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Analyzing sequential purchasing behavior and prioritizing brands in loyalty programs
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AI
DOI：10.1108/jpbm-12-2023-4870.png)
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Abstract
En 中文
<jats:sec>
<jats:title>Purpose</jats:title>
<jats:p>This study aims to develop a novel model to quantify the strength of customer loyalty based on sequential purchasing behaviors within loyalty programs. It also ranks brands to generate a comprehensive ranking list for each customer segment.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Design/methodology/approach</jats:title>
<jats:p>This study proposes a network-based model to record customers’ purchasing history, incorporating purchase frequency, time-decay and discount effects. This study also introduces a modified Hyperlink-Induced Topic Search algorithm to rank brands within each customer segment.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Findings</jats:title>
<jats:p>This study analyzes the transactional data set of a multi-industry loyalty program to identify future brand choices for each customer segment.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Research limitations/implications</jats:title>
<jats:p>The proposed methodology does not consider point redemption or expenses incurred for a specific brand. The methodology also does not assume any specific distribution for purchasing time or include predictive analysis.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Practical implications</jats:title>
<jats:p>Loyalty program managers can design marketing strategies based on representative transaction sequence networks from customer segments. They can also identify popular or influential brands. Cross-selling strategies can be developed using information about the brands most likely to be purchased subsequently.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Originality/value</jats:title>
<jats:p>To the best of the authors’ knowledge, this is the first study to propose a network-based model to quantify the strength of customer loyalty from sequential purchasing behaviors. This study also introduces a novel methodology for segmenting customers and proposes a modified Hyperlink-Induced Topic Search algorithm to rank brands.</jats:p>
</jats:sec>
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