In the current big data era, customer data are being accumulated and managed at an unprecedented rate. Examples of big marketing data range from traditional brick-and-mortar retailers (e.g., point-of-sale or POS scanner data, radio-frequency identification customer tracking data, and supply chain data) to online retailers (e.g., e-commerce transactions, mobile location-based services, clickstream, and Web traffic data), and other user-generated contents (e.g., customer review, blogging, and social networking data). Among all off-line and online data sources, POS data are the most extensive and frequently regarded as the most valuable marketing asset of retailers. / This study proposes a visual marketing analytics framework (VMAF) to analyze POS transaction data. VMAF is introduced to transform POS data into a display of products and customers on a map. A visual dynamic marketing analytics framework (VDMAF) aims to analyze the time-varying transaction patterns on a joint space map. This map presents a thousand words and allows marketers to view product–customer interaction, which enables them to perceive, compare, and derive insights intuitively to execute correct marketing actions. / VMAF and VDMAF solve two key problems in visual marketing analytics. First, we represent products and customer segments from a high-dimensional space as points on a 2D space with zero dimension reduction errors. Second, we can conduct customer segmentation, product association, and prediction analysis simultaneously, thereby allowing marketers to visualize complete results on a single map and apply these results to marketing campaigns. We apply VMAF to the POS data set (37 products and 49,027 customers) of a fast-food restaurant over a period of one month. We also employ a VDMAF to analyze the transaction data set (28,092 customers and 27 product categories) of a supermarket over a period of four months. Thereafter, we illustrate the different applications and report the results. / 在今天的大數據時代，客戶數據呈現爆炸式增長並廣泛地收集。大數據營銷例子包括從傳統的實體零售店（例如銷售點終端掃描數據、RFID 的客戶跟蹤數據、供應鏈數據）到網上零售點（電子商務交易、行動定位位置服務、點擊流數據和網站流量通數據）和其他用戶生成內容（客戶評論、博客和社交網路數據）。在眾多線下數據和線上數據中，銷售點終端掃描數據 (POS) 往往被視為對零售商最有價值的營銷資產。 / 針對處理大量營銷數據的需要，此論文提出可視化營銷分析框架 (VisualMarketing Analytics Framework – VMAF) 分析POS 的交易數據。VMAF 把POS數據轉換成營銷地圖，把產品和客戶顯示在地圖中。動態可視化營銷分析框架(Visual Dynamic Marketing Analytics Framework – VDMAF) 是分析隨時間變化的聯合空間圖。一幅畫能繪出千言萬語，並讓營銷人員以可視化方式查看產品和客戶的互動，使他們能夠感知、比較和洞察消費者，得出正確的營銷行動。 / 可視化營銷分析框架 (VMAF) 和動態可視化營銷分析框架 (VDMAF) 解決營銷兩個關鍵問題。首先，我們沒有誤差地從高維空間數據表示產品和客戶群在二維空間上。其次，我們可以同時進行客戶細分、產品關聯分析、和預測分析，由此容許營銷人員透過視象方式在同一張地圖上觀看完整的結果，並應用分析結果在營銷活動上。我們應用可視化營銷分析框架 (VMAF) 在有37個產品及49,027顧客的快餐店POS交易系統，並使用動態可視化營銷分析框架 (VDMAF)分析一家超級市場的交易數據集的28,092顧客和27個產品類別超過四個月時間。我們將舉例說明不同的應用，並報告結果。 / Chung, Yu Ho. / Thesis Ph.D. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 127-134). / Abstracts also in Chinese. / Title from PDF title page (viewed on 11, October, 2016). / Chung, Yu Ho. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_1291495 |
| Date | January 2015 |
| Contributors | Chung, Yu Ho (author.), Lau, Kin-Nam (thesis advisor.), Chinese University of Hong Kong Graduate School. Division of Business Administration. (degree granting institution.) |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography, text |
| Format | electronic resource], electronic resource, remote, 1 online resource (x, 134 leaves) : illustrations (some color), computer, online resource |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-NoDerivatives 4.0 International" License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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