IDC Revision

Week 7 · Kshetri, Dwivedi, Davenport & Panteli (2024)

GenAI in Marketing — Applications, Opportunities & Research Agenda

Generative Artificial Intelligence in Marketing: Applications, Opportunities, Challenges, and Research Agenda

Part of: Digital Marketing Topic 7 — Trends: Mobile / Voice / Location · Reading (editorial) Citation: Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. (2024). International Journal of Information Management, 75, 102716. https://doi.org/10.1016/j.ijinfomgt.2023.102716 Key concepts: Generative AI, Personalization, Content Marketing, Customer Experience, Lead Generation

TL;DR

This editorial maps the current state of Generative AI (GAI) in marketing and argues marketing is the firm function most positively transformed by it. It focuses on the top three applications identified by a 2023 Boston Consulting Group survey — personalization, insight generation and content creation — and surveys the facilitators (ease of use, trialability, low cost, tool variety, fine-tuning) and barriers (cost for SMEs, data security/privacy, transition costs, culture, job-loss fears) of adoption. Its central question is the mechanisms by which GAI affects marketing activities and outcomes, answered through five propositions tying GAI to more personally relevant content, higher efficiency/productivity, and a better Lead Generation process. GAI augments rather than replaces human teams.

Why It's on the Reading List

It is the broad, applied survey of how GAI reshapes everyday marketing trends and touchpoints (personalised, location- and behaviour-aware content), pairing concrete tools and survey statistics with a testable research agenda — useful for exam questions on GAI applications, facilitators/barriers, and customer experience.

Background & Research Question

Adoption is exponential: as of March 2023, 73% of US organisations had used GAI tools in marketing; the AI-in-marketing market was ~$15.84bn (2021), forecast to reach $107.5bn by 2028. Prior work examined GAI tools but left a gap on how they affect activities, outcomes and financial performance. Central question: What are the mechanisms by which GAI impacts marketing activities and outcomes? Digital technologies enable personalised, timely communication → better Customer Experience → engagement → loyalty and firm performance; GAI is argued to do this more powerfully than earlier technologies because it generates human-like, easily customised text, images and video.

Key Concepts & Definitions

Definition — Generative AI (GAI)

A category of AI systems capable of creating apparently new content through text, images or other media. Includes foundational models (GPT-4, DALL-E2, Midjourney) and marketing-tuned versions (Jasper.ai, Copy.ai).

Definition — Personalization

Using data collected from customers to deliver customised content and offers meeting their unique needs. GAI enables hyper-personalization at scale, but requires integration with CRM / marketing-automation systems.

Definition — Content marketing

A strategic approach focused on creating and distributing valuable, relevant, consistent content to attract and retain a defined audience and drive profitable action (Content Marketing Institute, 2015).

Representative GAI tools and applications:

Tool Marketing use
ChatGPT / GPT-4 Brainstorming, content creation, personalised solutions (GPT-4 accepts image + text)
DALL-E2 / Midjourney / Stable Diffusion Custom images and art from text prompts
Meta AI Sandbox Ad-copy variations, text-prompt backgrounds, image cropping to aspect ratios
Jasper.ai / Copy.ai / Peppertype.ai Blog posts, product descriptions, ad copy (Jasper uses OpenAI, Google, Meta and Anthropic's Claude)
Salesforce Einstein GPT / MS Copilot (Dynamics 365 Customer Insights) Insight generation and personalisation inside the CRM/Office stack

Main Arguments / Findings

Survey evidence (Table 2 in source): Salesforce/YouGov — GAI saves marketers >5 hours/week (~32.5 days/year). BCG — 51% of CMOs using GAI, +22% planning to "very soon". Botco.ai — 73% used GAI to create content; top 3 uses: personalization (67%), insight generation (51%), content creation (49%). Conference Board — 87% had used/experimented; mid/junior marketers adopt faster than seniors; only 4% expected productivity to decline but 40% expected a decline in marketing jobs.

Efficiency in practice — WPP / Cadbury

McKinsey estimates GAI could save 5–15% of total marketing spend. WPP's GAI-driven Cadbury campaign in India "featured" Shah Rukh Khan asking customers to shop at 2,000 local stores for Diwali; ~130,000 store-personalised ads were generated, viewed 94 million times. Shooting a GAI commercial can cost one-tenth to one-twentieth of normal.

Facilitators vs barriers

Facilitators: perceived ease of use, trialability (free/open tools like ChatGPT, Stable Diffusion), low/non-prohibitive cost, wide tool variety, ability to fine-tune on a firm's own content. Barriers: subscription cost (Copilot +$30/user/month) especially for SMEs; data security/privacy (employees pasting confidential data; some firms banned ChatGPT); transition/learning costs and prompt-writing skill; cultural fit (only 16% expected culture improvement); GAI-led job losses.

Framework / Model

The editorial advances five propositions (P1–P5) linking GAI to marketing outcomes:

# Proposition (vs previous digital technologies)
P1 GAI-generated market/customer insights are more effective in personalising content and offerings.
P2 GAI-generated marketing content is more personally relevant.
P3 GAI use in content creation leads to higher efficiency and productivity.
P4 GAI generates better insights to improve the sales lead generation process.
P5 GAI creates more effective content, improving lead generation.

A future-research agenda (Table 3) flags: customer-service LLMs vs traditional chatbots, value-proposition mechanisms, emotional vs cognitive Customer Experience, units/organisational characteristics of adoption, fostering creativity (deskilling/reskilling, pedagogy), and cross-functional comparison.

Implications for Marketers

  • Use GAI for personalization at scale — but only when integrated with CRM/automation (e.g. Einstein GPT inside Customer 360).
  • Exploit GAI's speed and cost advantage in content (multiple ad angles, artist-style visuals, localised imagery, video demos).
  • Apply GAI across the lead-generation funnel: insight-driven behavioural targeting, automated cold outreach, lead qualification, personalised messaging.
  • Combine, don't replace — pair open LLMs with purpose-built conventional chatbots and human teams; LLMs' open nature is unsuitable for some CX tasks.
  • Manage barriers proactively: cost for SMEs, data-privacy controls (e.g. ChatGPT Enterprise), prompt-writing capability and cultural change.

Exam Takeaways

Likely exam points
  • Top three GAI marketing applications: personalization, insight generation, content creation (BCG, 2023).
  • Five propositions: insights/content are more personally relevant and effective, and raise efficiency, productivity and lead generation vs earlier digital tech.
  • Facilitators (ease of use, trialability, low cost, tool variety, fine-tuning) vs barriers (cost, privacy, transition cost, culture, job loss).
  • Personalization at scale needs CRM/marketing-automation integration.
  • GAI augments, not replaces, human marketers and conventional chatbots.
  • Headline stats: 73% US adoption (Mar 2023); ~5 hrs/week saved; 5–15% potential marketing-spend saving (McKinsey).

Summary

  • GAI is the most positively transformed firm function; its top uses are personalization, insight generation and content creation.
  • Five propositions link GAI to more relevant content, higher efficiency and better lead generation.
  • Adoption is shaped by clear facilitators and equally clear barriers (notably privacy and cost for SMEs).
  • GAI complements rather than replaces human teams and existing tools.