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PoœmcKm xypHan MeHegxMeHTa 20 (2): 224-246 (2022)
Russian Management Journal 20 (2): 224-246 (2022)
TREATING CUSTOMERS AS INDIVIDUALS IN ONLINE RETAIL
K. S. GOLOVACHEVA, M. M. GOGUA, M.M. SMIRNOVA, O. N. ALKANOVA
Graduate School of Management, St. Petersburg State University, Russia
Goal: the purpose of the current paper is to examine how five consumer characteristics (namely, hedonic shopping orientation, comparison shopping proneness, consumer confusion proneness, privacy concerns, and awareness of privacy control) influence the need for different forms of customer experience (CX) individualization. Methodology: the study is based on an online survey of a representative sample of 586 Russian online consumers conducted in mid-2021. Several consumer groups with different preferences for CX individualization are identified using cluster analysis; then multinominal logit modelling is used to define whether five consumer characteristics can predict group membership. Findings: the results empirically confirm that consumers differ in the need for CX individualization and demonstrate that all five consumer characteristics do work in predicting the need for CX individualization, but their role varies for different CX individualization strategies. Originality and contributions: the paper is the first to jointly examine three CX individualization strategies that online retailers may use to interact with customers: content personalization, product customization, and interaction humanization. The results of the study shed light on CX individualization strategies that firms should use to address the diverse consumer needs in their long-term strategies.
Keywords: individualized customer experience, content personalization, product customization, interaction humanization. JEL: M31, O33.
INTRODUCTION
The era of mass marketing, wherein consumers were offered standardized products, information, and service support, is far gone . Extant market leaders almost unanimously declare that modern consumers want an individualized customer experience (CX) that is tailored to their preferences, needs, and
consumption situations [Lindecrantz, Gi, Zerbi, 2020] . Leading online retailers put a lot of effort into CX individualization . When entering a familiar online store, modern customers see the selection of product recommendations and offers based on their previous purchase and search history . When or-
This research has been conducted with financial support from St. Petersburg State University, grant ID 77098515 .
Postal address: 3, Volkhovskiy per., Graduate School of Management, St. Petersburg State University, St . Petersburg, 199004, Russia.
© K. S . Golovacheva, M. M. Gogua, M. M. Smirnova, O . N. Alkanova, 2023 https://doi . org/10 .21638/spbu18.2022 . 204
dering a product online, modern customers can modify the color, shape, material to get the product that best matches their needs and highlights their uniqueness . CX individualization implies that online retailers go beyond delivering different offers and products to different segments comprised of similar customers but treat each customer as an individual with uniquely relevant products, offers, and communications [Lindecrantz, Gi, Zerbi, 2020] .
The focus on CX individualization became even more pronounced during the COVID-19 pandemic that made an online channel an almost exclusive way to buy and interact with brands . Acknowledging all the benefits of online shopping, consumers perceived it as dehumanized, which, under the circumstances, made them feel detached and disconnected [Waytz, 2019; Youn, Rana, Kopot, 2022] . As consumer-brand interactions in in-person channels shrank to an extreme minimum, customers expected online retailers to compensate for the loss of human touch . It made online retailers introduce options that can replicate human interactions online and make an emotional connection digitally [KPMG, 2020; Paun, 2022] . Voice-assistance, Al-powered (artificial intelligence — AI) chat bots, video consultations appeared on the market to address the emerging consumer need [Hoyer et al., 2020]. Hence, a human-like approach, that was previously ignored by online retailers, became an indispensable ingredient of individualized CX in the new reality
While the movement toward CX individ-ualization seems irrevocable, it becomes clear that not all customers equally crave for an individualized CX . Some researchers suggest that consumers may intentionally prefer non-individualized experiences when they have extensive knowledge and expertise in a product category and want to explore alternatives on their own rather than relying on retailers' personalized recommendations [Iacobucci, 2006; Shen, 2014]. Others claim that consumers may sacrifice an individualized CX in favor of more privacy [Awad, Krishnan,
2006] . Despite consumer heterogeneity in the need for CX individualization is acknowledged, it is still unclear what differentiates those who expect CX individualization from those who don't. Extant literature is both limited and fragmented in this regard . It only sheds light on a limited number of consumer characteristics that are related to the need for individualized CX but does not provide a complex consumer profile
Furthermore, companies possess different tools and strategies that enable them to address their customers individually, including personalization, personification, one-to-one marketing, customization, customerization, humanization, anthropomorphism [Aksoy et al. , 2021; Blut et al. , 2021; Wind, Rangas-wamy, 2001] . All sorts of CX individualiza-tion tools abound in the Russian e-commerce market . The leading marketplaces Ozon1, Wildberries2, AliExpress3, and Yandex Market4 offer personalized recommendations based on customer purchase history and demographic characteristics . VseMayki5 and PrintBar6 provide product customization by allowing customers create their own designs of clothes. Human-like chatbot-assistants Lola in L'Etoile7 and Alyona in M-Video8 help customers navigate through the most frequent issues like returns, delivery options and gift cards making the customer journey more individualized. Extant studies have predominantly explored one CX individuali-zation tool at time but have rarely compared
1 Ozon. URL: https://www.ozon.ru (accessed: 30.10 .2022) .
2 Wildberries . URL: https://www.wildberries.ru (accessed: 30 10 2022)
3 AliExpress . URL: https://aliexpress. ru/?gatewayAdapt=glo2rus (accessed: 30 .10. 2022) .
4 Yandex Market URL: https://market.yandex. ru (accessed: 30 10 2022)
5 VseMayki . URL: https://www.vsemayki.ru (accessed: 30 10 2022)
6 PrintBar URL: https://printbar.ru (accessed: 30 10 2022)
7 L'Etoile . URL: https://www.letu.ru (accessed: 30 10 2022)
8 M-Video . URL: https://www . mvideo . ru (accessed: 30 10 2022)
the relative importance of different tools for consumers [Franke, Keinz, Steger, 2009; Hunt, Radford, Evans, 2013; Culley, Mad-havan, 2013; Chu, Lee, Kim, 2019; Schreiner, Rese, Baier, 2019]. Hence, there are several questions left unanswered by extant research: do consumers need an individualized CX when buying online; do consumers prefer some forms of CX individualization to the others?
Understanding consumer preferences for CX individualization tools is imperative in the time of economic turbulence when many retailers are cutting down investments in new technologies to address more urgent consumer demands for lower prices [Shchuren-kov, Andrianova, 2022; Retail & Loyalty, 2022] . Distinguishing must-have CX indi-vidualization tools from those that are less desirable by customers allows pursuing a more focused strategy and allocating scarce recourse to top priorities. Besides, despite the growing uncertainty and economic hardship, there is a drastic increase in Russian online commerce in 2022, followed by the changes in the market landscape and the withdrawal of foreign products [SberPro, 2022] . With these changes and possible consumer confusion, retailers need to ensure the support of and communication with customers for their further retention . In a long-term perspective online retailers can only gain a competitive advantage by improving holistic CX though a more individualized approach rather than lowering prices . Though some retailers decelerate the deployment of CX individualization technologies in 2022, the general trend toward CX individualiza-tion is expected to sustain in the future [Retail & Loyalty, 2022] .
The aim of the current paper is to evaluate the consumer need for different forms of CX individualization in the Russian market and examine how it relates to several theoretically sound and managerially useful consumer characteristics The paper seeks to contribute to the extant literature on CX management and consumer behavior in several ways Firstly, we consider three CX in-
dividualization strategies that retailers may use and explore the consumer need for different forms of CX individualization in the context of Russian online retail industry . Secondly, we examine how five consumer characteristics (namely, hedonic shopping orientation, comparison shopping proneness, consumer confusion proneness, privacy concerns, and awareness of privacy control) influence the need for different forms of CX individualization All five consumer characteristics are related to consumer traits and dispositions that are continually being reinforced by the recent changes in the marketplace Hence, the results of the study shed light on CX individualization strategies that are demanded by consumers in the light of market evolution
The structure of the paper is as follows In the first section, the notion of an individualized CX is introduced and three technology-enabled CX individualization strategies are considered: content personalization, product customization and interaction hu-manization In the second section, we review existing literature on consumer behavior to formulate hypotheses about the relationship between different consumer characteristics and the need for CX individualization. In the third section, we describe how we gather and analyze data to test the hypotheses In the fourth section, we present the results of hypothesis testing on a representative sample of 586 Russian online shoppers . In the fifth section, there is a discussion of findings in the context of marketing and CX management
THEORETICAL BACKGROUND CX & CX individualization
CX has been the subject of research since the early 1980s (see [Kranzbuhler et al ., 2018; Kumar et al ., 2022] for the temporal development of CX concept) . The introduction of the concept is associated with the work of [Holbrook, Hirschman, 1982] wherein
they define CX as "a primarily subjective state of consciousness with a variety of symbolic meanings, hedonic responses, and esthetic criteria" [Holbrook, Hirschman, 1982, p 132] Initial conceptualizations tended to define CX broadly as dependent upon "any stimuli during the entire consumption process, potentially involving many firms, customers, and stakeholders, all of which can contribute to the CX but are not necessarily under the firm's control" [Becker, Jaak-kola, 2020, p . 637] . Later studies adopted a more pragmatic view and narrowed the focus of CX to "a customer's cognitive, emotional, behavioral, sensorial, and social responses to a firm's offerings during the customer's entire purchase journey" [Lemon, Ver-hoef, 2016, p . 71] . Despite the fragmentation of the CX literature over time, researchers agreed upon a set of fundamental premises regarding CX summarized in [Becker, Jaak-kola, 2020] . Firstly, CX is subjective and depends on customer, situational, and so-ciocultural contingencies . Secondly, CX is dynamic and formed along the customer journey that comprises a series of touch-points across the stages before, during, and after service provision Thirdly, CX depends upon touchpoints within and outside firm control
At noted in [Lemon, Verhoef, 2016, p . 69], "the increasing focus on CX arises because customers now interact with firms through myriad touch points in multiple channels and media, resulting in more complex customer journeys". Good CX stand on three key pillars: customer effectiveness in accomplishing their goals during customer-firm interactions, ease in accomplishing their goals, and positive emotions provoked during these interactions [Temkin, 2014]. Studies demonstrated that poor CX leads to customer churn, lowers trust, decreases customer spendings, and eventually worsens firm financial performance [Klink, Zhang, Athaide, 2020; Feng et al., 2021; Srivastava, Kaul, 2016] . Acknowledging the importance of good CX for business performance, practitioners and academics started to extensively explore
the means to increase the effectiveness, ease, and enjoyment of customer journeys . The focus of research endeavors has been gradually shifting from employee-customer interactions and simple digital solutions (such as targeted mails) in 1980-1990s to advanced technology-enabled solutions (such as augmented reality (AR), virtual reality (VR), Internet of Things (IoT)) in 2010-2020s [Kranzbuhler et al., 2018, Kumar et al., 2022] . The most recent studies almost unconditionally claim that the future of CX management in Russia and abroad stands on CX individualization technologies powered by AI [Gogua, Smirnova, 2020; Dolganova, 2021; Hoyer et al . , 2020] .
We conceptualize CX individualization as a set of business practices that imply the adaptation of products, offers, and service encounters to an individual customer's preferences, needs, and consumption situations aimed to make customer-business interactions more effective, effortless, and emotionally engaging across the whole customer journey [Aksoy et al., 2021; Temkin, 2014; Puc-cinelli et al , 2009] Hence, CX individuali-zation represents firm efforts to make CX more individualized, and the product of those efforts is an individualized CX . Business may try to individualize CX through two ways: deploying time-consuming manual operations based on human labor or utilizing the power of modern technologies that can deliver an individualized CX on a mass scale . The focus of this study is on three technology-enabled CX individualization strategies: content personalization, product customization, and interaction humanization . While extant literature frequently refers to individualiza-tion, personalization, customization and hu-manization as interchangeable terms [Aksoy et al., 2021; Van Doorn, Hoekstra, 2013], we explicitly differentiate them
CX individualization strategies
Content personalization. Content personalization can be defined as the "specialized flow of communication that sends different
recipients distinct messages tailored to their individual preferences or characteristics" [White et al . , 2008, p . 40] . Content personalization implies that firms use personal consumer information in the interactions and transactions with customers to individualize CX and enhance marketing effectiveness [Aksoy et al . , 2021] . This information includes demographics, psycho-graphics, purchase histories, preferences, attitudes, behaviours, social environments, and consumption situations of individuals [Bellman et al . , 2006] . The use of content personalization relies on the idea of consumer inclination towards a more personal communication . When consumers receive irrelevant information, they experience reactance and try to skip it [White et al 2008] . Content personalization, in turn, helps increase communication relevance and stimulates acceptance
Product customization. From a business perspective, product customization is defined as the usage of flexible processes and organizational structures to offer products and services tailored to customers' needs but at costs that are almost the same as that of standardized production and mass marketing [Wind, Rangaswamy, 2001] . For consumers, product customization provides opportunities to introduce changes to a product they want to buy [Hunt, Radford, Evans, 2013] . Unlike personalization that implies a passive reception of content tailored to individual preferences, customization implies that the customer proactively specifies one or more elements of the product [Arora et al , 2008] Customers prefer customized products more than standard ones as they deliver a higher preference fit [Franke et al ., 2009] . At the same time, consumers may experience fatigue while constructing an ideal product from a set of available components provided by an online retailer [Dellaert, Stremersch, 2005] . Hence, finding a balance between the value of a customized product and the complexity of a customization process is one of the success factors for companies that use a
customization strategy [Dellaert, Strem-ersch, 2005] .
Interaction humanization. Live interactions with a company representative (salespeople or customer support personnel) have been the key element of customer-firm interactions for a long time There are always people who appreciate the phone calls to the company for advice or clarifications on their orders to standardized impersonal communications. With digital transformation, human interactions have been partially replaced by automated recommendation systems and customer support chatbots that try to handle problems previously addressed by real people Technology-enabled interaction humanization refers to "the use of human-like mechanisms as the mode of communication, and these can include mimics, gestures, voice, or even emotional reactions" [Aksoy et al . , 2021, p . 11] . Interaction hu-manization implies that a firm empatheti-cally react to customer inquiries, which can be realized through the combination of human and digital solutions that allow understanding the emotions of potential and real customers [Kulikova, Suvorova, 2022; Liu-Thompkins et al ., 2022] . One of the most popular technology-enabled ways to humanize customer-firm interaction is through cognitive and empathetic chatbots [Kozoriz, 2019; Behera, Bala, Ray, 2021; Agarwal, Maiya, Aggarwal, 2021] .
All three above CX individualization strategies serve the same purpose but rely on different implementation principles . The comparison of the three strategies is provided in Table 1 All three CX individualization strategies are used by online retailers in the Russian market Moreover, Russian practitioners and academic researchers repeatedly emphasize the importance of an individualized approach to managing customer-firm relationships [Kulikova, Suvorova, 2022; Nechaeva, 2021; Stebluyk, 2018; Mirogorodskaya, Ivanchenko, 2021] . Surprisingly, there is no studies that examine how Russian consumers perceive CX individualization practices, which is indicative of a research gap in existing
Table l
Comparison of CX individualization strategies
Comparison criteria CX individualization strategy
Content personalization Product customization Interaction humanization
Object of individualization Information that the customer receives from a firm Product or core service that the customer buys Service support
Source of interaction initiation Firm-initiated interaction Customer-initiated interaction Customer- and firm-initiated
Stages of customer journey Pre-purchase, Purchase, After-purchase Purchase Pre-purchase, Purchase, After-purchase
Form of customer information sharing Passive (after receiving an initial permission) Active Active
Degree of customer participation Low High Medium
Economy of scale High Low Medium
Cases in the Russian market Ozon1, Wildberries2, AliExpress3, and Yandex Market4 offer personalized recommendations that can be marked "Customers like you also buy" VseMayki5 and PrintBar6 provide product customization by allowing customers create their own designs of cloths on the web L'Etoile7 and M-Video8 use human-like chatbot-assis-tants Lola and Alyona to help customers navigate through the most frequent issues like returns and delivery options
Notes: 1 Ozon . URL: https://www.ozon .ru (accessed: 30 . 10 . 2022); 2 Wildberries . URL: https://www. wildberries .ru (accessed: 30 . 10 . 2022); 3 AliExpress . URL: https://aliexpress .ru/?gatewayAdapt=glo2rus (accessed: 30 . 10 . 2022); 4 Yandex Market . URL: https://market . yandex, ru (accessed: 30.10 .2022); 5 VseMayki. URL: https://www.vsemayki. ru (accessed: 30 . 10 . 2022); 6 PrintBar. URL: https://printbar.ru (accessed: 30 . 10 . 2022); 7 L'Etoile . URL: https://www . letu . ru (accessed: 30 . 10 . 2022); 8 M-Video . URL: https://www . mvideo . ru (accessed: 30 10 2022)
literature9 . We try to fill this void by em-
9 To identify articles that address the issue of CX individualization in the Russian context, we conducted a keyword search using the eLibrary database on October 9, 2022 Initially, we selected the following search queries in the paper titles, abstracts, and keywords: (customer experience) or (customer journey) or (klientskij opyt) (in Russian) or (klientskij put') (in Russian) or (potrebitel'skij opyt) (in Russian) . It produced 15321 articles . Further, we searched the following keywords among the previously found articles: personalizaciya (in Russain) or personalization or personalisation or kastomizaciya (in Russian) or customization or
pirically evaluating the Russian consumers' need for CX individualization
customisation or gumanizaciya (in Russian) or humanization or humanization. It narrowed the results to 92 articles . Finally, we manually screened out articles that are not related to either marketing or the Russian context, yielding a total of 72 articles . The analysis of the selected articles' texts demonstrated that none of the studies empirically examined the attitude of Russian consumers toward CX individualization practices .
HYPOTHESES DEVELOPMENT
Consumer need
for CX individualization
The fundamental premise that underlies this study is that customers differ in their need for an individualized CX [Schweidel et al 2022; De Groot, 2022] . Though, an individualized CX is associated with more positive cognitive, emotional, behavioral, sensorial, and social responses, it does not unconditionally translate into an improved CX One of the possible reasons why there is a gap between CX individualization and CX improvement is that not all consumers want an individualized experience [Nunes, Kambil, 2001] . We define the need for CX individualization as an individual consumer's inclination to value and attribute importance to CX individualization tools used in online retail Consumers differ in the need for CX indi-vidualization In turn, consumer differences in the need for an individualized CX may be dependent upon a variety of other individual characteristics
The influence of consumer characteristics on need for CX individualization
Consumer characteristics are "dimensions of individual differences in people's tendencies to show consistent patterns of thought, feelings, and behaviors in their role as consumers" [Steenkamp, Maydeu-Olivares, 2015, p 288] Though being relatively stable, they may slowly change over time [Steenkamp, Maydeu-Olivares, 2015] . When detected early, these changes can foreshadow a dramatic market shifts and open new opportunities for firms Many consumer characteristics are systematically monitored by market research firms that try to detect emerging market trends Understanding consumer characteristics that relate to the need for an individualized CX allows predicting the shifts in consumer preferences and proactively invest in optimal CX individualization technologies
While the list of consumer characteristics is tremendous, we have selected those that comply with two criteria. Firstly, they are theoretically justifiable, i e their effects on the need for CX individualization can be deduced from existing theories and logical reasoning . Secondly, they are managerially useful, i e they can be sensed by companies through market research, so that business can use them as the predictors of changes in the consumer need for CX individualiza-tion Besides, any CX individualization strategy incurs both additional benefits and risks for consumers [Aguirre et al., 2016; Stry-charz et al. , 2019] . Therefore, we focus on five consumers characteristics that fit into the two-fold nature of CX individualization Some of them are expected to make consumers more appreciative of benefits, while the others — more vigilant about risks .
Hedonic shopping orientation. Hedonic shopping orientation refers to "the enjoyment of shopping as a leisure-based activity" for its own sake [Rohm, Swaminathan, 2004, p . 752] . Pandemic and self-isolation increased the inclination of the customers to consider online shopping as a means of general entertainment, which does not necessarily lead to the purchase [New Retail, 2021] . Hedon-ically oriented shoppers appreciate CX indi-vidualization, as individualized information, products and interactions are associated with a higher entertainment value and a more interactive shopping process . Hence:
H1. Consumers' hedonic shopping orientation is associated with a higher need for an individualized CX.
Comparison shopping proneness. Comparison shopping proneness is an individual trait that describes consumers' general propensity to actively search for better deals (e g , price promotions) [Biraglia et al , 2022] CX individualization could offer comparison shoppers a possibility to find the best deals based on the products they bought without spending too much time searching; product customization makes the customer buy exactly what they want for the price they pay, and through humanized interactions with
frontline employees comparison shoppers can clarify the details of the deal . Hence:
H2. Consumers' comparison shopping proneness is associated with a higher need for an individualized CX.
Consumer confusion proneness. Consumer confusion proneness is defined as consumers' general tendency to experience the state of confusion characterized by anxiety, frustration, lack of understanding and indecision that may arise from processing similar, too much or ambiguous information [Walsh, Mitchell, 2010] . Consumer confusion is associated not only with information overload caused by the abundance of offerings in the shops, but also lack of insight into personal preferences that make consumers unaware of what they really need and want [Franke, Keinz, Steger, 2009] . CX individualization narrows down the scope of information, products and interactions that the customer has to interact with to a set of the most relevant options, thus making the process simpler and decreasing the likelihood of confusion . Therefore, CX individualization can help the confused and overloaded customers to make the choice Hence:
H3. Consumers' confusion proneness is associated with a higher need for an individualized CX.
Privacy Concerns. Privacy concerns are consumer beliefs about opportunistic behavior of firms in relation to consumers' personal information submitted over the Internet [Dinev, Hart, 2006] . Consumers with a higher level of privacy concerns are less willing to share personal information in the online setting and perceive personalization offerings to be of less value than consumers with a lower level of privacy concerns [Awad, Krishnan, 2006] Privacy concerns make consumers more avoidant of marketing communications [Mpinganjira, Maduku, 2019] . Moreover, "the more targeted the online ads are, the more consumers view them as annoying and as a violation of privacy" [Seyedg-horban, Tahernejad, Matanda, 2016, p . 121] . CX individualization requires customers to share personal (including sensitive) informa-
tion, thus making the process riskier for such customers Moreover, CX individualiza-tion implies that firms more actively interact with customers, therefore privacy concerned consumers can perceive it as intrusive . Hence:
H4. Consumers' privacy concerns are associated with a lower need for an individualized CX.
Awareness of Privacy Control. With the emergence of the technological advancement, the content conveyed to the customers is based on the predictions of the customers' behavioral patterns, however, now it becomes clear that the controlling power over what the customer can see while shopping should be given to the customer . Awareness of privacy control is defined as consumer objective understanding of their opportunities to control the data that is used by retailers for selling and advertising purposes [Ham, Nelson, 2016] . Awareness of privacy control would decrease the perceived intrusiveness of personalized content and make individuals more likely to accept CX individualization [De Groot, 2022] . Hence:
H5. Consumers' awareness of privacy control associated with a higher need for an individualized CX.
METHODOLOGY Sample
The study is based on an online survey of 586 respondents recruited from an online panel of a marketing research company in June 2021 . Replicating A . Parasuraman, V . A . Zeithaml, and A. Malhotra's criteria [Parasuraman, Zeithaml, Malhotra, 2005], we surveyed purchasers who had sufficient online shopping experience and made at least one purchase last month. Only consumers who are 18-55 years old and live in cities with 100.000 inhabitants or more were surveyed . We followed a quota sampling approach, with the intention of reproducing the sociodemographic profile of the popula-
tion of Russian consumers in terms of gender, age, and region of residence . Age and gender quotas correspond to age and gender structure of Russian shoppers in 2019-2020 as identified in the research by Yandex [Naz-arova, 2021] . Consumers of 18-55 y . o . comprise about 90 % of all online shoppers in Russia, so the results can be generalized to almost the entire population of Russian online shoppers
The description of the sample is provided in Table 2. We measured a consumer's financial condition as a financial cushion that
shows a period of time during which a consumer will have enough money savings to maintain living standards if the household stops receiving income Such an approach to measuring consumers' available financial resources is used in the studies of financial behavior (for example, [NAFI, 2022]), as it better reflects the real solvency of consumers in the time of market turbulence than the measurement of nominal income. This indicator absorbs information on changing market prices and possible income fluctuations
Table 2
Sample description
Characteristic Option Option share, % Characteristic Option Option share, %
Sex Male 49 Region of residence Central 36
Female 51 North-West 12
Age 18-24 y. o . 12 South 9
25-34 y. o . 30 Ural 6
35-44 y. o . 31 Far East 3
45-55 y . o . 27 Siberian 11
Marital status Married / In a partnership 68 Privolzhskiy 22
Not married / widow(-er) 32 North Caucasian 1
Education Secondary 5 Financial cushion (For what period would you have enough money savings if you and your family members stop receiving income?) Less than a month 24
Specialized secondary 24 1-3 months 40
Incomplete higher 8 Up to 6 months 18
Higher (bachelor or specialty) 45 Up to 1 year 9
More than a year 9
Higher (master) 16 Shopping frequency At least once a week 26
Higher (doctoral) 2 2-3 times a month 52
Once a month 22
Operationalization of variables
Hedonic shopping orientation, comparison shopping proneness, consumer confusion proneness, privacy concerns and awareness of privacy control were measured using existing scales adapted to the online shopping context A five-point Likert scale was used to measure all latent constructs . The scales were purified according to the procedures described by [Anderson, Gerbing, 1988] in order to develop valid and reliable measures The final set of items used to measure each variable is presented in Table 3
Need for content personalization, need for product customization, and need for interaction humanization were measured using single items . Considering that the study is exploratory and does not imply the usage of structural equation modeling and the constructs are quite concrete, the use of single-item measures instead of multi-item scales is acceptable [Diamantopoulos et al ., 2012; Petrescu, 2013] . For each of three constructs, respondents were asked to evaluate the importance of e-commerce options related to each of three strategies on a 5-point scale ranging from 1 = Unimportant to 5 = Critically important . Need for content personalization was measured as the importance of getting personalized recommendations and offers on e-commerce websites; need for product customization corresponded to the importance of making changes to the standard product parameters (color, material, etc .) when shopping; and need for interaction hu-manization corresponded to the importance of consulting with an online store's representative when shopping . All three scales were new and developed for the purpose of the current study
Analytical procedure
Firstly, we ran a two-step clustering procedure on three variables representing consumer needs for content personalization, product customization, and interaction hu-manization [Punj, Stewart, 1983] . This pro-
cedure aimed to identify consumer groups that differ in both the magnitude of desire for individualized experience and the structure of preferences for different CX individualization strategies (namely, content personalization, product customization, and interaction humanization) In the first step, we used Ward's hierarchical clustering with squared Euclidean distance to determine the appropriate number of clusters; this method can generate homogeneous clusters of relatively equal size [Hair et al . , 2010] . We determined the number of clusters using the Ward dendrogram, which showed a large decrease in a relevant distance measure when moving from a four- to a five-cluster solution, while further division of observations into 6 clusters produced a much smaller decrease in the distance measure . Thus, a five-cluster solution seemed most appropriate for our data . In the second step, we used a k-means clustering procedure [MacQueen, 1967] to develop a five-cluster solution The group centroids computed in the hierarchical procedure were specified as initial clusters for the fe-means clustering. The resulted clusters were interpreted, named, and profiled on a set of additional variables related to socio-demographics and shopping behavior
Secondly, we ran a confirmatory factor analysis to refine the latent variables used to predict consumer needs for CX individ-ualization . The resulting goodness of fit suggested that the model adequately represented the data (Chi square/df = 1. 94 (p-value = 0 .000); CFI = 0 . 985, TLI=0. 980, RMSEA = 0.040 (pclose = 0.944), SRMR=0 . 051) . All latent constructs were tested for reliability and validity [Anderson, Gerbing, 1988] . The composite reliability of all constructs after purification was greater than or close to 0 .7 . Convergent validity was ensured by using the following criteria: average variance extracted (AVE > 0 . 5), scale composite reliability (CR > 0 . 7), and the item factor loadings over 0 . 6 [Bagozzi, Yi, 2012] (see Table 3) . The results of the convergent and discriminant validity checks [Fornell, Larcker, 1981], as
Table 3
Consumer characteristics: measurement scales
Latent construct / Source Item Factor loading CR AVE
Hedonic shopping orientation / [Büttner, Florack, Göritz, 2014] - I enjoy online shopping 0 . 78 0 . 86 0 . 67
- When I shop online, I usually look for entertainment 0 . 77
- I like to kill time by shopping online 0 . 90
Comparison shopping proneness / [Anglin, Stuenkel, Lepisto, 1994] - I search for coupons and promo codes before buying something online 0 . 74 0 . 80 0 . 57
- I browse websites that aggregate information about discounts and special offers 0 . 84
- I use websites and applications that allow comparing prices and characteristics of products 0 . 69
Consumer confusion proneness / [Sprotles, Kendall, 1986] - In online stores, there are so many brands that I feel confused sometimes 0 . 85 0 .78 0 . 64
- Sometimes it is difficult to decide where to shop because of the abundance of online stores 0 . 74
Privacy concerns / [Smith, Burke, Milberg, 1996; Dinev, Hart 2006] - I am concerned about providing personal information to online companies, because it could be used in a way I could not foresee 0 . 87 0 . 94 0 . 81
- I am concerned that the information I submit to online companies could be misused 0 . 92
- I'm concerned what others might do with information I leave on the internet 0 . 90
- I am concerned about threats to my personal privacy today 0 . 91
Awareness of privacy control / [Ham, Nelson, 2016] - Internet users can opt out of being tracked by advertisers online 0 . 68 0 .72 0 . 57
- Internet users can choose what data about their online activity can and cannot be used by companies for advertising purposes 0 . 82
well as descriptive statistics and correlations between the constructs, are presented in Tables 3 and 4
Finally, we ran a multinomial logistic regression to define whether hedonic shopping orientation, comparison shopping proneness, consumer confusion proneness, privacy concerns, and awareness of privacy
control can predict consumer membership in one of the clusters . We have additionally included age, sex, and the size of financial cushion as control variables . Age is a continuous variable . Sex is a dummy variable with two categories (female = 0; male = 1) . The size of financial cushion includes five levels (see Table 2)
Table 4
Descriptive statistics and correlations
Construct Mean 1 2 3 4 5 6 7 8
1 Need for content personalization 3 . 5 (0 .9) 1 0 .18 0 .17 0 .13 0 . 12 0 . 07 0 . 01 0 .10
2 Need for product customization 3 .7 (0 .8) 0 . 42** 1 0 .17 0 . 04 0 . 07 0 . 02 0 . 02 0 . 03
3 Need for interaction humanization 3 . 5 (1 -0) 0 . 41** 0 . 42** 1 0 . 05 0 . 06 0 . 07 0 . 04 0 . 06
4 Hedonic shopping orientation 3 . 7 (0 .9) 0 . 36** 0 . 21** 0 . 21** 1 0 . 09 0 . 03 0 . 00 0 . 08
5 Comparison shopping 3 . 6 (0 .9) 0 . 34** 0 . 26** 0 . 24** 0 . 29** 1 0 . 04 0 . 02 0 . 07
6 Consumer confusion 3 . 3 (1 • 0) 0 . 27** 0 .14** 0 . 26** 0 . 17** 0 . 21** 1 0 . 03 0 . 05
7 Privacy concerns 4 . 0 (0 . 8) 0 .11** 0 .15** 0 .19** 0 . 03 0 . 15** 0 .18** 1 0 . 00
8 Awareness of privacy control 3 . 2 (0 . 9) 0 . 32** 0 .18** 0 . 24** 0 . 28** 0 . 26** 0 . 22** 0 . 00 1
Notes: standard deviations are in parentheses; Pearson correlations below the diagonal; squared correlations above the diagonal; ** —correlation is significant at the 0 . 01 level (2-tailed) .
RESULTS Cluster profiles
A two-step clustering procedure resulted in five clusters that differ in the magnitude and structure of needs for CX individualization. Figure summarizes the cluster titles and sizes
Each cluster has a distinctive profile of preferences in relation to CX individualization (see Table 5):
• Avoiders (10 % of the sample) represent the lowest scores on all three needs for
Figure. Clusters based on the need for CX individualization Note: percentages stand for cluster shares
Table 5
Cluster profiles: need for different types of CX individualization (means)
Comparison criterion Avoiders Social customizers Omnichannel searchers Digital customizers Individualization cravers
Need for content personalization 2 . 3 1 . 8 3 . 4 3 . 6 4 . 0
Need for product customization 2 . 3 3 . 8 2 . 9 4 . 0 4 . 2
Need for interaction humanization 1 . 9 3 . 3 3 . 5 2 . 6 4 . 3
Total need for CX individualization 2 . 1 3 . 0 3 . 3 3 . 4 4 . 2
CX individualization, meaning they do not want any form of an individualized CX
• Social customizers (8 % of the sample) are the smallest identified group. They need customized products and humanized social interactions and demonstrate almost no interest in personalized digital content They prefer putting some effort into upgrading the imperfect product with the help of store representatives than trying to process personalized information on alternative products that are present in the market
• Omnichannel searchers (17 %) demonstrate higher needs for personalized content and humanized interaction but attribute less importance to product customization . Presumably, they use individualized information received through passive or interactive channels to better navigate in the existing product assortment and find better matches rather that putting efforts into customizing products
• Digital customizers (22 %) are more focused on product customization and content personalization, not being much interested in human or human-like interactions during the shopping process
• Individualization cravers (43 %) are the largest group, in contrast to avoiders these customers demonstrate higher need for all three forms of CX individualization
Having a closer look at the shopping habits, we see that the number of online stores visited last year increases together with the increase in the need for CX individualization, whereas the average number of categories varies across clusters with the lowest score for Social customizers and the highest one for Individualization cravers (see Table 6) . At the same time there is a clear difference between the clusters in the relationship with the favorite online store: Individualization cravers are more engaged in various collaboration options that are offered by their favorite store, Social customizers are mostly interested in interactive options (e .g., earning points), and Omnichannel searchers benefit from participation in the loyalty programs (see Table 7) . Additionally, loyalty and WOM intentions increase as the need for CX individualization grows
Multinomial logit results
At the next step we perform a logit regression to identify the drivers of each of the cluster group (see Table 8) . Avoiders serve as the reference category It allows comparing how each of the four clusters with different magnitude and structure of needs for CX individualization (Social customizers, Omnichannel searchers, Digital customizers, and Individualization cravers) differ from Avoiders with the lowest need for CX indi-
Table 6
Share of cluster members who buy products/services online, %
Product/service category Avoiders Social customizers Omnichannel searchers Digital customizers Individualization cravers
Apparel 71 73 68 73 76
Food products 44 43 50 55 51
Ready meals from cafes/ restaurants 53 55 59 49 63
Cosmetics 59 51 45 44 56
Consumer electronics and appliances 68 67 54 68 67
Home and repair goods (furniture, interior items) 61 53 40 52 51
Books and office supplies 46 33 39 36 35
Tickets (travel) 49 41 45 49 56
Tickets (theatre, concerts, etc ) 32 35 34 39 40
Hotel booking 31 20 33 25 34
Pet products 47 24 32 35 41
Goods for kids and mom's 36 24 34 31 34
Accessories 27 27 34 35 40
Applications (software (e g , Microsoft Office), plays) 19 22 22 25 31
Car goods, spare parts 27 31 30 24 38
Medications and medical products 61 49 54 55 54
Other 2 8 5 3 2
Number of categories bought online (mean) 7.3 6.6 6.8 7.0 7.7
Number of online stores visited last year (mean) 6.3 6.4 7.0 8.8 10.1
vidualization . As we can see from the statistical outcome, for Social customizers and Omnichannel searchers consumer confusion proneness plays the leading role (B = 0 . 38 and B = 0 . 40 respectively) . Digital customizers demonstrate higher levels of comparison shopping proneness (B = 0 . 48) and awareness of privacy control (B = 0 . 43) . In case of Individualization cravers all five factors are
significant, including hedonic shopping orientation (B = 0.58) and privacy concerns (B = 0.48) that have no effect in case of other clusters An interesting observation is that Social customizers are more often male customers (B = 0 86)
The results of the study allow confirming four out of five hypotheses . As expected, hedonic shopping orientation, comparison
Table 7
Cluster profiles: relationship with the favorite online store
Comparison criterion Avoiders, % Social customizers, % Omnichannel searchers, % Digital customizers, % Individualization cravers, % Total, %
Usage of options in the favorite online store (proportion of cluster members)
Membership in the loyalty program 54 49 59 49 56 54
Earning points when shopping at the store 42 57 48 52 61 55
Membership in a special subscription program (e g , offering special shipping conditions) 14 14 24 27 32 26
Subscribed to the page of the store in social networks 9 14 21 22 28 23
Reading emails from the store 51 45 55 57 66 59
Participation in the store's online giveaways (e g social networks) 9 2 12 12 25 16
Quality of relationships with the favorite store (means of satisfaction, loyalty and WOM)
The service in this online store meets my expectations 4 . 3 4 . 1 4 .1 4 . 3 4 . 3 4 . 2
I will continue to use the services of this online store in the future 4 . 4 4 . 3 4 . 3 4 . 4 4 . 4 4 . 4
I often talk about this online store to others 3 . 3 3 . 1 3 . 6 3 . 6 4 . 0 3 .7
shopping proneness, consumer confusion proneness and awareness of privacy control positively affect the need for CX individualization . Contrary to hypothesis 4, privacy concerns do not decrease the need for CX individualization but, reversely, are highest
among consumers with the highest need for CX individualization .
Hedonic shopping orientation turns Avoiders into Individualization cravers who want to take advantage of all possible CX individualization strategies . This finding allows
Table 8
Multinomial logistic regression results
Independent variable Social customizers Omnichannel searchers Digital customizers Individualization cravers
B Sig. B Sig. B Sig. B Sig.
Intercept -3 .79 0 . 05 -2 . 62 0 . 10 -4 . 26 0 . 01 -8 . 57 0 . 00
Hedonic shopping orientation 0 . 05 0 . 84 0 . 09 0 . 64 0 . 23 0 . 22 0.58 0 . 00
Comparison shopping proneness -0 . 09 0 .72 0 . 33 0 . 12 0.48 0 . 02 0.68 0 . 00
Consumer confusion proneness 0.38 0 . 08 0.40 0 . 04 0 . 31 0 . 08 0.65 0 . 00
Privacy concerns 0 . 40 0 .11 0 . 00 0 . 99 0 . 09 0 . 64 0.48 0 . 01
Awareness of privacy control 0 . 09 0 . 71 0 . 35 0 . 08 0.43 0 . 03 0.62 0 . 00
Age -0 . 01 0 . 80 -0 . 01 0 . 67 -0 . 01 0 . 43 -0 . 02 0 . 28
Sex (male) 0.86 0 . 05 0 . 21 0 . 56 0 . 21 0 . 55 0 . 48 0 .15
Financial cushion
Less than a month 0 . 38 0 . 65 -0 . 36 0 . 56 0 . 51 0 . 43 0 . 00 1 . 00
1-3 months 0 . 67 0 . 40 -0 . 24 0 . 68 0 . 59 0 . 34 0 .13 0 . 82
Up to 6 months 0 . 05 0 . 96 -0 . 63 0 . 32 0 .12 0 . 85 -0 . 25 0 . 69
Up to 1 year 1 . 67 0 . 10 0 . 21 0 . 81 1 . 24 0 .16 1 . 39 0 .10
More than a year Omitted Omitted Omitted Omitted
Notes: reference category — Avoiders; coefficients significant at p< 0 . 05 are bold; coefficients significant at p< 0 . 10 are bold and italicized; McFadden Pseudo R2 = 0 .092; LR chi2 = 155 . 157, df = 44, p< 0 . 001; -2Log likelihood = 1526 . 883; classification accuracy = 44 . 5 %.
explaining the pattern observed in the Russian market during the COVID-19 lockdown . Deprived of many entertainment activities, consumers were increasingly using online shopping as a source of hedonic pleasures [New Retail, 2021], and online retailers responded by increasing the level of CX indi-vidualization
Some consumer characteristics drive consumer preferences for particular forms of CX individualization rather than CX indi-vidualization in general Consumer confusion stimulates the need for humanized interaction Supposedly, when feeling puzzled and
overwhelmed by information, consumers seek for a more empathetic interaction that can mitigate anxiety and decrease decision-making complexity . Higher comparison-shopping proneness accelerates the need for content personalization . This finding is particularly important under the current conditions of market turbulence characterized by shrinking financial resources of Russian consumers . A stronger desire to get a better deal can make consumers compare products and prices in different stores more actively The response of an online retailer to such a behavior is to provide a personalized offer to
those switching between different online shops
Awareness of privacy control is important in explaining the need for CX individualiza-tion, but mostly for those consumers who exhibit a relatively higher preference for content personalization Given that content personalization is the most data sensitive strategy, online retailers should ensure data exchange transparency and provide customers with the feeling of control over their private information
Privacy concerns are the highest among Individualization cravers who are ready to engage in all forms of CX individualization It's likely that consumers are indifferent to the practices of information sharing up to a point but get worried of their privacy when get deeply involved into variety of individualization practices . Additionally, Individualization savers' privacy concerns are balanced by their high awareness of privacy control
DISCUSSION AND CONCLUSION
The current study explores the role of CX individualization through the lens of various individual consumer characteristics . Our approach continues existing research on CX management that evolutionary developed since 1980s . Following the evolutionary development of the field, the study focuses on three CX individualization strategies — content personalization, product customization and interaction humanization — that increasingly rely on modern technologies as a part of CX management
Addressing the agenda of CX individualization requires from a firm a thorough understanding of consumer needs and expectations in relation to CX individualization tools, as well as their determinants . Results of the current study, thus, contribute to existing literature, firstly, by highlighting the multifaceted nature of an individual CX and demonstrating the heterogeneity of consumers' needs and expectations; secondly, by
exploring what drives this heterogeneity, and thirdly, by suggesting how firms can take into account the specifics of heterogeneous consumer groups in their long-term strategies
Firstly, this study highlights the multi-faceted nature of both opportunities to create an individualized CX and variability in potential consumers' preferences and needs from CX The combination of three CX indi-vidualization strategies reflects the choices on the side of a firm — how to address and improve CX . Additionally, the results demonstrate substantial differences between the five groups of consumers with very distinctive characteristics. The most active and demanding group of Individualization crav-ers is the largest one . Together with the second most active group of Digital custom-izers, they constitute more than half of the sample, which reflects a high demand for all CX individualization strategies among Russian consumers Hence, CX individualization is not an optional, but strongly demanded strategy for almost half of the respondents in the sample Still, the nature of the need for CX individualization is yet to be further explored: there is strong difference between the opposed groups of Individualization crav-ers and Avoiders, however all other groups are somewhere between a strong desire for and avoidance of CX individualization tools The results demonstrate that each of three CX individualization strategies has potential to be attractive, but only their combination covers most consumers
Secondly, we acknowledge that there is a multitude of antecedents which might foster the need for an individualized CX or constrain consumer readiness to be engaged, provide own data and, in the end, develop closer relationships with online retailers. On the one hand the antecedents we consider reflect different consumer approaches to online shopping (hedonic shopping orientation, comparison shopping proneness, consumer confusion proneness) that can make consumers differently evaluate the benefits of CX in-dividualization On the other hand, we include
antecedents that reflect consumer stance toward information sharing practices (privacy concerns, awareness of privacy control) that can influence how consumers perceive possible risks associated with CX individu-alization. The results demonstrate that all the antecedents do work in predicting the level and type of the need for CX individu-alization, but the role of them varies for the identified groups of consumers
Our further analysis of the role of selected antecedents confirmed the distinctive position of the largest cluster of Individu-alization cravers as all of antecedents matter for the cluster members and help identify their strong need for CX individualiza-tion. The profiles of other clusters reflect selective role of antecedents, which we can offer for further investigation . However, these results confirm the differences between the clusters not only through their behavior and attitude to CX individualization, but also through the driving forces behind their choices
Thirdly, we imply that the multifaceted nature of both the need for CX individuali-zation and technological opportunities force firms to make choices . But when making these choices, firms need to be aware of long-term outcomes such as consumer commitment and loyalty These outcomes will depend on the match between consumer needs for different CX individualization strategies and firm's responses .
These results mean a lot for practitioners Though being quite similar in terms of what they buy, five consumer clusters behave differently in terms of where they buy (see Table 6) The growing need for CX individ-ualization makes consumers expand the numbers of stores they visit with Individualiza-tion cravers having the largest repertoire of stores and Avoiders — the smallest . Surprisingly, all consumer clusters are equally sat-
isfied with and behaviorally loyal to their favorite store but willingness to recommend gradually increases as the need for CX in-dividualization increases (see Table 7) . Given that WOM is one of the major indicators of good CX, this means retailers should particularly focus on those segments that are ready to spread positive WOM (in particular, Individualization cravers). Coincidently, these segments are the most active in terms of store switching and the most demanding in terms of the expected CX individualization level Thus, investing into CX individualiza-tion is a must-be strategy for those firms who want to engage customers and be leaders in the market in the long term
The current study revealed that Russian consumers are already deeply embedded in the CX individualization The trend towards CX individualization is visible in the recent events for practitioners (e g , [Client Service Forum, 2022]), in online retailers' practice (e .g ., Ozon or L'Etoile), and academic research [Hoyer et al ., 2020; Liu-Thompkins et al , 2022] One can assume that this trend represents an answer to an earlier call to go away from using discounts as the only way to create value and fight for the customer facing decreasing consumer income [Nielsen, 2018] . It is not a question of whether an individualized CX matters, but rather a matter of managerial choices regarding CX in-dividualization tools that help consumers with different characteristics seamlessly move along the customer journey. The list of the consumer characteristics we select to explain the need for CX individualization is neither conclusive nor exhaustive, and future studies can expand it to get a more complete profile of the consumer who wants an individualized CX We can suggest these questions for future studies wherein quantitative analysis might be combined with in-depth interviews with consumers
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Initial Submission: June 16, 2022 Final Version Accepted: October 26, 2022
Индивидуальный подход к клиенту в онлайн-ретейле* К. С. Головачева, М. М. Гогуа, М. М. Смирнова, О. Н. Алканова
Институт «Высшая школа менеджмента», Санкт-Петербургский государственный университет, Россия
Цель исследования: определение влияния потребительских характеристик (ориентация на гедонистическое потребление, склонность к сравнительному шопингу, подверженность информационной перегрузке, обеспокоенность конфиденциальностью данных и осведомленность о контроле конфиденциальности) на потребность онлайн-покупателей в различных формах индивидуализации клиентского опыта. Методология исследования: исследование основано на репрезентативном онлайн-опросе 586 российских онлайн-покупателей, проведенном в середине 2021 г . С помощью кластерного анализа выделено несколько групп покупателей с разными предпочтениями в отношении индивидуализации клиентского опыта, а затем с применением многомерной логит-регрессии протестировано, могут ли потребительские характеристики предсказать предпочтения покупателей. Результаты исследования: эмпирически подтверждено, что потребность в индивидуализации клиентского опыта различается у разных покупателей и все пять потребительских характеристик влияют на степень выраженности этой потребности, но их роль различается для разных стратегий индивидуализации клиентского опыта . Оригинальность и вклад авторов: в статье впервые совместно исследуются три стратегии индивидуализации клиентского опыта, с помощью которых онлайн-ретейлеры могут взаимодействовать с покупателями: персонализация контента, кастомизация продукта и гуманизация взаимодействия Результаты исследования помогают понять, какие стратегии индивидуализации клиентского опыта фирмы должны учесть в своих долгосрочных планах для удовлетворения потребностей разных типов покупателей
Ключевые слова: индивидуализированный клиентский опыт, персонализация контента, ка-стомизация продукта, гуманизация взаимодействия
For citation: Golovacheva K . S ., Gogua M. M ., Smirnova M . M ., Alkanova O . N. 2022 . Treating customers as individuals in online retail . Russian Management Journal 20 (2): 224-246 . https://doi . org/10. 21638/spbu18. 2022. 204
Для цитирования: Golovacheva K . S . , Gogua M . M ., Smirnova M . M . , Alkanova O . N . 2022 . Treating customers as individuals in online retail . Российский журнал менеджмента 20 (2): 224-246 . https://doi . org/10 . 21638/spbu18. 2022. 204
Статья поступила в редакцию 6 июня 2022 г. Принята к публикации 26 октября 2022 г.
* Исследование выполнено при финансовой поддержке Санкт-Петербургского государственного университета, грант ГО 77098515.
