ARTIFICIAL INTELLIGENCE SUPPORTED BEHAVIORAL SEGMENTATION AND ITS USE IN SERVICE MARKETING OF PRIVATE EDUCATION INSTITUTIONS

Successful marketing hinges on precisely defining the target audience and conveying tailored messages. In private education-where services are pedagogically sensitive and family expectations are heterogeneous-traditional demographic or psychographic segmentation often falls short. Artificial-intelligence (AI)–supported behavioral segmentation offers a data-driven alternative by clustering parents and students according to observed preferences, usage patterns, and decision motives. The aim of the study is to adapt the applications in various sectors to the education sector by taking advantage of the benefits of artificial intelligence technologies on behavioral segmentation. The study synthesizes marketing and education literature with qualitative insights from semi-structured interviews with four private school owners. Interview themes (parental motives, student learning behaviors, pricing perceptions, and value expectations) were mapped to behaviorally observable features and translated into segment definitions that AI systems can operationalize (e.g., clustering and propensity scoring). The model specifies five complementary segmentation bases: student interests, student ability (cognitive track), learning style, learning pace, and parent–student–management relationship patterns. When implemented with AI, institutions can individualize service delivery, align pricing and value communication to segment expectations, and personalize curricula and guidance. The approach reconciles pedagogical appropriateness with market demands by differentiating messages and offerings across behavior-based segments. For this purpose, a behavioral segmentation model was developed based on the opinions of four private school owners in the sector. The unique contribution of the study is that it examines a specific use of behavioral segmentation by using artificial intelligence in a common field of management, marketing and education sciences. In this framework, it is thought that it can contribute to the management and marketing functions of private educational institutions as well as the creation of educational curricula in terms of bringing together different disciplines. As a result, this study argues that educational institutions, which have been effectively using artificial intelligence for a long time, should develop communication strategies integrated with behavioral segmentation in order to gain competitive advantage.

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Publication Details

Journal
PEDAGOGY AND EDUCATION MANAGEMENT REVIEW
Published
2025-09-30
DOI
https://doi.org/10.36690/2733-2039-2025-3-40-51
Primary Topic
Organizational and Employee Performance
Type
article
Field-Weighted Citation Impact
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article

ARTIFICIAL INTELLIGENCE SUPPORTED BEHAVIORAL SEGMENTATION AND ITS USE IN SERVICE MARKETING OF PRIVATE EDUCATION INSTITUTIONS

Şebnem Yücel, Cihat Kartal, Recep Yücel
PEDAGOGY AND EDUCATION MANAGEMENT REVIEW
Organizational and Employee Performance
article

ARTIFICIAL INTELLIGENCE SUPPORTED BEHAVIORAL SEGMENTATION AND ITS USE IN SERVICE MARKETING OF PRIVATE EDUCATION INSTITUTIONS

Şebnem Yücel, Cihat Kartal, Recep Yücel
article en

Abstract

Successful marketing hinges on precisely defining the target audience and conveying tailored messages. In private education-where services are pedagogically sensitive and family expectations are heterogeneous-traditional demographic or psychographic segmentation often falls short. Artificial-intelligence (AI)–supported behavioral segmentation offers a data-driven alternative by clustering parents and students according to observed preferences, usage patterns, and decision motives. The aim of the study is to adapt the applications in various sectors to the education sector by taking advantage of the benefits of artificial intelligence technologies on behavioral segmentation. The study synthesizes marketing and education literature with qualitative insights from semi-structured interviews with four private school owners. Interview themes (parental motives, student learning behaviors, pricing perceptions, and value expectations) were mapped to behaviorally observable features and translated into segment definitions that AI systems can operationalize (e.g., clustering and propensity scoring). The model specifies five complementary segmentation bases: student interests, student ability (cognitive track), learning style, learning pace, and parent–student–management relationship patterns. When implemented with AI, institutions can individualize service delivery, align pricing and value communication to segment expectations, and personalize curricula and guidance. The approach reconciles pedagogical appropriateness with market demands by differentiating messages and offerings across behavior-based segments. For this purpose, a behavioral segmentation model was developed based on the opinions of four private school owners in the sector. The unique contribution of the study is that it examines a specific use of behavioral segmentation by using artificial intelligence in a common field of management, marketing and education sciences. In this framework, it is thought that it can contribute to the management and marketing functions of private educational institutions as well as the creation of educational curricula in terms of bringing together different disciplines. As a result, this study argues that educational institutions, which have been effectively using artificial intelligence for a long time, should develop communication strategies integrated with behavioral segmentation in order to gain competitive advantage.

PEDAGOGY AND EDUCATION MANAGEMENT REVIEW(3(21))
Selçuk University (TR), Kırıkkale University (TR)
Openalex Percentile: Top 85%
Organizational and Employee Performance
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