Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

Omnichannel Marketing: The Challenge of Data-Integrity

Authors: Tony Haitao Cui; Anindya Ghose; Hanna Halaburda; Raghuram Iyengar; Koen Pauwels; S. Sriram; Catherine E. Tucker; +1 Authors

Omnichannel Marketing: The Challenge of Data-Integrity

Abstract

Channels have traditionally been viewed as intermediaries that facilitate the transfer of products from manufacturers to consumers. Innovations in digital technologies help firms to integrate the customer experience across channels and devices. This new phenomenon is referred to as “omnichannel marketing.” Existing research on omnichannel marketing has emphasized that this means that firms need to integrate and optimize across channels rather than within channels. In this paper, we argue that questions of data integrity are now at the forefront of the challenges that firms face embarking on omnichannel marketing strategy. The emergence of digital platforms, AI-powered assistants, and mobile devices have led to even more voluminous and diverse data, beyond simple binary formats, being created about consumers. However, we argue that tracking consumers across so many different devices and touchpoints is problematic, especially when the firm does not control that channel. We discuss two technologies that firms are using to address these challenges of data-tracking: Machine Learning and Blockchain. Firms now need to use machine learning to attempt to track consumers across different touchpoints and predict their response to marketing actions. Firms can also use a variety of blockchain technologies to better ensure data is tracked in a robust way across different devices. Last, we highlight that the need for individual-level tracking implied by omnichannel marketing and the technologies deployed to address this need are often in tension with consumer privacy, and that firms need to recognize the tradeoffs between optimizing omnichannel marketing strategies and consumer privacy.

  • BIP!
    Impact byBIP!
    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).
    9
    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 10%
    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 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
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!
9
Top 10%
Top 10%
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!