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Author: 林詠慈
Yung-Tzu Lin
Thesis Title: 以顧客數據建立購買旅程之可視化模型與應用
Visualization model and application of purchasing journey based on customer data
Advisor: 林孟彥
Meng-Yen Lin
Committee: 欒斌
曾盛恕
吳姮憓
黃振豊
Degree: 博士
Doctor
Department: 管理學院 - 管理研究所
Graduate Institute of Management
Thesis Publication Year: 2023
Graduation Academic Year: 112
Language: 中文
Pages: 57
Keywords (in Chinese): 零售數據科學全通路時代消費者洞悉顧客購買旅程關聯分析數據空間可視化工具
Keywords (in other languages): Retail Data Science, Omnichannel Era, Consumer Insights, Customer Purchase Journey, Association Analysis, Data Space, Visualization Tools
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  • 在台灣,過去四十年間產業發展和變革揭示了從傳統產業到網路世代的巨大轉變,眾多的傳統產業陸續轉型從產到銷,從B2B到零售模式的拓展,最大的差異來自於買賣模式的改變,從接單交貨到面對消費者的銷售行為,商業模式的改變非常不易,很多中小企業崛起又殞落,如同浪花般的前仆後繼。而網路的發展帶來了新的通路思維,零售從單純的實體延伸到了網路電商,進而更多新的銷售型態加入,全通路的概念就此成為每一個企業決策者的挑戰。歷經疫情,線上線下的消費行為變化更大。而企業慣用的媒體也在這個時候有了新的變革,開發顧客與及行銷溝通的成本倍增。這些衝擊讓企業重新思考必須逆轉舊思維,所有的策略與計畫都該從消費者洞悉展開。
    本研究的核心是探討如何以數據科學協助企業在營運管理及行銷溝通中,以創新的思維取代傳統的方式,透過數據科學,企業能夠更充分地掌握消費者行為,從而實現更長效的競爭力,不僅僅是單向的商品輸出。在網際網路以及智慧型載具覆蓋率如此高的時代,最重要的部分是如何藉由這些工具,在顧客跟銷售端之間建構出緊密的溝通網絡,彙整每一個接觸點所收集到的信息。從企業核心價值、品牌內涵、商品特性、功能、組合,一直到促銷規劃,如果這些資訊在銷售的過程裡,能夠十分完整地被收集進而分析,即是營運戰略的利基點,當然,這些信息可以多元發展成為數位行銷工具的應用,在操作中反覆獲得大量的顧客行為數據,進而解構顧客決策路徑,即可提升營銷的精準度。如果品牌經營者能夠優化及提升。
    此外,研究中提出了零售的銷售,商品及顧客三個面向的數據分析,以可視化工具,具體清楚的呈現顧客和商品的靜態與動態變化,例如商品購買的關聯分析和顧客消費演化的購買漏斗圖形。通過這些工具,企業能夠更深入地了解顧客的購買決策,並快速做出更有利的商業決定。進一步地探討在全通路時代,企業如何通過數據科學和創新經營來獲得差異化的競爭優勢。深度探討零售數據和顧客決策路徑可以為企業提供一個不容易被超越的競爭門檻。在大數據的時代,企業需要重新思考和策略,從消費者洞悉出發,以應對顧客購買行為的快速變化。


    Over the past forty years in Taiwan, the development and transformation of industries have revealed a significant shift from traditional sectors to the digital age. Many traditional industries have transitioned from production to sales, expanding from B2B to retail models. The biggest change has been in the buying and selling methods, moving from order fulfillment to direct consumer sales. This shift in business models has been challenging, with many small and medium-sized enterprises rising and falling like waves. The growth of the internet has introduced new channel concepts, with retail extending from physical stores to e-commerce, and the idea of omnichannel marketing has become a challenge for every decision-maker. The pandemic has further altered online and offline consumer behaviors, with the cost of customer development and marketing communication increasing significantly. These impacts have forced businesses to rethink and reverse old mindsets, with strategies and plans needing to start from consumer insights.
    The core of this study is how data science can help businesses in operational management and marketing communication, replacing traditional methods with innovative thinking. Through data science, companies can better grasp consumer behavior, achieving sustained competitiveness beyond just one-way product output. In an era of high internet and smart device penetration, the key is building a tight communication network between customers and sales, collecting and analyzing information from every touchpoint. Operational strategies can gain an edge by thoroughly collecting and analyzing information about core values, brand content, product characteristics, features, combinations, and promotion plans. Utilizing digital marketing tools and repeated customer behavior data can help deconstruct customer decision-making pathways, enhancing the precision of marketing efforts.
    Additionally, the study proposes data analysis in retail covering three aspects: sales, products, and customers. Visualization tools clearly present the static and dynamic changes of customers and products, like product purchase association analysis and the evolution of customer purchase funnels. These tools allow businesses to understand customer purchase decisions more deeply and make more advantageous business decisions. The study explores how businesses in the omnichannel era can gain differentiated competitive advantages through data science and innovative management. A deep dive into retail data and customer decision paths can provide a competitive threshold not easily surpassed by others. In the era of big data, businesses need to rethink and strategize from a consumer insight perspective, to respond to the rapid changes in customer buying behavior.

    摘要 I Abstract III 誌謝 V 目錄 VII 圖目錄 XI 表目錄 XII 第一章 緒論 1 1.1 背景與動機 1 1.2 研究目的 1 1.3 本論文貢獻 2 1.4 研究架構 2 第二章 文獻探討 4 2.1 零售數據分析及其可視化 4 2.1.1 零售數據分析 4 2.1.2 數據可視化 4 2.1.3 數據的圖形 5 2.2 購買與回購行為 6 2.2.1 購買與回顧 6 2.2.2 回購意向 6 2.2.3 回購意向的維度 7 2.3 顧客旅程的定義與範疇 8 2.3.1 顧客旅程與接觸點 8 2.3.2 顧客旅程模型的研究 8 2.3.3 顧客旅程的服務流程 9 2.4 購買行為漏斗 10 2.4.1 行銷漏斗 10 2.4.2 轉化漏斗 10 2.4.3 購買漏斗 11 第三章 零售數據分析的問題 12 3.1 商品與顧客 12 3.1.1 商品(Product) 12 3.1.2 顧客(Customer) 12 3.1.3 CP平面 13 3.2 銷售行為與SPC空間 14 3.2.1 銷售(Sales) 14 3.2.2 SPC模型 15 3.2.3 零售交易數據庫 15 3.3 顧客旅程的數據呈現 17 3.3.1 數據流轉換 17 3.3.2 面向顧客的樞軸轉換 18 3.3.3 數據空間中的顧客接觸點 19 3.4 顧客價值的應用 19 3.4.1 顧客價值 19 3.4.2 RFM模型 19 3.4.3 FM顧客價值表 20 第四章 顧客購買連帶關係的CPA圖 21 4.1 數據關聯的問題 21 4.1.1 關聯規則 21 4.1.2 關聯規則的(客觀)興趣衡量 22 4.1.3 關聯規則的可視化 23 4.2 購買數據關連的視覺化 23 4.2.1 既有的關聯規則可視化技術 23 4.2.2 本研究的關聯規則可視化基礎 24 4.2.3 CPA圖的圖形設計(GD)原則 25 4.3 購買關聯(CPA)圖 26 4.3.1 CPA圖的建構方法及呈現 26 4.3.2 CPA圖的決策支持(DS)原則 28 4.4 CPA圖的應用實例 29 4.4.1 案例說明 29 4.4.2 B系列的兩個顧客群的購買關聯比較 29 4.4.3 CPA 圖形的商品開發應用 31 第五章 顧客購買動態演化的CPEG圖 34 5.1 演化理論 34 5.1.1 演化理論的要素 34 5.1.2 演化理論應用到零售數據分析 34 5.2 顧客購買的演化理論 35 5.2.1 零售交易的顧客消費基因 35 5.2.2 零售交易的顧客消費物種與世代 36 5.2.3 零售交易的顧客消費基因演化運算 36 5.3 顧客購買的CPEG動態演化 38 5.3.1 顧客購買演化圖CPEG的建構方法 38 5.3.2 CPEG的說例 39 5.4 CPEG圖的應用實例 40 5.4.1 零售業實例說明 40 5.4.2 所有顧客的CPEG圖 40 5.4.3 SVIP的CPEG圖 43 第六章 總結與未來展望 46 6.1 總結 46 6.2 未來展望 47 參考文獻 49

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