在线论坛对客户购买决策的影响:机器学习方法

IF 0.7 Q3 ECONOMICS Studies in Business and Economics Pub Date : 2024-01-11 DOI:10.2478/sbe-2023-0042
Reeti Agarwal, Ankit Mehrotra
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引用次数: 0

摘要

在线论坛在影响客户购买决策过程中的作用日益重要,因此,了解客户在做出购买决策时依赖在线论坛的可能性是营销人员关注的重点。企业还应该了解客户的内向/外向性格对依赖可能性的不同影响。本研究使用因子分析作为数据还原技术,并使用分类和回归树作为机器学习技术,根据客户对内部交流舒适度(内向/外向程度)的自我认知,对客户进行分类并建立决策规则。根据客户对其人际沟通舒适度的自我感知,确定了四个群体:外向型、内向型、内向型和外向型:外向型、内向型、社交活跃型和摇摆型。对从 209 名受访者那里收集到的数据进行分析后发现,社交活跃是内向者和外向者在做出购买决策时受网络论坛影响的共同特征。本研究将有助于企业了解内向-外向程度对客户在做出购买决策时更容易受网络论坛影响的影响,从而帮助企业制定更有效的战略和更好的网络论坛相关预测模型。
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Influence of Online Forums on Customers’ Buying Decisions: A Machine Learning Approach
Online forums are becoming increasingly important in influencing customers’ buying decision process, hence understanding customers’ likelihood to rely on online forums while making buying decisions is of major concern for marketers. The companies should also be aware of how reliance-likelihood differs with the introversion/extraversion nature of customers. Using factor analysis as data reduction technique and classification and regression tree as machine learning technique, the current study categorizes customers and builds decision rules based on their self-perception related to their inter-intra communication comfort level (introversion/extraversion level). Based on customers’ self-perception of their inter-intra communication comfort level, four groups were identified as: Extroverts, Introverts, Socially Active and Vacillators. Analysis of the data collected from 209 respondents revealed that being socially active is a common trait for both introverts and extroverts in being influenced by online forums while making buying decisions. The current study will be useful for companies in understanding the effect of the level of introversion-extraversion in making customers more likely to be influenced by online forums for making buying decisions and hence will help firms in formulating more effective strategies and better predictive models related to online forums.
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来源期刊
CiteScore
1.30
自引率
16.70%
发文量
20
审稿时长
30 weeks
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