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To cope with online competitors and new consumer behaviors, retailers need to hybrid digital and physical offerings to implement an omni-channel business model. This constitutes a digital transformation of the traditional business model. However, business cases on how traditional retailers are shifting from multi-channel to omni-channel retailing are lacking. This paper aims to explore the different issues and organizational paths during the transformation of a business model.

This study is based on a qualitative multiple case study of five retailers with a global reach currently implementing an omni-channel business model.

This research sheds light on three main issues encountered by retailers and the different underlying decisions when moving toward an omni-channel business model. The first relates to revenue attribution across channels, which involves rethinking traditional key performance indicators to give incentives to stores when promoting digital offers. The second issue concerns the supply chain decisions associated with cross-channel operations. The third issue relates to the delicate balance between global reach (digital channel) and local reach (specific store) for communication on social media and marketing decisions on pricing. This study provides empirical evidence about the variety of choices that retailers make to cope with the issues during the implementation of an omni-channel business model.

This work explores the issues faced by established firms when moving toward a new business model that is the hybridization of two existing business model managed separately. It provides comprehensive and clear illustration of how to manage such a business model transformation process that can be used by both business strategy practice and academic research.

This study aims to analyze the impact of newly created brand awareness on customer’s buying behavior in online environment.

The authors analyzed more than 280,000 online customer journeys from four e-commerce stores based in Slovakia. Within the results of the interaction analysis of individual customer journeys, the authors determined three criteria based on the level of theoretical brand awareness. The purpose was to determine their occurrence in real-world data.

It was found that each of the specified criteria accounts for the significant share of the company’s revenues. Based on these criteria and the level of their occurrence, the authors introduced the term direct traffic effect.

Because of the available Web analytics tools, the data might be imprecise because of data collection issues. There is also ambiguity in the interpretation of the customer journey.

The company can build awareness among prospective customers by offering them a positive customer experience during the first interactions online. Data proved that customer will not only repeatedly visit the website from the direct traffic source but also his customer journey will end with the purchase of the company’s products.

This paper fulfills the need for further research on the impact of multi-channel marketing on brand awareness and consumer behavior, respectively.

Understanding the customer has been the focus of attention of businesses and academia for many decades. Starting in 1960s, complex buyer behavior models developed by Nicosia, by Howard and Sheth (1969), were followed by Engel, Blackwell and Miniard in 1978 (Engel, Blackwell, & Miniard, 1990) to understand the buying process, shaping the thoughts today about consumers’ experiences in an omnichannel world. Interest in customer perceptions and expectations (Parasuraman, Berry, & Zeithaml, 1991), SERVQUAL (Parasuraman, Zeithaml, & Leonard, 1985) and SERVPERV (Cronin & Taylor, 1994) moved the academia to discuss the relationship marketing (Morgan & Hunt, 1994; Parvatiyar & Sheth, 1999; Peterson, 1995; Sheth & Parvatiyar, 1995). Wilson’s model (1995) of buyer–seller relationships extended the former models with additional concepts like social bonds, comparison level of alternatives, power roles, technology, structural bonds and cooperation as influencers on relationship development stages. His emphasis reflects a high relevancy in the omnichannel world of customers’ interactions today. Winer (2001), a pioneer to discuss the customer relationship management focused on a database to know about customers’ purchase history and interests. The millennium look at customer lifetime value is again relationship focused. For Fader, Hardie, and Lee (2005) rather the long-term focus of the consumer value and actions are important to understand the loyalty and nonlinear nature of relations. While Reinartz and Kumar (2003) focused on profitable customer lifetime and customer heterogeneity, Verhoef (2003) analyzed the impact of customers’ relationship perceptions and relationship marketing instruments on both customer retention and customer share development. The customer-centric thinking was first discussed by Grönroos (2006) within a new definition of marketing. The service dominant logic (Vargo & Lusch, 2008) resulted in the next highlight, the co-creation of value with customer involvement and customer advisory (Güngör, 2012; Güngör & Bilgin, 2011; Messner, 2007) empowering the customers and giving them the control over the supplier networks. Different factors will be influential at different stages of the buying process of customer clusters. The Web- and non-Web-based customer-centric measures can be multifold. Andersson, Movin, Mähring, Teigland, and Wennberg (2018) and Bank (2018) emphasize the importance of technology readiness focus throughout the customer–supplier journey. The question to be answered is, to which extent the empowered customers and the suppliers of this age are ready to adopt, embrace and finally use new technologies in the omnichannel world of holistic interactions that form new visions, expectations, values and desires in a tremendous speed. Ideas and experiences are shared and exchanged in online communities without the need of the involvement of the suppliers. This “holistic view” challenges firms further through the seamlessness it requires to create unity. Customer-centric research needs a new push for the development of instruments and measures to cope with the consumer decision process challenges. Process thinking is needed to capture the purchasing habits in an omnichannel world and to build a new thought for customer journey experience with the aim to understand technology-linked value propositions of customer clusters to optimize channel interactions. Customer journeys have to focus and describe the online/offline experiences at the hybrid shopping mile, trace the behavioral influential factors of the customers’ and sellers’ world in a technological environment. This chapter will discuss “Technology based Orbit Interactions” for “The Hybrid Shopping Mile and its Customer Journey Mapping” with a “Customer Intelligence Framework.” The outcome of the hybrid customer journey mapping gives orientation for customer-management decisions in developing new approaches.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

It is important to measure the interaction between conversing in social media and searching on the web in order to understand the impact on electronic word-of-mouth marketing. The paper aims to discuss this issue.

The authors research the relationship between social media conversing and web searching concerning brands on three major social soundtrack platforms (Instagram, Twitter, and Tumblr) and on a major web search engine (Google). The authors examine the effects of changes in both volume and attitude of conversing and volume of searching for two phases (Pre and Post) concerning brands in commercials aired during Super Bowl XLIX. The authors perform Granger causality testing and panel data regression analysis to determine the causal relationship between social media conversing and web searching.

Results show that volume and attitude of social media conversing has a significant causality relationship to the volume of web searching. Each unit increase of volume on Twitter, Instagram, and Tumblr significantly increases Google search volume for the same brands by 4.7 times, 11.9 times, and 8.7 times, respectively. Each unit increase of attitude score on Twitter significantly increases web search volume 3.96 times, while for Tumblr, search volume significantly increases 0.95 times with each unit. Interestingly, search volume also has a significant causality relationship on the volume of social media postings.

This research seeks to understand the commercial impacts of the interaction among broadcast advertising, social media conversing, and web searching for which there is limited prior work, especially in the context of a major media event.

The purpose of this paper is to expand existing research on fulfillment systems for multi-channel retailers by combining analysis of customer expectation (CE) with economic performance (EP) aspects.

This exploratory study is based on a literature review of supply chain management and marketing combined with the results of an online questionnaire submitted to French and Chinese multi-channel retailers from two sectors.

The results enable the authors to identify 13 important key performance indicators (KPIs) used by multi-channel retailers for measuring e-fulfillment system performance and also to determine under which of the EP or CE criteria these KPIs might be linked.

Although the sample size is limited, this research is intended to be exploratory, based on the practices of supply chain and marketing managers. Moreover, the results do not address interdependencies among the different KPIs.

E-fulfillment is a major challenge for multi-channel retailers; it is cost intensive and critical for customer satisfaction. The identified set of KPIs enables retailers to choose those relevant to their particular infrastructure.

A limited number of scholarly works have explored the operational implications of online retailing in terms of e-fulfillment in a multi-channel context. In order to manage such supply chains efficiently and effectively, traditional measures of supply chain performance need to be adapted. This study expands existing knowledge by developing measurement systems and metrics for the evaluation of supply chain performance in this context.

The advent of digitalization and the trend of social distancing coincide with the individualized lifestyle that is emerging among contemporary shoppers. This study explores the unique market of “smart solo shoppers”. Two empirical studies are conducted, which aim to identify the major dimensions of multi-channel shopping activities that are engaged by the shoppers (Study 1, n = 64) and to differentiate the shoppers' valuation of time invested in the distinct dimensions under different cultural influences (Study 2, n = 519).

A survey questionnaire is used for data collection, and data are analyzed using structural equation modeling.

Results reveal that the shopping activities converge into four principal dimensions: offline shopping, online shopping, post-shopping delivery and product return activities. Shoppers who perceive offline shopping activities as a time burden and online shopping and delivery activities as venues of value creation are more strongly self-identified as smart solo shoppers. Furthermore, smart solo shoppers who are under a strong influence of individualistic culture are found to spend time on multi-channel shopping to make the right purchase the first time, whereas shoppers perceiving being in a weak individualistic culture prefer to engage online channels while being prepared to return the unwanted purchases.

This study highlights an emerging research field in the nexus of solo consumption and smart shopping. Emphasizing the utility-driven and ego-expressive nature of smart solo shoppers, the authors provide an initial profile of these shoppers based on their time-valuation patterns and the contextual impacts of individualistic culture.

Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.