07/11/2024
By Xavier Babu

The Manning School of Business, Department of Operations and Information Systems invites you to attend a doctoral dissertation defense by Xavier Babu on “Unveiling the Impact of Analytics on Customer and Analyst Engagement.”

Candidate Name: Xavier Babu
Degree: Doctoral
Defense Date: Wednesday, July 24, 2024
Time: 1 – 2:30 p.m. (EDT time)
Location: Via Zoom Link
Dissertation Title: Unveiling the Impact of Analytics on Customer and Analyst Engagement

Questions, please email Xiaobai_Li@uml.edu.

Committee:
Julie Zhang, Ph.D., Professor of Operations and Information Systems, UMass Lowell (Co-chair)
Luvai Motiwalla, Ph.D., Professor of Operations and Information Systems, UMass Lowell (Co-chair)
Berk M Talay, Ph.D., Professor of Marketing Entrepreneurship and Innovation, UMass Lowell
Xiaobai (Bob) Li, Ph.D., Professor of Operations & Information Systems, UMass Lowell

Abstract:
Analytics has a profound impact on both customers and analysts' engagement. Leveraging affinity analytics enables organizations to understand their audience better and improve engagement and conversion rates through targeted strategies. While employing analytics engagement and analytics application platforms empower analysts to make informed decisions, drive strategic initiatives, and contribute to business success. Therefore, this dissertation focuses on unveiling the impact of affinity analytics in customer engagement, leveraging large language models (LLM) and employing deep learning framework and analytics platforms in the engagement of analysts, leveraging Artificial Intelligence (AI) and employing action design research (ADR).

The research scrutinizes the efficacy of manager responses to product reviews, a critical aspect in shaping customer satisfaction and product reputation, which are fundamental to bolstering engagement with the product. Therefore, the first essay focuses on quantifying the effectiveness of manager responses responding to review points, unveiling the impact of affinity analytics in customer engagement by developing an affinity analytics research model. With a travel dataset from TripAdvisor.com, we understand semantic textual information in managers' responses and reviews. We quantify the effectiveness of manager responses responding to review points by introducing the STARS (Semantic Textual Affinity Reactions Score) algorithm based on the Sentence-BERT (Bidirectional Encoder Representations from Transformers), and our approach uses semantic contextual affinity measurements for review convergence and the alignment of manager responses with reviews. Utilizing empirical analysis with the PVAR (Panel Vector Autoregression) model, results highlight the impact of effective manager responses addressing points in product reviews on product reputation and uncover the interdependent relationships among the affinity of manager responses and product reviews, review convergence, product reputation, and industry response standards.

Furthermore, the study examines the effectiveness of managers' response styles to product reviews, a vital component in shaping product reputation, which is essential for enhancing customer engagement with the product. Consequently, the second essay focuses on the narrative persuasion style of manager responses to product reputation, aiming to uncover the impact of context-based qualitative text analytics on customer engagement. By leveraging narrative and persuasive theories, the research explores the use of narrative and persuasive writing in manager responses through Large Language Models (LLM). The results demonstrate that aligning response strategies with the issues highlighted in product reviews and employing narrative persuasion techniques in manager responses significantly enhance product reputation.

In a digital organization, analysts should focus exclusively on analysis to expedite the market entry of their solutions, thereby enhancing efficiency and effectiveness in their domain. Expanding analysts' responsibilities beyond their primary functions can dilute their concentration on core competencies. Additionally, analysts have varying technical, analytical, and domain expertise levels. Consequently, the cost, effort, and time required to develop efficient analytics solutions are high and inevitably slow to market when analysts must support critical processes outside their core competencies or have limited skills. Therefore, the third essay focuses on designing an engagement platform for analysts using digital analytics. Specifically, it focuses on the digital marketing domain and designs a novel EngageAI platform to Engage Analysts Innovatively. EngageAI seamlessly integrates the capabilities of an analytics engagement platform and an analytics application platform. EngageAI enables more strategic and informed decision-making, enhancing efficiency and increasing ROI within the digital marketing agency. A field study was also conducted at Epsilon, a division of Publicis Groupe, Inc., using business use cases. The result encourages further exploration into the design of broad-based, sustainable digital platforms for analyst engagement beyond digital marketing.

All interested students and faculty members are invited to attend.