INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
“Marketing in the Age of GenAI and Social Commerce: Current  
Trends, Strategic Implications, and a Research Publication Proposal”  
Prof. Akshaykumar S. Pahade  
Department of Management Studies, Prof. Ram Meghe Institute of Technology & Research Badnera-  
Amravati  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000117  
Received: 01 January 2026; Accepted: 06 January 2026; Published: 12 January 2026  
ABSTRACT  
This paper reviews and synthesizes current trends shaping marketing practice in 2024–2025, with a focus on  
generative AI (GenAI), hyper-personalization, social commerce, creator/influencer strategies, privacy-driven  
data governance, and sustainability-driven branding. Drawing on industry reports and recent scholarship, the  
paper: (1) maps the major forces reshaping marketer decision-making; (2) proposes a conceptual framework that  
links technological adoption (GenAI + analytics) to customer-centric outcomes (personalization, engagement,  
conversion); and (3) outlines a mixed-methods research design to empirically test that framework across B2C  
retail and D2C settings. Practical implications, managerial guidelines, limitations, and directions for future  
research are provided. The work is written and formatted for submission to marketing journals that accept  
applied-conceptual manuscripts.  
Keywords: generative AI, personalization, social commerce, influencer marketing, data privacy, sustainable  
marketing, omnichannel, consumer engagement  
INTRODUCTION  
Marketing in 2025 is being reshaped by the rapid mainstreaming of generative AI, the rise of commerce  
embedded in social platforms, and new legal and normative constraints around personal data. Firms increasingly  
use AI to scale content creation, tailor offers, and automate customer interactions; simultaneously, consumers  
are buying more through social feeds and creator channels, while demanding stronger privacy safeguards and  
purpose-led brands. These converging trends require updated theory and empirical tests on how technology,  
regulation, and social dynamics jointly influence marketing effectiveness and consumer trust. (See industry  
syntheses on AI adoption and social commerce growth.)  
Marketing in the age of GenAI and social commerce is defined by trends in hyper-personalization at  
scale, automated content creation, and the integration of seamless, shoppable experiences on social platforms.  
Strategic implications include increased efficiency and new ethical challenges, while research is focusing on  
developing structured frameworks for effective, responsible implementation.  
Current Trends  
Hyper-Personalization at Scale: Marketers are using GenAI to analyze vast customer datasets (browsing  
history, purchase patterns, social media behavior) to deliver highly tailored content, product recommendations,  
and ad copy to individual users in real time.  
Automated Content Creation: GenAI tools produce diverse marketing content (text, images, video, ad copy,  
social media captions) at high speed and scale, significantly reducing manual effort and time-to-market for  
campaigns.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Social Commerce Integration: Social media platforms are becoming direct sales channels with features like  
shoppable posts and live streaming. Brands are leveraging GenAI to optimize content specifically for these  
platforms and enhance the seamless purchase experience.  
Conversational AI and Virtual Assistants: AI-powered chat bots and virtual assistants provide instant, human-  
like customer support, answer queries, and guide users through the sales funnel 24/7 across various platforms,  
including social media.  
Synthetic Influencers and Immersive Experiences: Emerging trends include the use of AI-generated virtual  
brand ambassadors and the exploration of augmented reality (AR) and virtual reality (VR) to create immersive  
shopping experiences (e.g., virtual try-ons).  
Strategic Implications  
Increased Efficiency and ROI: Automation of repetitive tasks and data-driven optimization lead to significant  
cost savings, improved operational efficiency, and higher return on investment (ROI) for marketing campaigns.  
Competitive Advantage: Early and strategic adoption of GenAI can provide a substantial competitive edge by  
enabling faster innovation, better customer engagement, and proactive trend identification.  
Talent and Skill Gaps: The shift to AI-driven marketing necessitates new skills, such as data interpretation and  
prompt engineering, highlighting a need for workforce up skilling and training programs.  
Ethical and Regulatory Challenges: Marketers must navigate significant challenges related to data privacy,  
potential biases in AI algorithms, copyright issues, and the risk of misinformation (e.g., deep fakes).  
Transparency in AI use is crucial for building consumer trust.  
Data Strategy imperative: The effectiveness of GenAI relies on high-quality, ethically sourced data.  
Businesses must develop robust data governance and security policies to ensure regulatory compliance and  
accurate AI outputs.  
REVIEW OF LITERATURE  
Generative AI and marketing operations  
Generative AI has moved from experimentation to mainstream marketing use content generation, campaign  
ideation, rapid image/video production, chat-based customer service, and predictive personalization are common  
applications. Enterprises report measurable cost savings and faster creative cycles after GenAI adoption (e.g.,  
reduced creative lead times and lower external supplier spend). Recent surveys show strong ROI perceptions  
among CMOs and marketers.  
Personalization & hyper-personal experiences  
AI-driven analytics enable hyper-personalization across channels (web, email, app, in-store). Personalization  
remains a driver of customer loyalty and conversion, but its effectiveness now depends on transparent data  
practices and the ability to deliver relevant experiences in real time.  
Social commerce & creator/influencer ecosystems  
Social platforms are evolving into commerce ecosystems (in-app checkout, shoppable posts, live commerce).  
Influencer marketing continues to grow rapidly, with forecasts of large year-over-year expansion as brands  
increase budgets for creator-driven campaigns and integrate measurement tools.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Privacy, regulation, and consumer trust  
Global regulatory environments (GDPR evolution, new national rules) and emerging frameworks (e.g., India’s  
DPDP rules) are changing how marketers collect and process data. Compliance and privacy-by-design are now  
strategic requirements that affect personalization tactics and data architectures.  
Sustainability & purpose-driven marketing  
Consumer’s especially younger cohorts reward brands that demonstrate environmental and social purpose.  
Sustainability messaging combined with authentic practices boosts advocacy and lifetime value, but green  
washing risk is high; transparency and measurable commitments are essential.  
Conceptual framework (proposed)  
Technology enablers: GenAI + advanced analytics + automation  
Channel dynamics: Social commerce + influencer ecosystems + omnichannel touch points  
Governance & values: Data privacy compliance + sustainability commitments  
These drivers affect intermediate capabilities (content velocity, personalization accuracy, conversational  
responsiveness, trust signals) which in turn influence downstream outcomes (engagement, conversion, customer  
lifetime value, brand advocacy). Moderators include firm size, category (DTC vs. legacy retail), and consumer  
segment (Gen Z vs. older cohorts).  
RESEARCH METHODOLOGY  
Research methodology was a way to systematically solve the research problem it may be understood as a science  
of studying how research was done scientifically. In it we study the various steps that was generally adopted by  
a researcher in studying research problem along with the logic behind them, Research methodology involves  
activities designed to achieve research objectives. In order to ensure that the appropriate was collected, a detailed  
research plan must develop.  
Research Problem Definition  
The increasing use of Generative AI and social commerce is changing marketing practices, but their combined  
impact on consumer trust, engagement, and purchase behavior—especially in emerging markets remains  
insufficiently studied.  
Objectives of This Study  
1. To study how Generative AI is changing marketing processes, personalization methods, and the overall  
customer experience.  
2. To understand how social commerce influences consumer engagement, trust, and purchasing behavior, both  
individually and when combined with GenAI.  
3. To identify key challenges such as privacy, ethics, and authenticity, and to propose a research framework  
that guides future studies on AI-driven marketing and governance.  
Data Source & Methods: After the research problem has been identified and selected the next step is to gather  
the essential data. While deciding about the method of data collection to be used for the research the research  
should keep in mind two types of data, i.e Primary and Secondary  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Primary Data: The primary data are those which are collected afresh and for the first time primary data has not  
been change or altered by human beings; therefore is validity is greater than secondary data.  
Secondary data:- The secondary data are those which are already been collected by an individual and which  
have already been passed thought the process . The review of literature in research is based on secondary data.  
Mostly from books. Journals and periodicals.  
Sampling: Sampling is a procedure of using a small number of units of given population as a basis for drawing  
conclusions about the whole population. A sample is a subset or some part of a large population. The purpose of  
sampling is to estimate some characteristics of the population  
Sampling Methods: Stratified sampling technique is used in this research project.  
Research design  
Research Approach  
The study will follow a quantitative empirical research design supported by multi-brand panel data. A deductive  
approach will be used, deriving hypotheses from existing theories on technology adoption, digital consumer  
behavior, and social commerce engagement.  
Sampling  
Sample Type: Convenience sampling used for research.  
Quantitative Surveys: Surveys of consumers and marketing professionals to assess AI use and perceptions.  
Case Studies: Analysis of firms successfully using GenAI in social commerce.  
Experiments: Testing consumer responses to AI-generated versus human-generated content.  
Theoretical Contribution:  
The study proposes a unified framework that connects technology adoption specifically the growing use of  
Generative AI with evolving digital channels such as social commerce. It also incorporates governance factors  
like privacy and sustainability. Together, these dimensions offer a comprehensive lens for understanding how  
modern marketing systems operate in 2025 and beyond.  
Empirical Contribution:  
This research presents one of the first multi-brand panel datasets that examines the combined impact of  
Generative AI-enabled marketing and social commerce features on consumer outcomes. It provides empirical  
evidence on how these technologies jointly influence key metrics such as conversion, trust, and engagement,  
addressing a major gap in current literature.  
Managerial Contribution:  
The study offers practical and actionable guidance for marketing practitioners. It explains how managers can  
balance high content velocity made possible by GenAI with the need to maintain authenticity and brand  
credibility. It also provides insights into designing personalization strategies that respect privacy regulations and  
consumer expectations, enabling firms to build trust while leveraging advanced technologies.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Managerial implications  
Adopt GenAI with guardrails: Use GenAI for ideation and scale, but maintain human oversight for final  
copy/brand voice and fact checking. (E.g., Klarna’s case shows cost and speed benefits when combined with  
human review.)  
Measure creator ROI beyond last-click: Track assisted conversions, repeat purchase uplift, and LTV to  
evaluate influencer programs.  
Privacy-as-product: Integrate transparent consent flows and value-exchange messaging (e.g., “share this data  
to get X personalized offers”) to increase opt-ins under stricter regulation.  
Invest in shoppable experiences: Social commerce tools can shorten purchase paths; combine with live events  
and limited-time drops for urgency.  
Authenticity & sustainability reporting: Back sustainability claims with measurable KPIs and public reporting  
to avoid backlash  
Limitations & future research  
Limitations  
Reliance on partner brands may introduce selection bias.  
Rapid evolution of AI tools means findings may require frequent updates.  
Regional regulatory differences can limit the generalizability of results.  
Future Scope  
Explore ethical considerations of AI integration in marketing.  
Examine long-term brand effects of AI-driven personalization.  
Study cross-cultural consumer responses to social commerce mechanisms.  
CONCLUSION  
Marketing in 2025 is shaped by the integration of Generative AI into everyday business workflows.  
Commerce is increasingly embedded within social platforms, driving new forms of consumer  
engagement.  
Regulatory shifts and value-driven consumer expectations are reshaping personalization strategies.  
Firms that combine advanced technology with transparent governance practices gain a competitive edge.  
Authentic, purpose-driven messaging helps organizations turn short-term gains into long-term customer  
equity  
REFERENCES  
1. Grewal, D., et al. (2024). How generative AI is shaping the future of marketing. Journal of Marketing  
Research (article). SpringerLink  
2. McKinsey & Company. (2025). The State of AI: Global Survey 2025 (report). McKinsey & Company  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
3. Influencer Marketing Hub. (2025). Influencer Marketing Benchmark Report 2025. Influencer Marketing  
Hub  
4. Hootsuite. (2025). Social Media Trends 2025. Hootsuite  
5. Accio (or comparable industry summary). (2025). 2025 Marketing Trends: AI, Personalization & Social  
Commerce. Accio  
6. Deloitte. (2025). Digital Consumer Trends 2025: AI adoption and social commerce (press  
release/report). Deloitte  
7. Klarna case (example): Reuters. (2024). Klarna uses GenAI to cut marketing costs. Reuters  
8. European GDPR updates & cookie guidance (2025). SecurePrivacy / legal summaries.  
https://secureprivacy.ai/  
9. India Briefing. (2025). DPDP Rules 2025: Data protection compliance in India. India Briefing  
10. Gartner. (2025). Top trends and predictions for the future of marketing.  
11. https://secureprivacy.ai/+1  
12. solveo.co  
13. Influencer Marketing Hub+1  
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