Monetization of customer futures through machine learning and artificial intelligence based persuasive technologies

Abstract

Purpose

The purpose of this study was to ascertain how real options investment perspective could be applied towards monetization of customer futures through the deployment of machine learning (ML) and artificial intelligence (AI)-based persuasive technologies.

Design/methodology/approach

The authors embarked on a theoretical treatise as advocated by scholars (Cornelissen, 2019; Barney, 2018; Cornelissen, 2017; Smithey Fulmer, 2012; Bacharach, 1989; Whetten, 1989; Weick,1989). Towards this end, theoretical argumentative logic was incrementally used to build an integrated perspective on the deployment of learning and AI-based persuasive technologies. This was carried out with strategic real options investment perspective to secure customer futures on m-commerce apps and e-commerce sites.

Findings

M-commerce apps and e-commerce sites have been deploying ML and AI-based tools (referred to as persuasive technologies), to nudge customers for increased and quicker purchase. The primary objective was to increase engagement time of customers (at an individual level), grow the number of customers (at market level) and increase firm revenue (at an organizational level). The deployment of any persuasive technology entailed increased investment (cash outflow) but was also expected to increase the level of revenue and margin (cash inflow). Given the dynamics of market and the emergent nature of persuasive technologies, ascertaining favourable cash flow was challenging. Real options strategy provided a robust theoretical perspective to time the persuasive technology-related investment in stages. This helped managers to be on time with loading customer purchase with increased temporal immediacy. A real options investment space involving six spaces has also been developed in this conceptual work. These were Never Invest, Immediately Investment, Present-day Investment Possibility, Possibly Invest Later, Invest Probably Later and Possibly Never Invest.

Research limitations/implications

The foundations of this study domain encompassed work done by an eclectic mix of scholars like from technology management (Siggelkow and Terwiesch, 2019a; Porter and Heppelmann, 2014), real options (Trigeorgis and Reuer, 2017; Luehrman, 1998a, 1998b), marketing intelligence and planning (Appel et al., 2020; Thaichon et al., 2019; Thaichon et al., 2020; Ye et al., 2019) and strategy from a demand positioning school of thought (Adner and Zemsky, 2006).

Practical implications

The findings would help managers to comprehend what level of investments need to be done in a staggered manner. The phased way of investing towards the deployment of ML and AI-based persuasive technologies would enable better monetization of customer futures. This would aid marketing managers for increased customer engagement at the individual level, fast monetization of customer futures and increased number of customers and consumption on m-commerce apps and e-commerce sites.

Originality/value

This was one of the first studies to apply real options investment perspective towards the deployment of ML and AI-based persuasive technologies for monetizing customer futures.

Citation

Bhattacharyya, S.S. (2023), "Monetization of customer futures through machine learning and artificial intelligence based persuasive technologies", Journal of Science and Technology Policy Management, Vol. 14 No. 4, pp. 734-757. https://doi.org/10.1108/JSTPM-09-2021-0136