Measuring the Influence of AI-Driven Virtual Influencers on Brand Value and Customer Engagement: Introducing Novel Variables for Next-Gen Impact Assessment

The rise of AI-driven virtual influencers (AIVIs) represents a paradigm shift in digital marketing. These synthetic personas, powered by artificial intelligence and computer-generated imagery, engage audiences and promote brands with unprecedented scalability and control. This review explores the impact of AIVIs on brand value and customer engagement, contrasting them with human influencers and identifying novel evaluation challenges. Key dimensions such as emotional realism, trust, relatability, and personalization are analyzed through emerging frameworks like the Emotional Realism Index (ERI) and Synthetic Ethics Trust Index (SETI). Quantitative and qualitative metrics, along with AI-powered analytics platforms, are reviewed to assess engagement efficacy. Additionally, the study introduces eight novel variables for evaluating AIVIs and emphasizes the need for standardization and ethical transparency. By synthesizing theoretical models with practical insights, this paper offers a multidimensional roadmap for measuring AIVIs’ strategic effectiveness and lays the foundation for more ethical, data-informed, and emotionally resonant AI-led marketing practices.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-10
DOI
https://doi.org/10.5281/zenodo.15692196
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Measuring the Influence of AI-Driven Virtual Influencers on Brand Value and Customer Engagement: Introducing Novel Variables for Next-Gen Impact Assessment

Zenodo (CERN European Organization for Nuclear Research)
AI in Service Interactions
article

Measuring the Influence of AI-Driven Virtual Influencers on Brand Value and Customer Engagement: Introducing Novel Variables for Next-Gen Impact Assessment

article en

Abstract

The rise of AI-driven virtual influencers (AIVIs) represents a paradigm shift in digital marketing. These synthetic personas, powered by artificial intelligence and computer-generated imagery, engage audiences and promote brands with unprecedented scalability and control. This review explores the impact of AIVIs on brand value and customer engagement, contrasting them with human influencers and identifying novel evaluation challenges. Key dimensions such as emotional realism, trust, relatability, and personalization are analyzed through emerging frameworks like the Emotional Realism Index (ERI) and Synthetic Ethics Trust Index (SETI). Quantitative and qualitative metrics, along with AI-powered analytics platforms, are reviewed to assess engagement efficacy. Additionally, the study introduces eight novel variables for evaluating AIVIs and emphasizes the need for standardization and ethical transparency. By synthesizing theoretical models with practical insights, this paper offers a multidimensional roadmap for measuring AIVIs’ strategic effectiveness and lays the foundation for more ethical, data-informed, and emotionally resonant AI-led marketing practices.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 75%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Measuring the Influence of AI-Driven Virtual Influencers on Brand Value and Customer Engagement: Introducing Novel Variables for Next-Gen Impact Assessment · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS