基於購物旅程目的的機器學習消費者分群 -- 以R公司為例

Abstract

隨著小眾品牌的興起與網際網路的普及，消費者們更容易接觸到不同種類的品牌，找到符合自己個人化需求的產品。美容保養品的個人化需求高，不同膚質、生活習慣、對保養品的態度都會影響到消費者的保養需求與購物動機。相較過去傳統的市場調查與外部顧問調研，在大數據時代，企業需要通過更有效的手段來理解消費者的需求，找到市場痛點，從而制定更全面的行銷策略。 量化行銷是一種結合數量方法如機器學習、計量經濟到行銷領域的學科，利用進階的分析技術從數據中挖掘消費者的深層需求，從而創造資訊差來創造企業的競爭優勢。隨著消費者的購買行為逐漸覆蓋到全通路融合的線上線下行為，不同品牌開始發展會員制，試圖創造更多與消費者的接觸點．從數位應用程式、通路、電商網站等收集消費者的線上足跡，這也沈澱下許多消費者的行為與消費數據，有利於發展量化行銷與策略行銷的實際應用。 本研究旨在探討機器學習技術應用於用戶分群，以R公司保養品作為例子。我們使用了因素分析與K-means集群方法。通過實證數據分析，我們發現通過對交易數據運行因素分析有助於企業找到消費者的購買動機，且結果具有符合業務邏輯的可解釋性 ; 將消費者的購買動機做集群分析可以有效地區分用戶群體，並且能夠幫助R公司更好地了解用戶需求與理解目標消費者輪廓。此外，我們也探討了模型的優缺點以及應用限制，以期為未來的學術界相關研究與保養品業者行銷策略擬定的提供參考。

With the rise of niche brands and the prevalence of the internet, consumers are more easily exposed to various types of brands and able to find products that meet their personalized needs. Personalization is particularly important in the beauty and skincare industry, as different skin types, lifestyles, and attitudes towards skincare can affect consumers' skincare needs and shopping motivations. In the era of big data, companies need more effective means to understand consumer needs, identify pain points in the market, and develop comprehensive marketing strategies. Quantitative marketing is an interdisciplinary field that merges quantitative methods like machine learning and econometrics with marketing strategies. By leveraging sophisticated analytical techniques, it uncovers profound consumer insights from vast amounts of data, thereby creating information asymmetry to gain a competitive edge. As consumer purchasing patterns increasingly span both online and offline channels, numerous brands have embraced membership programs as a means to establish multiple touch points with their target audience. The collection of consumers' online footprints through various digital applications, channels, e-commerce platforms, and more, generates a wealth of valuable data on consumer behavior and consumption patterns. This abundance of data presents significant opportunities for the practical application of quantitative marketing and strategic marketing initiatives. This study aims to investigate the application of machine learning techniques in user segmentation, using R company's skincare products as an example. We used factor analysis and K-means clustering methods. Through empirical data analysis, we found that conducting factor analysis on transaction data can help companies find consumers' purchasing motivations, and the results are interpretable according to business logic. Cluster analysis of consumers' purchasing motivations can effectively differentiate user groups and help R company better understand user needs and profile their target consumers. Additionally, we also explored the strengths, weaknesses, and limitations of the model, with the aim to provide reference for future academic research and marketing strategies of skincare product companies.