智慧領航：眼鏡零售廠商AI精準行銷策略分析-以F公司為例

Abstract

本研究希望探討人工智慧（AI）技術於眼鏡零售產業精準行銷的實務應用，並以台灣眼鏡零售業領導品牌F公司為研究對象，深入剖析其AI精準行銷策略的導入方式、執行架構及成效，以提供台灣零售產業未來發展AI精準行銷的具體參考。 首先，研究針對人工智慧與精準行銷相關的文獻進行深入探討，並建立研究所需的理論基礎與分析架構。精準行銷在數位時代已成為企業提升競爭力的重要工具，其中AI技術特別透過消費者行為數據的分析與預測，實現更精準的個人化推薦與行銷推播，有效提升消費者互動與企業營運效率。 接著，本研究針對台灣眼鏡零售產業的整體發展現況進行分析。透過PESTEL模型分析，發現產業整體受到政府積極推動數位轉型政策、高齡化社會的來臨以及科技進步帶來的快速變革，使零售業者亟需透過數位化及AI技術應用，滿足消費者日益多樣化且即時化的需求。此外，透過五力分析模型，深入探討了台灣眼鏡零售市場的內外部競爭環境，包括同業競爭激烈程度高、潛在進入者威脅中低、替代品威脅較低、買方議價能力較高及供應商議價能力中高等特性。 為更清楚地呈現AI技術應用的趨勢，本研究也針對台灣零售產業中AI精準行銷的實務應用進行案例分析，探討其如何透過顧客數據平台(CDP)、AI個人化推薦引擎、AI自動化行銷推播系統等技術，提升整體行銷效率與顧客價值，並針對研究核心個案分析F公司，該公司作為台灣眼鏡零售業的龍頭企業，於2020年正式導入AI精準行銷策略，透過建置完整的顧客數據平台，有效整合會員跨渠道的消費與行為數據，並導入AI個性化推薦引擎與智慧推播系統，成功實現會員行銷的精準化與即時化，進而提升會員回訪率與終身價值。研究發現，F公司在AI精準行銷策略的實施過程中，面臨資料整合困難、技術人才不足與隱私保護議題等挑戰，但透過完整的規劃與外部合作，克服障礙取得顯著營運績效，研究總結AI精準行銷策略成敗的關鍵因素包括完善數據整合與管理能力、持續且有效的技術投資、以及組織內部對數據驅動決策的認同實踐。 最後，本研究指出未來在AI精準行銷的研究中，應進一步探討如何平衡個人資料保護與行銷成效，並建議更多量化效益評估與跨產業的比較研究，以協助更多零售業者透過AI技術實現營運效率提升，達成永續經營與競爭優勢的長期目標。

This research investigates the application and effectiveness of artificial intelligence (AI)-driven precision marketing strategies in Taiwan's retail industry, focusing specifically on Company F as a case study. With the rapid digital transformation across various sectors, integrating AI technologies has become crucial for retailers aiming to enhance consumer engagement and improve marketing efficiency. This study explores several core AI-driven marketing techniques utilized by Company F, including Customer Data Platforms (CDP), personalized AI recommendation engines, automated AI-driven push notification systems, and intelligent Customer Relationship Management(CRM) systems. The effectiveness of these strategies was assessed by examining key performance indicators such as click-through rates, conversion rates, annual revenue growth, and average transaction value, both before and after AI implementation from 2019 to 2024. Findings indicate that after adopting AI-driven precision marketing strategies, Company F experienced significant improvements across multiple performance metrics. Notably, the introduction of personalized recommendations and automated push notifications substantially boosted consumer interaction and purchasing behavior. Additionally, AI integration markedly reduced the need for manual operations, optimizing operational efficiency and lowering associated costs. This research also identifies critical success factors for AI-driven marketing implementation, including comprehensive data integration, strategic technological investment, and a strong organizational culture oriented toward data-driven decision-making. However, the study highlights challenges related to data governance, privacy concerns, and regulatory compliance, emphasizing the need for ongoing improvements in these areas. In conclusion, the successful implementation of AI-driven precision marketing by Company F serves as a valuable model for the retail industry, demonstrating how effectively leveraging AI technologies can significantly enhance business performance and customer value. This study provides practical insights and strategic recommendations for retailers aiming to adopt AI to achieve sustained competitive advantages in an increasingly digital and data-driven marketplace.