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Journal of Direct Data and Digital Marketing Practice
Article . 2014 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
e-Prints Soton
Article . 2014 . Peer-reviewed
Data sources: e-Prints Soton
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Mapping customer journeys in multichannel decision-making

Authors: Wolny, Julia; Charoensuksai, Nipawan;

Mapping customer journeys in multichannel decision-making

Abstract

This study is focused on multi-channel shopping, which refers to the integration of various channels in the consumer decision-making process. The term was coined in the early 2000s to signify the integration of offline and online shopping channels. It has since evolved to encompass the proliferating number of channels and media used to formulate, evaluate and execute buying decisions. With the explosion of mobile technologies and social media, multi-channel shopping has indeed become a journey in which customers choose the route they take and which, arguably, needs to be mapped to be understood. Existing consumer decision-making models were developed in pre-internet days and have remained for the most part unquestioned in the digital marketing discourse. Darley, Blankson and Luethge concluded that there is a ‘paucity of research on the impact of online environments on decision making’, which has also been observed in the multi-channel context. Our study adopts an inductive approach allowing for realistic patterns to emerge of how consumers use and react to different media and channels in their shopping journeys for cosmetics. It therefore provides a threefold contribution: (1) it systematizes what are widely used yet largely misunderstood practices (ZMOT, webrooming and showrooming); (2) it defines the key multi-channel influences across different stages of decision making; and (3) it segments actual customer journeys into three distinct patterns that brands can use to optimize their multi-channel strategies.

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    138
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
138
Top 1%
Top 1%
Top 10%
bronze