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Paper A

Unveiling Trends in AI-Powered Marketing

Parul Singh, Vasco Santos — 2026

academic book chapter  ·   ·  watchlist

Key findings

• Four distinct topic clusters emerged from the academic literature on AI-driven marketing and virtual influencers — the specific topics are not named in the abstract, so exact content cannot be reported here.
• AI-created virtual influencers (digital avatars built with AI and computer-generated imagery) are a growing trend in marketing, used to engage audiences, personalize content, and encourage co-creation between brands and consumers.
• The LDA analysis surfaced patterns across the literature that researchers and marketers had not previously organized into a single framework — suggesting the field is fragmented and still taking shape.
• Visualizations were used to present topic distributions, which the authors say offer actionable insights for both academic researchers and business practitioners.

Marketing implications

• Virtual influencers are moving from novelty to mainstream strategy — if your brand hasn't tested one yet, now is a reasonable time to run a small pilot campaign with an AI avatar and measure engagement versus a human creator.
• The research landscape on AI influencers is still fragmented and emerging, which means early movers who document their own results have a real advantage — keep records of what works so you're not starting from scratch when standards solidify.
• When briefing agencies or social teams, ask specifically about co-creation and personalization features of virtual influencers, not just follower counts — the literature suggests these interactive elements are where the value is building.

Paper B

AI-Driven Marketing

Kaushal Kishore Mishra, Pawan Pant, Azmee Zaheer — 2026 — Advances in Computational Intelligence and Robotics (IGI Global book series)

academic book chapter  ·   ·  watchlist

Key findings

• AI tools like chatbots, recommendation engines, and predictive analytics are helping small businesses and startups understand their customers better and personalise their offers — without needing a large team to do it manually.
• Using AI, companies can tailor what they show each customer based on that person's data, which can improve how many people buy, come back, or stay loyal over time.
• AI can help businesses make faster, more informed decisions about where to focus their marketing efforts — for example, predicting which customers are likely to leave before they actually do.
• Deploying AI in marketing also comes with real challenges: keeping customer data private, and making AI tools work with existing business systems are two problems companies need to plan for upfront.

Marketing implications

• If you run a small business or startup, this chapter signals that AI tools (chatbots, recommendation engines, predictive analytics) are accessible enough to start testing — you don't need an enterprise budget to begin personalising customer communications.
• Before rolling out any AI marketing tool, map out your data privacy obligations and check whether the tool actually connects to your existing CRM or e-commerce platform — these are the two biggest blockers the chapter flags.
• If you're writing a business case for AI adoption, this chapter can serve as a supporting reference for the customer acquisition and retention use cases.

Paper C

AI for Marketing and Customer Engagement in Entrepreneurship

Saliu Hakeem Tomi, Olumoyegun Peter Mayowa, Omale Danjuma, Uloko Felicia et al. — 2026 — Advances in Computational Intelligence and Robotics (IGI Global book series)

academic book chapter  ·   ·  watchlist

Key findings

• AI tools like recommendation engines and churn prediction models can help small business owners figure out which customers are about to leave — and act before they do.
• Personalizing what customers see (based on their data) tends to make them happier and more likely to stay, according to examples from streaming, hospitality, and fintech industries.
• Entrepreneurs who use data-driven AI tools can compete more effectively against larger companies by targeting the right customers at the right time.
• The chapter argues that combining AI insights with a clear business strategy — not just plugging in AI tools — is what drives long-term growth.

Marketing implications

• If you run a small business or startup, look into basic churn prediction tools (many CRMs offer this) — they can flag customers who are drifting away so you can reach out before losing them.
• Personalization doesn't require a big budget: even simple recommendation logic (e.g., 'customers who bought X also bought Y') can lift engagement, especially in e-commerce or subscription services.
• AI tools are only useful if they connect to a real strategy — before adopting any AI marketing tool, decide what customer behavior you're actually trying to change.

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