本研究探討了社群偵測技術在精準行銷領域的應用潛力與成效。本研究的方法與一般利用分群演算法將顧客和商品進行分群的傳統方式不同，我們根據顧客購買記錄計算其與商品之間的購買次數。根據這些資訊，建立了顧客與產品之間的二分圖，然後在此基礎上運用社群偵測技術將顧客與產品分群，目標是分到同群的顧客與產品代表該群顧客對同群產品的偏好勝過對它群產品的偏好。為了提升社群偵測分群的精確度和適用性，本研究在基礎社群偵測演算法中引入了解析度參數。通過逐步提高解析度，我們找到較佳的分群方式，減少每個分群中的顧客與商品的數量，讓分群結果更加清晰易懂，最重要的是新的分群結果顯著提高了顧客回應率，因而驗證了社群偵測技術在精準定位顧客對產品之主要喜好，同時提升行銷績效上的價值。

This study explores the potential and effectiveness of community detection technology in the field of precision marketing. Unlike traditional methods that use clustering algorithms to categorize customers and products, our approach calculates the purchase frequency between customers and products based on customer purchase records. Utilizing this information, we construct a bipartite graph between customers and products. Then, building on this foundation, we apply community detection technology to cluster customers and products. The goal is to group customers and products in such a way that customers in the same group have a stronger preference for products in their group compared to products in other groups. To enhance the accuracy and applicability of clustering based on community detection, this study introduces a resolution parameter into the basic community detection algorithm. By gradually increasing the value of resolution, we discover new ways of clustering, reducing the number of customers and products in each group, making the clustering results clearer and more understandable. Most importantly, the new clustering results significantly improve customer response rates, thereby validating the value of community detection technology in precisely targeting customers' primary preferences for products while enhancing marketing performance.

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