社群媒體上之訊息貼文績效評估-以某 Facebook 旅遊社團及粉絲專頁為例

Abstract

旅遊部落客經營者經常使用 Facebook 社團或粉絲專頁面發佈訊息貼文內容，以吸 引更多粉絲參加社團之活動。目前很􏰃有研究利用開發效率評估模型來評估旅遊部落 客所經營之 Facebook 訊息貼文。本研究建立旅遊粉絲專頁及旅遊社團-Facebook 上之訊 息貼文的效率評估模型;根據效率值和客戶關注數當作雙軸來區分四個象限;利用資 料包絡分析 (Data Envelopment Analysis, DEA) 模型，建議針對效率不佳的訊息貼文進 行改善。本研究運用推敲可能性模型 (Elaboration likelihood model, ELM)，以評估訊息 貼文整理套用在 DEA 模型的測量。引用波士頓管理諮詢顧問公司 (Boston Consulting Group, BCG) 分析矩陣之方法，利用效率值與客戶關注數為兩軸，根據文獻和專家意見 選擇兩個輸入 (文字內容長度和圖片數量多寡) 和六個輸出 (觸及數、貼文互動次數、心 情數、留言數、分享數、其他點擊數) 去製作四象限分析法 (高效率–高客戶關注度∕高 效率–低客戶關注度∕低效率–高客戶關注度∕低效率–低客戶關注度)。研究者利用 FB 後臺數據資料共 234 筆，最後根據 BCG 矩陣特性給出各個訊息貼文項目之相對作法， 位於高效率–高顧客關注數之象限之項目:建議繼續保持;位於高效率–低顧客關注數 之象限之項目:增加消費者關注;位於低效率–高顧客關注數之象限之項目:提升訊息 貼文效率;位於低效率–低顧客關注數之象限之項目:取消訊息貼文推廣。The independent tour guides often use the Facebook group& fan page to post marketing messages in order to attract more fans to participate in packaged tours. However, there is only a paucity of research developed utilizing the performance assessment model to evaluate the Facebook marketing messages. This study aims to develop a performance evaluation model for travel marketing messages on the travel social network- Facebook; distinguish four quadrants based on performance score and customer attention; it suggests improvements for underperforming marketing messages based on the data envelopment analysis (DEA).The paper applied the elaboration likelihood model (ELM) to select two inputs (text and photo) and six outputs (number of people reached, post interaction, emoji, comments, shares and other clicks). These inputs and outputs were based on literature and expert opinions to identify marketing messages for packaged tours through DEA model.Using Boston Consultant Group (BCG) theory, it creates four quadrants based on performance score and customer attention. (High performance& High customer’s attention/ High performance & Low customer’s attention/ Low performance & High customer’s attention/ Low performance & Low customer’s attention). Researcher collected post marketing message information 234 items from FB insight report.Based on BCG matrix definitions, it provided solution for each quadrant as below, High performance & High customer’s attention: Keep it as benchmark; High performance & Low customer’s attention: Attract more customer’s attention; Low performance &High customer’s attention: Enhance performance; Low performance& Low customer's attention: Drop out.