本研究探討數位廣告在高頻觸及與演算法優化下，可能由「促進銷售」轉為「引發反感」的廣告疲乏現象，並以「廣告優養化」作為概念框架。隨著程序化投放普及，多數從業者仍依賴經驗判斷「何時跑到極限」與「哪些指標能反映看膩」，缺乏可跨平台溝通的標準化量化工具。
本研究提出「廣告優養化指標」（Advertising Eutrophication Index, AEI），將疲乏判斷外化為可計算之形成性（formative）診斷性綜合指標。AEI 以 Frequency、CTR、VTR、VCR100、CPC 五項指標為向度，經 P1–P99 標準化後依「核心」（Frequency、CTR，佔 70%）與「輔助」（其餘，佔30%）加權整合，形成 AEI 分數與五級燈號。量化分析使用與數位廣告代理商合作取得之真實投放週次資料共 64,393 筆（以展示／影音等推送式廣告為主），用於共線性檢核以避免向度冗餘、權重敏感度與排序一致性比較，並觀察 AEI 之長期變化軌跡；另結合操作人員、廣告主與一般消費者之質性訪談，以主題分析比對 AEI 分級與實務語彙之契合度。
研究結果顯示：（一）五項指標可分別對應接觸壓力、前端興趣、觀看深度與成本壓力，排除轉換類指標有助於聚焦疲乏診斷本質。（二）在維持核心高於輔助的架構下，7:3 與 6:4 權重版本的案例排序高度一致；等權重版本則相對稀釋 Frequency 與 CTR 的診斷影響。（三）AEI 較高之週次常同時呈現高頻次、互動／意願下降與成本壓力上升，與操作人員與廣告主對「跑到極限」的描述相互呼應；一般消費者所描述之厭煩感亦多集中於較高風險分級。（四）五級燈號能呈現清楚的分級剖面與風險溝通效果，較三級分類更能支撐決策傳達與跨角色對齊。
本研究建構 AEI，將分散的成效指標與操作直覺轉化為可視化、可溝通的診斷框架，提供辨識疲乏風險與調整策略的量化依據，並促使數位廣告評估朝系統性生態診斷邁進。未來可擴增跨產業與跨平台樣本，並可結合自動化資料串接與儀表板部署，以持續提升 AEI 的應用價值。

This study examines digital advertising fatigue—a phenomenon in which ads, under high-frequency exposure and algorithmic optimization, may shift from “promoting sales” to “triggering irritation”—through the conceptual lens of “advertising eutrophication.” With the widespread adoption of programmatic delivery, many practitioners still rely on experience-based judgments to decide when campaigns have “reached their limit” and which indicators best reflect audience wear-out, while standardized quantitative tools for cross-platform diagnosis and communication remain limited.
To address this gap, the study proposes the Advertising Eutrophication Index (AEI), a computable formative, diagnostic composite that externalizes practitioners’ fatigue judgments. AEI integrates five dimensions—Frequency, Click-Through Rate (CTR), View-Through Rate (VTR), 100% Video Completion Rate (VCR100), and Cost Per Click (CPC). After P1–P99 standardization, these indicators are combined using a hierarchical weighting scheme: “core” signals (Frequency and CTR; 70%) and “auxiliary” signals (the remaining three; 30%), producing an AEI score and a five-level traffic-light grading system. Quantitative analyses draw on 64,393 weekly observations from real-world campaign data obtained through collaboration with a digital advertising agency, focusing primarily on push-based display and video formats. The analyses include multicollinearity checks to prevent indicator redundancy, sensitivity testing and rank-order consistency across alternative weighting schemes, and longitudinal tracking of AEI trajectories. In addition, semi-structured interviews with media operators, advertisers, and general consumers are analyzed thematically to assess how AEI grades align with real-world diagnostic language and judgments.
The findings suggest that: (1) the five indicators map onto distinct facets of fatigue risk—exposure pressure, early-stage engagement, viewing depth, and cost pressure—while excluding conversion metrics helps concentrate on fatigue diagnosis rather than outcome volatility; (2) within the hierarchical design, rank-ordering remains highly consistent between the 7:3 and 6:4 weighting versions, whereas an equal-weight version tends to dilute the diagnostic influence of Frequency and CTR;
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(3) higher-AEI weeks commonly coincide with higher exposure frequency, weaker engagement or willingness signals, and increased cost pressure, aligning with operators’ and advertisers’ descriptions of campaigns “hitting the limit,” while consumers’ irritation narratives tend to cluster in higher-risk grades; and (4) the five-level traffic-light system provides a clear profiling gradient and practical communication value, offering more actionable risk signaling than coarser three-level categorizations.
Overall, AEI translates scattered performance metrics and tacit operational heuristics into a structured, visualizable diagnostic framework, providing a quantitative basis for identifying fatigue risk and supporting strategy adjustments. Future research may extend AEI by incorporating broader cross-industry and cross-platform samples and by integrating automated data pipelines and dashboard deployment to enhance its practical applicability.

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