- 學位論文
結合多重對應分析與資料採礦於智慧型穿戴裝置之市場區隔與產品推薦
Integrating Multiple Correspondence Analysis with Data Mining to Achieve Market Segmentation and Product Recommendation for Smart Wearable Devices
摘要
近年來智慧型穿戴裝置逐漸嶄露頭角，多家廠商紛紛推出智慧型穿戴裝置產品，然而部份產品的銷售量卻不如預期。本研究推測其根本原因是智慧型穿戴裝置目前正處於產品發展生命週期的導入期，市場需求仍不明確，因此本研究致力於探討智慧型穿戴裝置之市場需求。以資料採礦技術找出潛在消費者的基本屬性以及吸引消費者購買的關鍵功能屬性，並針對智慧型穿戴裝置產品提出產品推薦之方法。 本研究以對應分析探討智慧型穿戴裝置產品與產品功能屬性間之關係，發現智慧手環應具備睡眠監控、心率量測、卡路里消耗紀錄、體溫量測、計步器等功能；智慧手錶應具備錄音、錄影、GPS接收器、電話撥接功能、訊息提醒、音樂播放、觸控、聲控等功能；運動手錶應具備心率量測、卡路里消耗紀錄、計步器、里程表、時速表、氣壓計等功能。接著以多重對應分析探討消費者與不同智慧型穿戴裝置產品之關係，發現智慧手環之目標市場在於40歲以上、女性、較無運動習慣之消費者；智慧手錶之目標市場在於19歲以下、男性、運動頻率普通之消費者；運動手錶之目標市場在於20~29歲、男性、運動頻率較高之消費者。 最後本研究以關聯規則與對應分析之結果進行變數篩選，再以最近鄰居法進行產品推薦，結果獲得百分之六十六以上的預測正確率。
並列摘要
Recently, new kinds of smart devices are invented and sold to the market rapidly, and we witness a rise of wearable devices. Since the release of wearable devices by companies, we found that the sales of most products are below expectation. We thought that the fundamental reason is that wearable device are still in the introduction stage of its product life cycle, and the market demand is still ambiguous. In order to find the demand of wearable device, this study uses data mining techniques to find the potential consumers’ background and the key attributes which cause them to purchase. And this study also proposed an approach to achieve product recommendation. This study conducts correspondence analysis to find the relation of function attribute and different kind of wearable device. We found that smart wristbands should be provided with sleep pattern monitor, heart rate monitor, burnt calories tracker, body temperature sensor and pedometer. Smart watches should be provided with audio recording, video recording, GPS receiver, phone function, message reminding, music player, touch screen and voice control. Sport watches should be provided with heart rate monitor, burnt calories tracker, pedometer, odometer, speedometer and barometer. We also conduct multiple conducts correspondence analysis in order to investigate the relation between consumer and different kinds of wearable devices. The following is the result of multiple conducts correspondence analysis. For smart wristbands, we suggest the manufactures should place their target market at consumers who are above 40 years old, female or exercise frequency is low. For smart watches, the target market should be placed at consumers who are under 19 years old, male or exercise frequency is normal. As for sport watch, the target market should be placed at consumers who are 20~29 years old, male or exercise frequency is high. To sum up, in variable filtering part, this study compares association rules and correspondence analysis. And in classification part, this study uses k-nearest neighbors method, with 66% of prediction accuracy, which is capable of product recommendation.