e-retailers must understand those things that motivate and inhibit customers from shopping online. Marketing activities targeted at the reticent eshoppers should focus on benefits, guarantee safeguards and facilitate technical literacy. Service, value, and online ambiance should be carefully tailored to meet the desires and expectations of each customer type. Originality /Value: The study is a replication and extension of earlier online studies which are summarized in the reviewed literature.
----------------------------Keywords: Segmentation, Cluster, Holiday, Online Shopping, Competency Category: Research Paper
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Introduction This article examines the diversity of Internet users with respect to their shopping behavior and the heterogeneity of their lifestyles and behaviors, particularly with regard to their view of online spending. It replicates and extends on the Swinyard and Smith (2003) study which was the first published paper to identify Internet shopping segments based on online shopping lifestyle measures. Using the same measures but a different data collection procedure, the present paper defines and then profiles six lifestyle segments in the U.S. and reveals some substantial differences between the segments of these Internet users. Swinyard and Smith (2003) presented results of a comprehensive study focused on Internet shopping. Among a national U.S. sample of online households, they examined the computer-oriented lifestyles that facilitated and inhibited Internet and online shopping behavior. Their study reported the lifestyles, demographics, computer use, and online behaviors of eight online segments – four online shopper segments, and four online non-shopper ones. Data for that study was collected in January 2001 using a printed questionnaire mailed to 4000 U.S. households having an online connection at home. Rather than using the mailed paper-and-pencil questionnaire of that study, we update and extend that research using online questionnaire administration. An email message was sent to online U.S. households, followed by web-based questionnaire administration. The current study provides an updated snapshot of the online shopper and non-shopper, and also reports on the effectiveness of using web-administration for questionnaire distribution. This paper also minimizes “discrepancies between academic developments and real world practice” (Wind, 1978; p. 317) in market segmentation by employing a consumer-revealed approach to segmentation. It further minimizes the “lack of systematic effort to build a -2-
cumulative body of substantive findings about consumer behavior” (p. 320) for online shoppers. Thus the key objectives of this paper are to: • replicate and extend the Swinyard and Smith (2003) research using a more contemporary data collection procedure •
illustrate the benefits of a consumer-revealed segmentation procedure over traditional methods, and thus build on the past body of segmentation research
• identify consumer-revealed Internet shopping/non-shopping segments and their characteristics Consumer-revealed segmentation is designed to identify naturally-occurring target customer groups. It can illuminate a product’s potential segments and can provide an understanding of segment motives, lifestyles, or needs (Swinyard, 1996). This knowledge permits firms to gain a strategic advantage over their competition by helping them identify the unique attitudes and characteristics of potential segments, and by giving them a focus in translating strategic opportunity into a tactical plan. Discussion of this approach is followed by an application of these procedures - the segmentation of home Internet users is based on data collected from 1824 online household heads. This study defines and profiles six online shopping segments.
Literature Previous research has used different approaches and methods to segment markets, and two basic approaches have evolved. The first is an a priori or “management-imposed” method in which the delineating variables for the segments are pre-defined by management. These variables might include demographics, purchase frequency or amount, or other observable/behavioral characteristics (see Hansen and Deutscher, 1977-1978; Gentry and Burns, 1977-1978; Bearden,
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1977). While this method can be successful in defining segments, the segments are based on imagined rather than natural boundaries, and the segments are described only to the extent that the variables defining the segments are correlated with naturally-occurring segments. The second is an analysis-based or “consumer-revealed” method. This allows the segments to be identified based on natural associations observed during the data analysis process, typically using cluster analysis (Wedel and Steenkamp, 1989; DeSarbo, Oliver, and Rangaswamy, 1989; Swinyard 1996). This clustering is most often based on consumer statements of the importance of product attributes (Howell and Rogers, 1983; Malhotra. 1986; Tantiwong and Wilton 1985), but can also be based on other characteristics, including lifestyles (when the lifestyles are product-related ones), shopping behavior toward the product, and even demographics (when demographics are highly correlated with product use as in the case with age- or income- related products). For example, Hortman et al (1990) clustered individuals on consumer demographic characteristics and subsequently estimated the importance of image attributes for each segment. When appropriate variables are used, consumer-revealed segments directly reflect the segments’ responsiveness to the product. For example, a segmentation approach that uses consumers’ “activities, interests, and opinions” (i.e., AIO’s or lifestyles) toward a product can lead to these natural homogeneous segments. This study, for example, includes many lifestyle items dealing with attitudes towards or benefits of online shopping (online prices, the novelty of online shopping, online shipping charges, being able to see or not see items before purchase, the hassle of returning online products, giving a credit card number to a computer, etc.). Using cluster analysis on appropriate variables permits consumer-revealed segments to emerge unhampered by management’s preconceptions. -4-
The existing literature provides little insight into differences between online shoppers and non-shoppers, and even less insight into the identifying lifestyles of the Internet users within or between these groups. However, several related publications warrant attention here. Chang, Cheung and Lai (2005) provided a comprehensive review of 45 empirical studies of online shopping intention and use. In this review, three antecedent dimensions were identified: consumer perceptions, website and product characteristics, and consumer characteristics. Measures from the reviewed papers are categorized within each of these dimensions. Experience, perceived risk, trust, relative advantage, and service quality are identified as elements of consumer perception. Product characteristics, web features, and risk reduction measures describe the product, process and delivery mechanism. Consumer orientations, demographics, knowledge and competence, attitudes, and psychological variables are consumer characteristics. While observed effects across studies were somewhat mixed, several trends were apparent. Risk perceptions, price, and cost typically had a negative impact on online shopping attitudes, while relative advantage and trust had a positive impact. Much of the relevant literature focuses on the perceived risks associated with online shopping and consumer attitudes that foster or inhibit online exchange. Teo and Yu (2005) reported that a consumer’s willingness to buy online is positively influenced by the dependability of the online store, reduction of uncertainty and online experience. Cho (2004) found that risk perception increases the likelihood of aborting an e-shopping transaction. Forsythe and Shi (2003) characterized four risk types (financial, product performance, psychological, and time/convenience loss). They found perceived financial risk to be the most consistent negative predictor of search frequency with the intent to buy, time spent on the web, and frequency of purchasing online. Lim (2003) examined yet a different set of perceived risks: technology, -5-
vendor and product risk. Bhatnagar and Ghose (2004) showed that consumer perceived risks decline with age and experience, and are lower in categories high in search attributes. Tan (2005) proposed vendor strategies for reducing consumer risk perceptions. The literature also gives some attention to attitudes that encourage or inhibit online shopping. Kwan, Fox, and Zinkhan (2002) studied the four domains that influence online purchasing: attitudes, experience, demographics and personality traits. They found that consumers who frequently seek product information online are more likely to purchase online. Hung-Pin (2004) showed that perceived ease of use and usefulness help drive e-shopping attitudes. Donthu and Garcia (1999) reported Internet shoppers to be convenience and variety seekers, innovative and impulsive, and less risk averse than non-shoppers. They also found Internet shoppers to be less brand and price conscious, having a more positive attitude toward advertising and direct marketing. Siu and Cheng (2001) reported that attitudes toward technological development and venturesomeness are key factors in identifying potential online shoppers. Perceived quality, computer literacy, and experience also increase a individuals willingness to shop online (Zigi and Cheung 2001) as do atmospheric variables (McKinney 2004). The literature includes a few segmentation studies of Internet users. Mathwick (2001) clustered Internet users into four segments characterized by relational norms and behavior in order to describe online social activities. These segments described transactional community members, socializers, personal connectors, and lurkers. Vijayasarathy (2003) examined relationships between shopping orientations, product types and Internet intentions. Shopping orientations (which encompass the dimensions of convenience, enjoyment, necessity, and value) and product types are shown to have a significant effect on online shopping intentions. Age, -6-
gender and income also have a significant influence on shopping intent. These findings are consistent with other studies that report internet shoppers are most likely to be younger males with more knowledge of the internet, greater education and higher incomes (Li, Kuo, and Russell 1999, Sin and Tse 2002, Swinyard and Smith 2003). Sorce, Perotti and Widrick reported that, compared to younger shoppers, older shoppers search for fewer products online. However, older shoppers purchase as much online as younger shoppers (2005). Finally, it has been shown that web-site loyalty is manifest through both attitudes and behaviors (Day 1969). Srinivasan, Anderson and Ponnavolu (2002) examined the antecedents and consequences of customer loyalty in an online context. Eight factors were identified that had impact on e-Loyalty: customization, contact interactively, care community, convenience, cultivation, choice, and character. These studies suggest that one key to understanding online shoppers and non-shoppers is an understanding of their Internet-related-lifestyles. With the exception of the Swinyard and Smith (2003) study however, none has studied online shopper/non-shopper lifestyle segments in depth. Not only did that study uncover Internet segments that differ along many Internet-user characteristics, but it also found noteworthy online-shopping deterrents. For example, online shoppers differed from non-shoppers in their perceptions of online risk, the fear of financial loss, and confusion about Internet and computer technology. In extending that study, we examine further whether online shopper segments will include an innovative segment that is an online shopping leader, and whether online non-shopper segments will include segments largely defined by (1) their fear of online financial risk and loss, and (2) confusion over online technologies.
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Method Instrument Development Measures originated by Swinyard and Smith (2003) were employed in this study. That study used a number of procedures to develop valid measures of online shopping lifestyles; items that would discriminate between not only online shoppers and non-shoppers, but between segments of online shoppers and non-shoppers. Every effort was made to ensure that a representative sample of online shopping vs non-shopping lifestyle items were included in the questionnaire. The measure-development procedures included: • Twenty depth interviews with individuals having an Internet connection at home (to identify activities, interests and opinions [AIO’s] pertinent to computer, Internet, and online shopping preferences and behaviors, and items for the Computer Competency Index [CCI]). • Three focus-group interviews, conducted in New York, San Francisco, and Salt Lake City. Each focus group included 8 to 12 participants, having the purpose of identifying Internet satisfiers and dis-satisfiers – attributes or reasons why online households do, or do not, shop on the Internet. This resulted in an attribute listing of 107 online shopping satisfiers and 54 dis-satisfiers. • An open-end computer-lifestyle pilot questionnaire administered to 322 undergraduate business students (to screen a listing of hundreds of items down to a more manageable subset of more pertinent items). This step and the above ones revealed in excess of 190 online shopping measures and 50 Computer Competency Index (CCI) measures. • Administration of scaled measures for items derived from the above to a second group of undergraduate business students (to permit factor analysis of the AIO’s etc.) • Review of factor loadings and coefficient alphas (to permit low-loading or low-alpha items to be dropped from further consideration, and leading to the identification of final items for a questionnaire) • Pre-tests of the final questionnaire among several smaller samples (to ensure questionnaire readability) Thus development of the AIO and CCI measures unique to that study relied on qualitative inquiry for item expansion, pilot testing for item screening, initial validity testing, secondary -8-
validity testing, and a final review. The questionnaire contained a 12-item Computer Competency Index (CCI) – a measure of computer and Internet use, knowledge, and competence; 14 computer and Internet frequency-of-use measures; 38 Internet shopping lifestyle statements (AIO’s); a three-item opinion leadership scale; and demographics. This resulted in a dense four-page questionnaire having over 100 individual item measures. In preparation for the current study, the factor analyses and coefficient alphas of those AIO and CCI measures were once again examined to assess whether items should be dropped from the study. One AIO item had a factor loading of 0.29 (just below our boundary criterion), but it was decided to retain this item for compatibility with the earlier (Swinyard and Smith 2003) study. No CCI item was dropped as all were found to contribute to satisfactory coefficient alphas. Data Collection The instrument was reformatted as an online questionnaire to be hosted by SurveyZ.com. Notification of the questionnaire was sent to respondents by e-mail. It seems clear that the combination e-mail/online questionnaire administration would reach a different population than a printed questionnaire mailed to online households (as used by Swinyard and Smith [2003]). We expect the former individuals would be online more, and have correspondingly greater computer knowledge and experience. For this study, SurveyZ.com provided access to a double-opt-in national consumer panel e-mail list of 400,000 online household heads. The list is biased by individuals’ willingness to participate in this panel. A similar bias would exist with even non-panel data having nonresponse numbers typical of online questionnaire administration. Many of the most important results of this study rely on within-sample comparisons, which diminishes the need for external -9-
validity. But the breadth of the sample (400,000 households) provides some assurance that it represents a useful representation of U.S. online households since it is not an insignificant portion of the 22 million U.S. households having Internet access. The initial e-mailing occurred on January 28, 2004. It was desirable for the questionnaire to arrive just after the Christmas holiday since this is the most significant retail period of the calendar year – indeed, retailers of non-essential goods report that typically 40 to 60 percent of their annual revenues occur during this period. In addition, a late-January mail-out date was chosen because by this time any young children in the household were back in school, it was expected to be a time of somewhat reduced activity in households following a busy school holiday vacation, and recollection of their holiday shopping activity would still be relatively fresh on respondents’ minds. The e-mail message introduced the study, assured respondents of confidentiality, and provided a link to the questionnaire-completion site. A contact name, phone number, and e-mail address was also provided for those recipients that wished to find out more about the study. Final Sample Tracking showed that 13,175 of the respondents opened our email message and clicked its link to the questionnaire. By the cutoff date 2100 of these online questionnaires had been completed, for a completion rate of 15.9 percent questionnaire-link clicks or 0.53 percent of all messages sent. The final number of respondents after cleaning the data set was 1824 individuals – 13.8 percent of questionnaire-link clicks, and 0.46 percent of all messages sent. This single contact response rate is in contrast with the Swinyard and Smith (2003) multiple contact response rate, using postal mail and a paper-and-pencil questionnaire, of 43.5 percent. It should be expected – and other online studies substantiate (Shaffer and Dillman, 1998; -10-
MacElroy, 2002) – that the response rate for an online questionnaire is normally very low, particularly given the instant nature of a point-and-click environment. The low click and completion rate is a function of e-mail message opening, questionnaire site visits, and questionnaire completion. The proportion of e-mail messages opened is also a function of stale e-mail addresses, spam blockers or filters, and virus-infection concerns. The percent online visits and completion is inhibited by the reluctance or inconvenience felt by the addressee to click on the link to the survey, the length of the questionnaire, dropped Internet connections and user-system slowdowns or lockups. Other online studies which have reported completion rates ranging from 10 to 20 percent among those who opened the questionnaire (Smith, 2004; Dillman et al, 2004), which is consistent with the 13.8 percent of the current study.
Results Descriptive Data This discussion will begin with a short review of descriptive information about these online households: their demographics, online spending, and Internet use. The demographics of the sample is substantially different from Smith and Swinyard’s 2003 report (based on data collected in 2001). In the present study, over 76 percent of the sample was female (vs 51 percent in the 2001 data), of which 59 percent were married (vs 76 percent in 2001). The average age for the sample as a whole was approximately 39.7 years (vs 49 years in the 2001 data). Annual household and personal income in the current sample are $45,000 and $30,000 respectively (vs $61,000 and $41,000 in the 2001 data). In this study at least – in comparison to mailed and paper/pencil questionnaires – the online questionnaire administration has appeared to attract a higher percentage of women, people of younger age, lower likelihood of -11-
being married, and having a lower household and personal income (all at p < .01). Since this questionnaire was distributed online, as expected these results are closer to the average Internet user population than to the U.S. population (Smith, 2004). While many studies report that online users are younger than non-users, we did not find this for online shoppers vs online non-shoppers. We define a respondent who personally made, influenced, or participated in an online purchase during the holiday season as an online shopper. At 39.6 years, the online shopper is not significantly younger than the 40.0 years for nonshoppers (F = 1.04, n.s.). We did find that online shoppers are significantly wealthier than online non-shoppers. Shoppers’ annual household income of $48,350 is significantly higher than the $38,660 of non-shoppers (F = 62.92, p < .001). Note that the 1999 median U.S. household income was $40,816 (U.S. Department of Commerce, 2002). And too, the annual personal income for shoppers of $31,720 is significantly higher than the $27,160 of non-shoppers (F = 30.30, p < .001). Further, the data show that online shoppers are also better educated than online non-shoppers (14.6 years vs 14.0 years of education; F = 45.74, p < .001). It was no surprise to find that online shoppers are more computer competent than nonshoppers. Our Computer Competence Index (CCI) was measured by asking respondents to indicate the extent to which 12 computer activities (e.g., install an operating system, find the lowest online price for a product) were hard (1) or easy (3). The CCI itself was transformed into a summed and standardized measure of the 12 items (mean = 0, sd = 1), and was found to have a range from -3.93 (lowest CCI) to 1.39 (highest CCI). The 12-items comprising the CCI have a desirable coefficient Alpha of .87, with each item contributing positively to the scale. Online shoppers were found to be significantly more computer competent than non-shoppers (shoppers’ CCI of +0.14; non-shopper’s CCI of -0.24 [F = 62.37, p < .001]). -12-
The measures use in this study also included a three-item averaged and standardized opinion leadership scale (alpha = .802). The items were generalized, not specific to opinion leadership for technology, computers, or the Internet (e.g., “In group discussions, my opinions are valuable”). Shoppers showed stronger opinion leadership characteristics on this scale than nonshoppers (shoppers mean of 0.06, non-shoppers of -0.11, F = 12.19 with 1, 1823 df, p < .001). We also found that online shoppers are bigger overall spenders than online non-shoppers – not for just online spending, but also for local retail and mail-order spending. Average total household holiday gift-spending for the online shoppers was $935 (online, retail and mail-order), while among online non-shoppers the average spending was lower at $556 (F = 29.33, with 1, 1869 df, p < .001). Considering all personal Internet buying during the holiday, whether for gifts or other purpose, online shoppers spent $375 online during the holiday. Of this sum, $305 was for gifts. Online shoppers also spend more time using their computers and the Internet than online non-shoppers. Online shoppers spend 24.46 weekly hours online (vs 22.78 for non-shoppers; F = 5.02, p < .05), and 11.51 weekly hours offline (vs 10.12 for non-shoppers; F = 4.43, p < .05). Total weekly computer hours are 35.51 for online shoppers vs 32.75 for non-shoppers (F = 5.61, p < .05). For online vendors, it is particularly significant to note that, compared with online shoppers, online non-shoppers reported significantly greater fear of financial loss from online shopping. Using a five-point scale (1 = “Not at all like me,” 5 = “Just like me”), respondents answered four online shopping lifestyle measures related to fear of online financial loss (all significant at p < .001): • “I don’t want to give out my credit card to a computer,” – 12 percent strong agreement -13-
(i.e., “just like me”) by online shoppers vs 32 percent by online non-shoppers • “I worry about my credit card number being stolen on the Internet,” – 28 percent strong agreement by shoppers vs 42 percent by non-shoppers • “Buying things on the Internet scares me” – 5 percent strong agreement by the shoppers vs 16 percent of the non-shoppers • “I just don’t trust Internet retailers” – 2 percent strong agreement among the shoppers vs 11 percent among the non-shoppers Thus we find in this study that – compared with non-shoppers – online shoppers are about the same age, are wealthier, are better educated, have higher computer literacy, higher opinion leadership, spend more overall, spend more time on their computers and on the Internet, and are less fearful of financial loss through online purchases. Online Shopper and Non-shopper Segments Preparatory to identifying online shopper and non-shopper segments, the 38 online shopping lifestyle items in Table I were factor analyzed using principal components analysis to reduce them to a smaller and more manageable number of variables or factors. Online shoppers and online non-shoppers were first factor analyzed separately on these items, but the results from each group were so similar as to deserve the same factor labels. Inasmuch as the loadings for the two groups were to be compared, however, a single factor solution for the shoppers and the nonshoppers combined was desirable and so was used. A unity eigenvalue approach (using varimax rotation) provided good explanatory power for the final factor solution. Eight factors were found to be the key satisfiers and dis-satisfiers for Internet shopping. Of the variance in the original measures, 61.7 percent is explained by the factors. Table I reports the factors and loadings for these 38 online shopping lifestyle items. Eight factors were identified. Each of these is shown (in bold) in Table I, accompanied by its associated
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measures – lifestyle characteristics or descriptors that respondents perceive as being related. The factors define attributes having impact on whether internet users will or will not be online shoppers. Six of the attributes were almost identical to those identified by Swinyard and Smith (2003): • • • • •
love e-shopping find e-shopping to be a hassle have online financial fears don’t know how to shop online like the value of online shopping
Two additional factors emerged from the data, being: • like the sociability of brick and mortar shopping • have an online shopping “support group” — Table I Here — The respondents’ scores for each of these eight factors were next calculated. In doing this, for simplicity of interpretation we chose to use an SPSS procedure that simply standardizes the factor scores (i.e., Anderson-Rubin coding). For each factor the method produces an overall sample mean factor score of 0.0 and sd of 1. These scores are shown in radar charts in Figure 1, which compares online shoppers to non-shoppers. Note that the radar charts are just line charts rolled into a circle. The average factor score for the entire sample ( i.e., combining both online shoppers and online non-shoppers) is 0.0 for each factor. The chart’s vertical axis reports each group’s average standardized factor score in comparison with the entire sample (which is 0.0 for every factor). Differences in the factor scores for each group will show the extent to which that group differs from the average respondent. For example, if a segment has a factor score of +1.0, its score on that factor is a full standard deviation above the sample average. — Figure 1 Here —
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As shown in Figure 1, online shoppers are significantly higher than non-shoppers with respect to “like e-shopping convenience,” “like e-shopping value,” and “love e-window browsing.” Non-shoppers are higher in “hate the e-shopping hassle,” “like socializing at brickand-mortar stores,” “online financial fears,” “don’t have the skills to shop online,” and “have no e-shopping support group” (all at p < .01 except “hate the e-shopping hassle” at p < .05). Online Shopper & Non-shopper Segments Using respondents’ factor scores as input data, a cluster analysis was separately conducted for the online shoppers and for the online non-shoppers. The SPSS “k-means” clustering procedure was used, which is a procedure appropriate for larger data sets. The clusters identified from this procedure are market segments of online shoppers and online non-shoppers. These segments are reasonably homogeneous with respect to their activities, interests, and opinions about shopping on the Internet, and are quite heterogeneous between the segments. The segments to emerge from this clustering procedure were given labels based on the online shopping lifestyle factors which best described them. The first of these cluster analyses isolated three online shopper clusters or segments, which we identified as, “Socializers,” “e-Shopping Lovers,” and “e-Value Leaders.” The second cluster analysis identified three online non-shopper clusters/segments, identified as, “Fearful Conservatives,” “Shopping Averters,” and “Technology Muddlers.” Table II reports the statements most and least often agreed with by each segment, and Table III summarizes their demographics and ownership characteristics. — Table II and Table III Here — The Online Shopper Segments Internet shoppers comprise 63 percent of online households – again, these are families -16-
having an online connection in which the respondent personally made, influenced, or participated in an online purchase during the holiday season. This study shows that internet shoppers belong to one of three online shopping segments: “Socializers”, “e-Shopping Lovers”, and “e-Value Leaders”. E-retailers can influence each these segments through targeted sales and marketing activities. The appeal of the Socializers is moderately high. While members of this segment spend comparatively more at local retail stores, they are also very active spenders online (Table III). Perhaps most important, Socializers are opinion leaders that can champion online shopping among their friends. Winning the business of Socializers also wins their influence. The appeal of the e-Shopping Lovers is high. Not only does this segment represent a significant share of online shoppers, but members of this segment spend comparatively more online. Unfortunately, e-Shopping Lovers are not general leaders of opinion and are not likely to be champions of online shopping. A vendor who wins over an e-Shopper Lover will win the business of that one shopper. The appeal of e-Value Leaders is very high. Compared to all others, these internet users spend the most time online and are the most computer competent. They are also the premier opinion leaders of online shopping world. Vendors who win over an e-Value Leader are likely to win many times over since they capture both the online shopper’s purchase and influence. For both online shopper segments and online non-shopper segments, summaries of their market share, spender share, spending share, purchasing index, demographics, and online shopping lifestyles are shown in Table IV. That table also includes our conclusion of the relative online vendor attractiveness of each segment, based on the above measures. – Table IV Here – -17-
The Online Non-shopper Segments Online non-shoppers are defined as those households in which the head of household did not make, influence, or participate in a personal online purchase during the holiday buying season. To the extent that online purchases were made by these segments, the purchases were made by household members other than the household head. Online non-shopper households comprise 37 percent of all online households. This study shows that non-shoppers belong to one of three online segments: “Fearful Conservatives”, “Shopping-Averters”, and “Tech-Muddlers”. The appeal of the Fearful Conservative is particularly low. This small segment is characterized by its online insecurity and limited computer competency. Since significant barriers must be overcome before the Fearful Conservative will embrace online shopping, this segment is not an attractive target for the Internet retailer.. The appeal of Online Shopping Averters is moderate. If and when these individuals get involved in online shopping, they are in a position to make a considerable impact on this market. Averters would be easier to convert to online shopping than the other non-shopper segments. They are potential online shoppers who are most likely to be influenced by online shopping champions such as Socializers and e-Value Leaders. The appeal of Tech Muddlers is Very Low. Not only do Tech Muddlers lack important computer skills, but they also have relatively little influence over the opinions of others. This is an unpromising target segment for e-retailers that is not likely to become an online shopping champion any time soon.
Concluding Discussion As a result of the data collection method, this project does have limitations. Descriptive -18-
research is intended to characterize a population of interest, normally using a sample from the population. A valid representation of a population requires (1) a proper population specification, and (2) a probability sample from that population such that every individual in it has a known chance of being included in the sample. In the conduct of descriptive research projects, however, sub-optimal decisions often must be made in population operationalization and in sample selection. This study suffers from both difficulties, since the defined population (home Internet users) differs from the operational population (home Internet users who joined an opt-in panel of respondents). And the optimal sample (random selection, with 100 percent response) differs from the operational sample (self-selection due to non-response bias, with very low response). So all that can truly be said about our results is that they represent those respondents who replied from among an opt-in panel of home Internet users. With that caveat, let us proceed to the conclusions. This research shows that online shoppers differ substantially from online non-shoppers. Compared with online non-shoppers, it shows that online shoppers are younger, wealthier, better educated, have higher “computer literacy,” and are bigger retail spenders. They also spend more time on their computer, and are less fearful about financial loss resulting from online transactions. These conclusions reaffirm those of earlier research (Swinyard and Smith 2003) using data collected online, rather than through the mail. Among the online shopper segments are online shopping leaders (the Socializers and eShopping Lovers). This online study shows a lower level of online shopping fear than the Swinyard and Smith (2003) study. Among the non-shoppers, 48 percent (70 percent in the 2003 study) – and 26 percent of the shoppers (33 percent in the 2003 study) – agreed with the statement, “I don’t want to give a computer my credit card number.” Sixty one percent of the non-shoppers (75 percent in the 2003 study) – and 47 percent of the online shoppers (similar to the 2003 study) – agreed that, -19-
“I worry about my credit card number being stolen on the Internet.” These data show the persistence of the key shopping deterrent of fear of financial loss, but also suggest that it is declining. If fears of online shopping can be minimized, a substantial increase in the overall spending in the e retail market will be observed. The research identifies six segments descriptive of online households - three online shopper groups, and three online non-shopper groups. Each segment contains individuals which use and perceive the Internet differently. Properly addressing these segments in any marketing campaign requires an understanding of the differences between them and the unique perspectives maintained by each. A variety of marketing opportunities exist among the different segments. Some basic strategies are suggested by the segment profiles to assist online retailers to optimize their opportunities among the segments. Catering to the Online Non-shopper New market development and market penetration strategies help expand a retailer’s customer base, which leads to greater revenue and profitability. More than one third of those surveyed did not shop online. Security concerns and internet complexities inhibited most of these users from entering the online shopper ranks. In particular, several tactics are necessary to get more online non-shoppers to click their way through the “check out” button, including the following. Help online non-shoppers take the initial plunge. Online non-shoppers need confidence gained through experience, perhaps best obtained through trial checkouts or checkout tutorials. Online vendors should routinely provide hand-holding assistance to the new shopper (e.g., “New to Online Shopping? Click Here”), letting new users experiment with the checkout procedure without having to commit themselves. -20-
Provide greater safeguards. Financial safety, easier payment systems, greater reassurances, and clearer safeguards – these are all in demand by online shoppers. The use of Verisign, Authorize.net, Billpoint, and Paypal, etc. are appropriate measures, but it is clear that these, as well as the display of guarantees, signs, or certificates of safety is insufficient to alleviate their online shopping fears. Return and business policies comparable to catalog firms are desirable if not necessary to enhance security. Many online shoppers find that to their dismay there is a 20 percent restocking fee on returns and even on exchanges. More is needed, including not only pre-shopping but postshopping assurances intended to reduce dissonance felt by the new shopper. A step in the right direction is eBay’s $2000 “insurance coverage” for some sellers. Improve technical literacy. Many Internet users need online education and simplified technologies. Virtually no e-retailers tackle the job of tutoring or training prospective customers. Store-based shopping tutorials, simplified shopping cart versions, and simplified checkout are necessary for the naive shopper. This includes strenuous error-testing – e.g., test what happens if a shopper clicks the “back” button on their browser during a critical check-out step, or if they enter credit card information incorrectly. Energize the market. While some online non-shopper groups (the Fearful Conservatives and Tech Muddler segments) exhibit high fear and low Internet competence, these factors are not the sole deterrents to online shopping. There also exists a more competent and less fearful group of online non-shoppers – the Online Shopping Averters (see Table II). Online shopping is of low interest to this segment of Internet users, despite the fact that they are similar in their literacy and computer use to even the highly motivated eShopping Lovers. Online shopping is either an avoided or spurious activity undertaken by the Averters segment, who appear to be influenced by -21-
opportunity rather than deterred by fear. Overcoming this shopping reluctance will require promotional strategies for such spurious shoppers and variety seekers, and loyalty programs to remind them of online shopping opportunities and to keep them active. Catering to the Online Shopper The market-driven approach to segmentation used in this study revealed three types of online shoppers: Socializers, eShopping Lovers, and eValue Leaders. These online shopping segments are rewarded by different dimensions of the online shopping experience. Socializers seek more social interaction. Online retailers can appeal to this segment by enhancing the social aspects of online shopping. For example, they can do the following. Online social interactions. Internet vendors can improve social interaction between online customers through such provisions as user forums and message boards, chat rooms, interactive entertainment with other customers, FAQs, how-to demonstrations, and other community-building events. These will not ameliorate customers’ needs for social interaction, but if a prospect is encouraged to chat with other customers currently on the site, a virtual community bond is being formed. This gives newcomers more information resources and bolsters their courage to check-out. It could also give them a social reason to revisit the site. Personalize services. e-Retailers must offer more personalized services and support such as personal shoppers, virtual models, follow-up phone service, live online chats with service personnel. Some shopping sites are experimenting with these (e.g., Land’s End’s “virtual model”). Many online users “want to see things in person before [they] buy” (40 percent of total respondents), and agree that “it would be a real hassle to return merchandise bought online” (47 percent).With the magnitude of these concerns, it is fashion e-retailers should be doing more to permit prospective buyers to simulate seeing things in person before they buy, to provide precise -22-
dimensioning measurements and sizing, etc. Provide ambiance-enhancers. Physical stores maximize ambiance with location, decor, displays, lighting, salesperson uniforms, and music. Online vendors need to define and embrace more fully the virtual counterparts of visual displays, end-caps, dramatic lighting, in-store couponing, video displays, in-store sampling, music, etc. For example, simple technologies such as background video streaming and music are available to them. While perhaps few online retailers would choose to use up bandwidth with streaming music or video, as compression technologies improve, and as greater numbers of households move to broad-band online connections, these should be examined as enhancements to the online ambiance package. Translating visual display, end-cap displays, and dramatic merchandise lighting into a virtual showroom will require ingenuity and creative thinking. Encourage opinion leaders. The relatively large segment of eShopping Lovers love the convenience of online shopping. For them, brick and mortar shopping is a utilitarian experience. e-Retailers appeal to this segment by reducing the time and energy needed to acquire goods and services. And while these are the e-shopping opinion leaders, 57 seven percent of them at least somewhat agree that “none of [their] friends shop on the Internet.” That represents a great many friends who could be strongly influenced by this technically competent and enthusiastic segment of online shoppers. Online vendors should reach out to them with greater reward systems not just for buyer referrals but for passing along Internet education. They could be challenged by eretailers to display their Internet prowess through a contest and award/reward system, with a resulting emotional or loyalty commitment to the site. Similar strategies should work with the smaller Socializers segment. The strategies discussed above are merely intended to be illustrative. Online vendors -23-
know their business, and need to be proactive in knowing their customers. The primary contribution of this study is that it reveals and profiles the market-driven segments that can uniquely respond to distinctive vendor strategies. e-Retailers that continue to assume that all online visitors are alike will continue to miss opportunities to maximize the loyalty of their existing customer base, to attract customers from other sites, and to educate and convert noncustomers.
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Figure 1 Average Factor Scores Online Shoppers vs Online Non-Shoppers Overall Mean = 0, sd = 1
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Figure 2 Average Factor Scores Shopper and Non-Shopper Segments Overall Mean = 0, sd = 1 a–c: Online Shopper Segments
d–f: Online Non-shopper Segments
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Table I Factor Analysis of Online Shopping Lifestyle Measures
Lifestyle Item I enjoy buying things on the Internet I think online buying is (or would be) a novel, fun way to shop I like having products delivered to me at home I think Internet shopping would avoid the hassle of local shopping I would shop on the Internet (more) if the prices were lower I like it that no car is necessary when shopping on the Internet I would like not having to leave home when shopping It would be a real hassle to return merchandise bought online I dislike the idea of shipping charges when buying on the Internet I don't like having to wait for products to arrive in the mail It's hard to judge the quality of merchandise on the Internet I disklike the delivery problems & backorders of Internet buying I want to see things in person before I buy Local stores have better prices & promotions than Internet stores I like the "energy" & fun of shopping at local retail stores I like to go shopping with my friends For me, shopping in stores is a hassle I like the help & friendliness I can get at local stores I worry about my credit card number being stolen on the Internet Buying things on the Internet scares me I don't want to give out my credit card number to a computer I want my purchases to be absolutely private I just don't trust Internet retailers I'd have a hard time searching the Internet to find what I need I don't think Internet stores carry things I want I find the Internet ordering process is hard to understand & use I don't know much about using the Internet I think the Internet offers lower prices than local stores I think Internet shopping offers better selection than local stores I think Internet shopping offers better quality than local stores I think local stores have better service policies than Internet stores I often go to the Internet for product reviews or recommendations I often go to the Internet to preview products I like browsing on the Internet I always search for the lowest price in just about everything I buy None of my friends shops on the Internet I often buy using lay-away or store payment programs I often return items I have purchased
Factors and Factor Scores Hate the Like Online Don't Know Like Love Lack an Love e-Shopping B&M Financial How to e-Shopping e-W indow e-Support e-Shopping Hassle Sociability Fears Shop Online Value Browsing Group 0.66 -0.18 -0.11 -0.23 -0.22 0.32 0.27 -0.10 0.66 -0.16 -0.07 -0.24 -0.24 0.32 0.21 -0.10 0.66 -0.18 -0.16 -0.09 -0.16 0.21 0.30 -0.10 0.65 -0.16 -0.37 -0.06 -0.08 0.29 0.18 -0.04 0.61 0.37 0.02 0.03 0.03 -0.17 0.05 0.03 0.60 -0.08 -0.38 0.03 -0.09 0.28 0.29 -0.05 0.54 -0.11 -0.53 0.02 -0.04 0.29 0.25 -0.02 -0.10 0.65 0.11 0.27 0.10 -0.09 -0.04 0.09 0.22 0.65 0.06 0.07 -0.02 -0.17 0.08 0.10 -0.19 0.62 0.12 0.22 0.14 0.03 -0.01 0.18 -0.06 0.61 0.18 0.36 0.15 -0.09 -0.01 0.01 -0.10 0.60 0.03 0.04 0.49 -0.10 0.01 -0.01 -0.31 0.50 0.37 0.23 0.23 -0.16 0.02 0.07 -0.17 0.45 0.25 0.12 0.28 -0.18 0.04 0.24 -0.14 0.22 0.79 0.14 0.15 -0.07 -0.03 0.13 0.05 0.08 0.78 0.13 0.09 -0.04 -0.01 0.10 0.32 -0.09 -0.71 0.02 0.04 0.20 0.11 -0.02 -0.13 0.12 0.57 0.14 0.20 -0.11 0.20 -0.08 -0.03 0.23 0.12 0.80 0.11 -0.13 0.05 0.02 -0.23 0.16 0.15 0.71 0.27 -0.09 -0.10 0.18 -0.26 0.17 0.10 0.71 0.26 -0.15 -0.06 0.07 0.18 0.21 0.01 0.62 -0.07 0.05 0.06 0.00 -0.27 0.38 0.12 0.50 0.29 -0.07 -0.09 0.18 -0.10 0.15 0.11 0.12 0.74 -0.10 -0.16 0.10 -0.06 0.24 0.11 0.07 0.73 -0.19 -0.09 0.05 -0.18 0.16 0.10 0.22 0.70 0.01 -0.14 0.12 -0.06 -0.10 0.08 0.36 0.45 0.04 -0.27 0.14 0.19 -0.05 -0.09 -0.08 -0.09 0.78 0.18 -0.04 0.36 -0.11 -0.21 -0.15 -0.10 0.67 0.20 -0.06 0.34 -0.20 -0.14 -0.08 0.02 0.65 0.17 0.03 0.09 0.22 0.20 0.02 0.39 -0.54 0.05 0.10 0.18 0.02 -0.09 -0.07 -0.10 0.20 0.81 -0.07 0.15 -0.03 -0.04 -0.10 -0.10 0.15 0.81 -0.11 0.31 -0.01 0.07 0.02 -0.24 0.17 0.58 -0.02 0.15 0.12 0.08 0.15 -0.05 -0.09 0.45 0.27 -0.09 0.25 -0.12 0.04 0.00 0.04 -0.10 0.68 0.00 -0.18 0.22 0.20 0.23 -0.22 0.13 0.59 -0.07 0.24 0.16 0.04 0.20 -0.01 -0.05 0.54 -31-
Table II Most and Least Frequent Online Shopping Lifestyle Descriptors, By Segment (Ranked Order, Based on Relative Standard Scores) a: Online Shoppers Most Descriptive
Least Descriptive
I like to go shopping with my friends. I like the "energy" & fun of shopping at local retail stores. I like the help & friendliness I can get at local stores. I want to see things in person before I buy. Local stores have better prices & promotions than Internet Socializers stores.
For me, shopping in stores is a hassle. None of my friends shop on the Internet. I think Internet shopping offers better quality than local stores. I would like not having to leave home when shopping. I think Internet shopping offers better selection than local stores.
I would like not having to leave home when shopping. None of my friends shop on the Internet. I think Internet shopping would avoid the hassle of local shopping. For me, shopping in stores is a hassle. I would shop on the Internet (more) if the prices were lower. Lovers eShopping
I like the help & friendliness I can get at local stores. I like the "energy" & fun of shopping at local retail stores. I like to go shopping with my friends. I'd have a hard time searching the Internet to find what I need. I find the Internet ordering process is hard to understand & use.
I think Internet shopping offers better selection than local stores. I think Internet shopping offers better quality than local stores. I think the Internet offers lower prices than local stores. eValue I enjoyLeaders buying things on the Internet. In group discussions my opinions are valuable.
I dislike the idea of shipping charges when buying on the Internet. It's hard to judge the quality of merchandise on the Internet. It would be a real hassle to return merchandise bought online. I want to see things in person before I buy. I think local stores have better service policies than Internet stores.
b: Online Non-shoppers Most Descriptive Buying things on the Internet scares me. I don't want to give out my credit card number to a computer. I worry about my credit card number being stolen on the Internet. I just don't trust Internet retailers. I want to see things in person before I buy. Fearful Conservatives Local stores have better prices & promotions than Internet stores. I dislike the idea of shipping charges when buying on the Internet. I would shop on the Internet (more) if the prices were Averters lower. I think online buying is (or would be) a novel, fun way to shop. I like having products delivered to me at home. I'd have a hard time searching the Internet to find what I need. I find the Internet ordering process is hard to understand & use. I don't think Internet stores carry things I want. Tech Muddlers I don't know much about using the Internet. Buying things on the Internet scares me.
Least Descriptive I enjoy buying things on the Internet. I think online buying is (or would be) a novel, fun way to shop. I like having products delivered to me at home. I think Internet shopping would avoid the hassle of local shopping. I think Internet shopping offers better selection than local stores.
I worry about my credit card number being stolen on the Internet. I don't want to give out my credit card number to a computer. Buying things on the Internet scares me. I just don't trust Internet retailers. I want my purchases to be absolutely private.
I enjoy buying things on the Internet. I think online buying is (or would be) a novel, fun way to shop. I like having products delivered to me at home. I often go to the Internet to preview products. I think Internet shopping would avoid the hassle of local shopping.
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Table III Demographics, by Segment
Characteristic n= % Demographics: Percent W omen Percent Married Age in years Years of Schooling +++ Annual HH income in $000 +++ Annual Personal Income in $000 +++ Spending: 1 Entire HH Gift Purchases at local retail stores + Entire HH Gift Purchase through mail-order catalogs +++ Entire HH Gift Purchases on the Internet +++ Total HH Gift Purchase (Retail + Mail-Order + Internet) +++ Internet HH Gift Purchase as % of Total HH Gift Purchase Total Personal Spending Online +++ Computer Use and Competence: Weekly Hours Online ++ Weekly Hours Offline ++ Total W eekly Hours (on + off-line) ++ Computer Competence Index (mean = 0, sd = 1) +++ Opinion Leadership: Opinion Leader Index (mean =0, sd =1) +++ Internet Connection at Home: Dial-up Modem Broadband Cable or ISDN DSL Line
---------
Online Shoppers eShopping eValue Socializers Lovers Leaders 270 19%
296 21%
311 22%
Online Non-shoppers Fearful Shopping Tech Conservatives Averters M uddlers 151 11%
200 14%
162 12%
82% 62% 38.41*** 14.59** 50.18 30.71***
77% 64% 37.81*** 14.38** 47.14 29.65***
73% 59% 40.69*** 14.76** 51.28 34.44***
76% 53% 39.77 13.79*** 37.82 25.09**
75% 53% 39.18 14.30*** 41.35 29.34**
76% 67% 38.64 13.87*** 36.99 26.99**
625.14* 45.72 227.88 898.45 25% 305.94
536.90* 44.42 353.65 934.96 38% 427.90
493.76* 73.32 307.29 874.27 35% 376.15
496.70 16.69 26.82** 540.21 5% –
433.25 24.35 83.89** 541.48 15% –
501.06 36.19 32.33** 572.54 6% –
22.68** 10.43 32.84*** .05**
23.54** 10.25 32.99*** .14**
26.31** 12.26 38.20*** .30**
23.54** 9.84 33.26** -0.31***
23.98** 10.93 34.80** 0.15***
20.26** 8.14 28.08** -0.70***
.14*
.00*
.15*
.04**
-.04**
-.29**
46% 54% 32%
46% 54% 35%
44% 56% 31%
59% 41% 22%
53% 47% 29%
62% 38% 27%
20%
18%
23%
15%
16%
10%
1
During the previous holiday season Significant within Shoppers and Non-Shopper categories: *** p < .01, ** p < .05, * p < .10, ‘ p = .105 Significant across Shopper and Non-Shopper categories: +++ p < .01, ++ p < .05, + p < .10
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Table IV Segment Summaries and Online Vendor Value a. Online Shopper Segments Socializers
e-Shopping Lovers
e-Value Leaders
Market share (% of online HH)
19%
21%
22%
Spender share (% of people)
31%
34%
35%
Spending share (% of dollars)
26%
40%
35%
Purchasing index: Spending share ÷ Market share
1.32
1.87
1.54
Distinctive Demographic Characteristics
Socializers, the smallest of the online shopping segments. Among the online shopper segments, members of this segment use their computers least often. The most heavily populated by women, this segment is of about average age for the sample and has average personal and household income.
The e-Shopping Lover segment is the second largest segment. This is the youngest of the six segments. This segment represents the largest share of online spending, spent more online than any other segment, and reported that nearly one dollar in four was spent online during the holiday.
The largest of the segments, and shop more online than other segments. It is the wealthiest segment, and has marginally the oldest average age of the segments. e-Value Leaders spend more time on their computers than any other segment and are the greatest online users.
Distinctive Lifestyle Characteristics
Socializers love getting out of the house. More than other segments they shop in brickand-mortar stores with friends. There, they like to be waited on, want to see things in person before they buy, and tend more than other segments to believe B&M stores to provide better prices than online stores. Socializers like the convenience of occasional online shopping, deeply dislike the hassles of buying online, and are average in their online financial fears. At 0.15 Socializers are tied with e-Value Leaders as having the highest opinion leadership scores.
Their friends tend not to shop online, but more than other segments, this stay-at-home group of shoppers would love to never leave the house to shop. They find B&M shopping tiresome. Even though they have only slightly above-average computer literacy, the eShopping Lovers find the online purchasing process easy to understand and use. This segment seems not to lead opinion for online shopping. They prefer solitary shopping, and their lack of an e-support group suggests that they do not surround themselves with like-minded individuals. They are above average in opinion leadership (0.01).
More than other segments they think the Internet offers better selection than B&M stores, quality, and lower prices. They love online shopping, are not unhappy about online shipping charges, online merchandise returns, need not see things in person before they buy, and have quite low online financial fears. They are more energized by the above online shopping values than by its convenience. Their friends also shop online. They are unaffected by any “hassle” connected with online shopping. At 0.15 Socializers are tied with Socializers as having the highest opinion leadership scores.
Internet Vendor Appeal
M oderately High. Although not a leading online shopping group, this segment has a steady approach toward online shopping and are capable leaders of social opinion.
High. A large online shopping segment which gives current ROI but should not be viewed as a long-term investment for opinion leadership.
Very high. A large segment which includes the opinion leaders of online shopping, eValue Leaders are ideal targets for e-retailers as they provide both current ROI and longterm synergy with others.
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b. Online Non- Shopper Segments Fearful Conservatives
Online Shopping Averters
Technology Muddlers
11%
14%
12%
Spender share (% of people)
–
–
–
Spending share (% of dollars)
–
–
–
Purchasing index: Spending share ÷ Market share
–
–
–
Distinctive Demographic Characteristics
Fearful Conservatives represent the smallest of any segment. Members of this group are less likely than most others to be married, and has the lowest income of any group. But they are not the smallest holiday spenders, with a greater share spent in B&M stores than other segments. Fearful Conservatives are next to the lowest group in computer competence.
Averters simply avoid shopping online. They have the computer competence to do so, spend a high number of weekly hours on the computer, but steadfastly avoid checking out an online shopping cart. Members of this segment spent less than any other segment during the holiday. They are about average in income. This is one of the older segments.
Members of this group are not remarkable in their age, but they have below-average education and the lowest income of any group. Their B&M store spending is about average, but more discretionary holiday spending is low. Tech Muddlers have the lowest computer literacy of any group. They simply do not know how to shop online.
Distinctive Lifestyle Characteristics
They report the greatest fears of Internet purchasing. They do not trust online retailers, do not enjoy even the idea of online buying, and want to see things in person before they buy. Despite feeling they have the necessary skill, online shopping frightens them, and they lack a support group to guide them through these fears. However, they are somewhat above average in opinion leadership (0.05).
Members of this segment prefer local stores over Internet ones, believing the prices and promotions are better, and disliking online shipping charges. They use the Internet to check prices and products, and like products delivered to their homes. They have few credit card fears about online buying, but this is a passive group when it comes to online shopping. They are even passive in influencing their friends, having an opinion leadership score somewhat below average (-0.06).
Tech Muddlers have trouble searching the Internet, figuring out the online ordering process, and are afraid of online buying. They have concluded that Internet vendors do not carry what they want. They do not find even e-window shopping interesting. They dislike having to wait for products to be delivered to them at home. They have the slowest Internet connection of any segment, and are the least likely of any segment to lead leadership (opinion leadership score of 0.29).
Internet Vendor Appeal
Low. This small shopping group is characterized by insecurity and is not an attractive target for the online retailer.
M oderate. If and when these individuals get involved, they are in a position to make a considerable impact to the online shopper market.
Very Low. This is an unpromising target segment for e-retailers, and is not likely to become an online shopping champion any time soon
Market share (% of online HH)
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