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The purpose of this paper is to present a multichannel segmentation approach to identifying customer segments based on actual customer channel usage in the post-purchase phase in the health insurance industry.

A multinomial regression model and count regression models were estimated to describe the profiles of customer segments and the frequency of channel usage based on generations and sociodemographic variables.

This study identified generational differences in channel usage. Single female customers from the Pre-Boomer or Baby Boomer generation and customers living in states with lower incomes are more likely to use call centres. Website users tend to live in regions with higher per capita income. Multichannel users are, on average, more frequent users of both the website and call centres. In terms of sociodemographics, they display a more heterogeneous profile.

The proposed segmentation needs to be enriched with additional variables such as customers’ health status or channel usage motivations.

Customers, who are male, married and from Generations Y and X, are more likely to use the website. Their propensity to switch to a digital channel could be investigated further to develop targeted migration strategies. Multichannel users are, on average, more frequent users of all channels. To avoid increased channel costs, segments should be analysed in terms of their size and profit potential to help allocate marketing investment more efficiently.

As opposed to existing research, the proposed segmentation approach is based on transactional data of channel usage from a real company, combined with analyses using generations and sociodemographic variables.

Digital technology is revolutionizing insurance distribution allowing the insurer companies to reach customers via multichannel. The aim of this study is to segment potential customers of life insurance based on their information search, purchasing channels and personal characteristics in the digital environment.

The study uses cross-sectional research survey. In total, 422 questionnaires were collected through a convenience sample of the Romanian population. The data was segmented based on consumer information touchpoints (online vs offline), purchase channel preference (offline by a professional vs online by a standardized platform) and personal characteristics (age, marital status and children).

The channel segmentation analysis revealed that information channel preferences are the most important clustering variables, followed by purchase channel preferences, marital status, having children and age. Four distinct segments were identified: young fully offliners (23.7%), mature fully offliners (31.5%), committed online searchers (23.2%) and cross-channel onliners (21.6%).

Insurance companies should adapt their communication and distribution strategy based on multichannel segmentation and should focus on digital touchpoints with costumers.

Firstly, the paper reveals multichannel and hybrid segmentation for life insurance. Secondly, it extends the already studied retail channels with search engines and companies' websites. Thirdly, it extends the behavioural variables for channel segmentation with technology acceptance behaviour, attitude towards life insurance, knowledge about life insurance, attitude towards personal selling and quality appraisal of online information sources.

This paper analyses how the purchase channel and customer complaint goals affect the sequential choice of post–purchase complaint channels when customers experience a service failure followed by a service recovery failure (double deviation).

An online survey involving a scenario manipulation was conducted with 577 apparel shoppers. The study employs multi-group latent class analysis to estimate latent customer segments within both online and offline groups of shoppers and compare latent classes between the two groups.

The results show that the purchase channel has a lock-in effect on the complaint channel, which is stronger for offline buyers. Moreover, there is evidence of channel synergy effects in the case of having to complain twice: shoppers who complain in store in the first attempt turn to online channels in the second complaint attempt, and vice versa. Complaint goals shape the choice of complaint channels and define different shopper segments.

The present study is the first to adopt a cross-stage approach that analyses the dependencies between the purchase channel and the complaint channel used on two subsequent occasions: the first complaint after a service failure and the second following a service recovery failure.

The purpose of this paper is to identify patterns of medium preference for loyalty programs (LPs) among members to support the case for segmenting customers based on their medium preference.

A survey of nearly 2,000 customers who are enrolled in at least one supermarket LP was employed. LP members are segmented based on a latent class clustering model and then profiled in terms of socio-demographic variables by means of a multinomial logit regression model.

Medium preference is heterogeneous and differs at the customer segment and at the LP touchpoint level. Five segments emerge which display different medium preference patterns. LP medium preference is associated with age, gender, affluency and number of different LPs the customer is enrolled in.

Retailers, e-tailers and brands can benefit from this customer segmentation when faced with the challenges of adding online features or migrating their LPs online. Marketers should differentiate their investment in online and offline LP touchpoints according to the medium preference for each LP touchpoint of the customer segments of interest.

Retailers, e-tailers and brands are today introducing online marketing strategies and tactics, such as LPs, that have been traditionally used offline. So far, however, they have failed to answer the question whether online and offline LPs and related touchpoints have the same preference among consumers. Literature on LPs has not explored customer preference for the LP medium or the consumer characteristics related to medium preference. This work is unique in providing an overview of medium preference for LPs and their touchpoints.

Consumer behavior has evolved because of technological development. Nowadays, consumers carry out the different stages of the decision-making process by combining multiple devices which has been defined as multi, cross and omnichannel behavior. These behaviors have attracted the attention of academics and become a hot topic in literature. As a result, vast amounts of studies on the subject need to be revised and clarified. Thus, the aim of this chapter is to synthetize the primary academic literature that analyzes multi, cross and omnichannel behavior from the consumer point of view. To do that, first, the main concepts (multi, cross and omnichannel) and their differences are clarified. Second, the major findings of channel mix literature regarding the topics, channel scope and theories are exposed and described. Third, the opportunities and future lines of research are presented. This chapter contributes to the literature by clarifying the conceptualization of multi, cross and omnichannel behaviors; offering a complete picture of the main topics, channel approaches and theories addressed in channel mix literature; and presenting future research opportunities and open research questions in a channel mix context that could serve as a starting point to build further research.

Despite the rapid adoption of smartphones among digital fashion consumers, their attitude to retailers' mobile apps and websites is one of increasing dissatisfaction. This suggests that understanding how mobile consumers use smartphones for fashion shopping is important in developing digital shopping platforms that fulfil consumer' expectations.

For this research, mobile eye-tracking technology was employed in order to develop unique shopping journeys for 30 consumers, using fashion retailers' websites on smartphones, documenting their differences and similarities in browsing and purchasing behaviour.

Based on scan path visualisations and observed shopping experiences, three prominent mobile shopping journeys and shopper types were identified: “directed by retailer's website”, “efficient self-selected journey” and “challenging shopper”. These prominent behaviour patterns were used to characterise mixed cluster behaviours; three distinct mixed clusters were identified, namely, “extended self-selected journey”, “challenging shoppers directed by retailer's website” and “focused challenging shopper”.

This research argues that mobile consumers can be segmented based on their activities and behaviours on the mobile website. Knowing the prominent shopping behaviour types any other complex behaviour patterns can be identified, analysed and described.

The findings of this research can be used in developing personalised shopping experiences on smartphones by feeding these shopper types into retailers' digital marketing strategy and artificial intelligence (AI) systems.

This paper contributes to consumer behaviour literature by proposing a novel mobile consumer segmentation approach based on detailed shopping journey analysis using mobile eye-tracking technology.

Consumers are increasingly combining distribution channels, thus displaying so-called omni-channel behavior, both to complete a given purchase and between purchases. The authors make a distinction between omni-channel customers, who make use of distribution services in both channels and omni-channel users, who make partial use of the distribution services of one channel to support purchases in another. This paper aims to identify the omni-channel behavior among the customers of a global fast fashion retailer dealing in a wide range of apparel and clothing accessories.

Using a multinomial logit model, the authors perform a customer segmentation based on observed omni-channel behavior, considering the explanatory roles of demographics, distribution service features and customer service policies across the different retail channels.

The authors observe that the key retail channel features for explaining omni-channel customer behavior are product accessibility, both in store and online; the assurance that goods purchased online will satisfy the customer’s needs and expectations; and the option to return goods found unsatisfactory.

The results clearly show that the nature of the visits and purchases made by customers is determined by various components of the companýs customer service policy, which can, therefore, be used to guide the retailer’s segmentation strategy.

Future lines of research should explore the economic implications of this customer segmentation. The price perception data emerging from our findings suggest a greater sensitivity to prices in the mono-channel segment, which might be worth exploring in future research.

Future lines of research should explore the economic implications of this customer segmentation. The price perception data emerging from our findings suggest a greater sensitivity to prices in the mono-channel segment which might be worth exploring in future research.

Los consumidores combinan canales de distribución en el denominado comportamiento omni-canal cada vez en mayor medida, tanto para completar una misma compra como entre distintas compras. Distinguimos entre clientes omni-canal, que hacen uso de los servicios de distribución de ambos canales, y usuarios omni-canal, que hacen solo un uso parcial de los servicios de distribución de un canal para apoyar las compras en el otro canal. En este trabajo identificamos este comportamiento omni-canal entre los clientes de una empresa global del sector de la moda que vende un amplio rango de productos de ropa y complementos.

Mediante un modelo logit multinomial, realizamos una segmentación de los clientes en base a su comportamiento omnicanal. En esta segmentación, consideramos el papel explicativo, no solo de las características de los individuos, sino también el de los servicios de distribución y las políticas en cada canal.

Obtenemos cómo el acceso al producto, tanto en el establecimiento como a la página web, la garantía de que el producto comprado online tendrá las características esperadas y las facilidades para devolver el producto adquirido online si no cumple las expectativas, son rasgos clave de los canales que explican el comportamiento omnicanal de los clientes.

Nuestros resultados muestran claramente que diferentes aspectos de la oferta de servicios y de políticas de la empresa determinan las compras y las visitas y estos aspectos pueden ser utilizados para guiar la estrategia de segmentación del detallista.

En este trabajo contribuimos a la literatura sobre el marketing omnicanal presentando un modelo de segmentación, basado en los servicios de distribución ofertados por los minoristas, para las empresas que comercializan productos a través de distintos canales. Aportamos una distinción conceptual entre usuarios de un canal y compradores que tiene un amplio rango de aplicación.

Es necesario proseguir con las líneas futuras de investigación para investigar las implicaciones financieras de esta segmentación. La percepción de los precios que se detecta en nuestros resultados puede sugerir una sensibilidad mayor a los precios en el segmento mono-canal lo que puede ser una línea interesante a contrastar en investigaciones futuras.

Omni-canal, Moda rápida, Trabajo de investigación, Segmentación, Servicios de distribución, Comercio electrónico

Trabajo de investigación

Performing retailing in a complete omnichannel manner is not a simple task, and it considerably increases the complexity of supply chain management operations. This paper aimed at identifying the barriers hindering efficient management of a return channel in this type of retail.

A systematic literature review procedure was used, including descriptive and content analysis of results. The review was performed using four academic databases. Applicable barriers were categorized thematically, a conceptual framework was proposed and future research avenues were drawn.

The contribution of this paper comprises a theoretical description of reverse logistics applied to omnichannel retail, the identification of the return barriers in omnichannel and a conceptual framework for a holistic view of the problem. In all, 43 barriers were identified including high investments, product restocking, additional transportation costs and poor communication.

The link between reverse logistics and omnichannel area is recent and publications are still scarce, so the newness of this study limits the opportunity for further deepening or more robust validation of the results.

The results offered may be used by managers in the areas of retail and supply chain management in general in order to reduce the natural complexity in omnichannel environment.

Regarding previous literature on omnichannel retail, only a few works consider the after-sales stage. This work intended to pave the way in this poorly explored intersection (reverse logistics and omnichannel) by presenting a conceptual framework to classify various barriers in omnichannel retail return.

The purpose of this paper is to identify the differences in the perceived media richness of three marketing channels by different consumer generations as well as to indicate the importance of the perceived media richness of each of channel for the intention of online use at pre-purchase stage.

The study is based on a sample of 1,103 consumers in Poland, which research sample included three generations: Baby Boomers, X generation and Y generation. Computer-assisted personal interview method was used with a standardised questionnaire; the research sample was determined using a quota-random method, with the quotas referring to age and gender and the nature of the place of residence (provincial town, town/city other than a provincial one, or a village) – the structure of the sample was maintained at the regional level.

Analysed channels (online, offline, phone) have different perceived media richness. Consumers from different generations can perceive media richness of the same marketing channels in various ways. Perceived media richness of the analysed marketing channels has an impact on the intention to use the channel online.

First, the fact of blurring differences between consumers generation, particularly X and Y generation. As well the fact that each generation has identified an internal diversity. Second limitation refers to the type of marketing channels included in research. Finally, limitation also applies to spatial extent of the study. The above limitations can also be some directions of future research – taking into account some other marketing channels and verifying the conclusions on other consumer groups.

General recommendation refers to the need for treating customers in each marketing channel in the same way – immediate response is one of the aspects of this recommended unified customer service in multichannel environment. Online channel communications with customers should be supported by giving consumers more personalised communication via phone, chat and social media. For older consumers, it is recommended to gradually redirect them to an online channel. Information search is a good start for online customer journey, because this channel at the pre-purchase stage is the most useful and least risky for the consumer unfamiliar with online environment.

Better understanding of the needs of different customers generations and their perception of different marketing channel at the pre-purchase stage.

Identification of intergenerational differences in the perception of media richness of online, phone and offline channels.

The purpose of this paper is to classify live chat service users in the banking industry and provide relevant descriptive information on each group to be able to suggest appropriate strategies to managers.

A total of 682 panelists from a large Canadian polling firm self-administer a web-based questionnaire. Respondents are users of financial sector live chat services. Two-step cluster analysis was performed.

Four groups emerge from the analysis. Young frequent users (Group 1) attach dominant importance to speed of service, whereas computer users (Group 3) and conservative users (Group 4) who avail themselves of live chat services via computer focus on ease of use.

This study, which details four groups of live chat service users in the banking industry, enables managers to better adapt their strategies to the different market segments with a view to providing customers with better quality service and enhancing their experience.

The study presents the first live chat service classification to detail user profiles and examine differences at the before, during and after phases of the user experience. Findings enrich the body of academic literature in the service sector, in particular literature focusing on customer service in the banking industry. The paper also provides an interesting managerial framework for the implementation of successful, segment-specific strategies.