Transforming customer engagement with artificial intelligence E-marketing: an E-retailer perspective in the era of retail 4.0
Marketing Intelligence & Planning
ISSN: 0263-4503
Article publication date: 21 May 2024
Issue publication date: 3 September 2024
Abstract
Purpose
With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.
Design/methodology/approach
The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.
Findings
The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.
Originality/value
The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.
Keywords
Citation
Behera, R.K., Bala, P.K., Rana, N.P., Algharabat, R.S. and Kumar, K. (2024), "Transforming customer engagement with artificial intelligence E-marketing: an E-retailer perspective in the era of retail 4.0", Marketing Intelligence & Planning, Vol. 42 No. 7, pp. 1141-1168. https://doi.org/10.1108/MIP-04-2023-0145
Publisher
:Emerald Publishing Limited
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