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on Marketing |
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Issue of 2026–03–09
three papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Iommi, Silvia |
| Abstract: | This dissertation presents a retrospective, current, and prospective analysis of the Artificial Intelligence impact on marketing, with an emphasis on early-stage consumer engagement. In an era marked by media saturation and diminishing consumer attention spans, brands face increasing pressure to establish meaningful engagement from the very first interaction. Artificial Intelligence has emerged as a transformative force in marketing, revolutionising data collection, segmentation, targeting, personalisation, and content creation. Despite exponential growth in AI research and adoption, academic literature has yet to fully address the strategic, operational, and ethical implications of AI in upper funnel marketing, especially regarding brand awareness, brand equity, and the privacy-personalisation paradox. The study specifically focuses on three core areas: (1) the effectiveness of AI in optimising brand awareness and consideration, (2) the development of methodologies for monitoring AI’s impact on brand equity, and (3) the ethical balance between AI-driven personalisation and consumer privacy. The research aims to bridge the gap between academic theory and industry practice, offering actionable insights for both researchers and marketing professionals. Employing a thematic analysis of secondary data, this study synthesises findings from 37 academic and 11 industry publications, filtered through systematic review protocols. Data sources include Google Scholar, Perplexity, Forbes, and McKinsey, ensuring a robust multi-perspective approach. Key findings reveal that AI adoption in upper-funnel marketing significantly enhances brand awareness and consideration, outperforming traditional methods when paired with human oversight. AI-driven personalisation increases conversion rates. However, the privacy-personalisation paradox remains a critical challenge. The study recommends phased AI implementation, putting a human-centric strategy first, and the technology second. This study also recommends including new KPIs, such as Answer Share Rate and impressions from LLMs, to measure the impact of Artificial Intelligence on brand equity. Limitations include reliance on short-term data collected through platforms owned by companies with a commercial interest in AI, as well as the underrepresentation of non-Western perspectives. Future research should address longitudinal impacts, cross-cultural consumers’ sensitivities, the evolving role of agentic AI in marketing, and the future of marketing organizations. Key Words: Artificial Intelligence, Upper Funnel Marketing, Brand Equity, Personalisation, Privacy |
| Date: | 2026–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:osf:thesis:pswec_v1 |
| By: | Tommaso Bondi; Michelangelo Rossi |
| Abstract: | Online ratings emerge from a multi-stage process that can systematically distort their informational content. We develop a unified framework decomposing the rating process into distinct components: experienced quality (driven by intrinsic quality, seller effort, and price), expectations formed prior to consumption, contextual influences, strategic distortions, idiosyncratic tastes, and selection into reviewing. This decomposition organizes a growing theoretical and empirical literature and clarifies how seemingly disparate findings -- from fake reviews to disappointment effects to selection biases - relate to distinct stages of the data-generating process. Our framework also provides a lens for evaluating platform design interventions: effective policies target specific components of the rating process, yet many distortions remain difficult to address without introducing new trade-offs. We highlight open questions where further research is most needed. |
| Keywords: | online reviews, rating biases, digital platforms, platform design |
| JEL: | D83 L86 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12427 |
| By: | Beknazar-Yuzbashev, George (University of Chicago,); Jimenez-Duran, Rafael (Bocconi University, IGIER, Stigler Center, CESifo, and CEPR); Simonov, Andrey (Columbia University and CEPR); Mateusz Stalinsk, Mateusz (University of Warwick and CAGE) |
| Abstract: | Most digital platforms are funded through advertising rather than direct payments. Why? We argue that three main factors could explain this prevalence: users are more sensitive to monetary prices than to ad loads, microtargeting improves the match quality between users and ads, and platforms can personalize ad loads and thus price discriminate. We conduct a field experiment on Facebook with 1, 810 users who install a browser extension that (i) hides nearly all ads or (ii) replaces microtargeted ads with untargeted ones. We find that hiding 82% of ads increases time on the platform by only 6%, showing that users are highly insensitive to ad loads. Removing targeting sharply reduces ad clicks and long-run engagement, indicating that targeting increases the match quality between users and ads. Finally, two-thirds of ad-load variation occurs across users, consistent with ad-load discrimination. Counterfactual simulations indicate that an ad-funded model performs at least as well as a subscription model i terms of profits and delivers higher consumer surplus. The key mechanism is that users are much less sensitive to ad loads than to monetary prices, making advertising a relatively efficient revenue source. |
| Keywords: | social media platforms ; online advertisement ; user engagement ; field experiment |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1602 |