The Research of Using O2O IOT Context-Aware Platform in Beauty Retail Store to Predict Consumer Purchase Intentions

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Project title

Code/計畫編號

MOST103-2622-E019-005-CC3

Translated Name/計畫中文名

運用美妝零售賣場之O2O物聯網情境感知平台推測消費者購物意圖之研究

Project Coordinator/計畫主持人

Meng-Ru Tu

Funding Organization/主管機關

National Science and Technology Council

Website

https://www.grb.gov.tw/search/planDetail?id=8399128

Year

2014

Start date/計畫起

01-11-2014

Expected Completion/計畫迄

31-10-2015

Bugetid/研究經費

446千元

ResearchField/研究領域

管理科學
化學工程

近年來，因智慧型手機普及以及物聯網 (Internet of Thing, IoT)的興起，一種新型的行動商務消費模式 Online to Offline（O2O）已快速在市場上發展起來，利用手機App來從事行銷各種產品或服務的企業越來越多。因此，讓消費者在任何時間及地點透過手機或是具備物聯網裝置的數位看板來進行購物已成為行動商務的重要趨勢。因應上述的趨勢許多的美妝與零售商家在附近交通樞紐或大賣場設置互動式數位看板來吸引旅客或行人前往附近的商店購物，所以若是能在消費者與物聯網數位看板互動的同時主動推薦消費者所喜愛的商品，這將減少消費者尋找商品的時間，增加產品在O2O模式下的銷售的機會。本研究試圖利用貝氏網絡模型來建構O2O物聯網情境感知平台之商品推薦系統，並利用產品組合集合的資料來建構貝氏網絡的結構，最後該貝氏網路可根據消費者與環境參數來做產品的推薦。本研究期望將此產品推薦的方法運用在交通樞紐購物中心的互動數位看板系統。我們利用某數位看板系統廠商所提供的半年數位看板互動交易資料，來分析並透過我們的研究方法進一步地去改善推播精準度。實驗結果顯示本研究所提出的數位看板產品推薦方法優於廠商現有的方法，本研究的成果將可做為未來發展數位看板產品推薦系統之參考。"In recent years, the popularity of using smart phones and the rise of the Internet of Things (Internet of Thing, IoT) have contributed to the rise of a new type of mobile commerce called Online to Offline (O2O), which allows companies around the world to market their products and services through mobile phone APP. Thus, it is becoming an important trend for mobile commerce that consumers can engage in shopping activities through smart phone or interactive digital signage at any time, any place. In response to the above mobile commerce trend, many beauty and retail stores have installed interactive digital signage near the transportation hub or shopping center to attract visitors or pedestrians to shopping at nearby stores. The proactive product recommendation to consumers when they are interacting with a digital signage can reduce the time to search product and increase the chances of the selling products under the O2O shopping model. This research proposes an O2O IoT based context-aware product recommendation system based on Bayesian network model. This Bayesian network structure then can serve as a product recommendation engine to recommend product to consumers based on consumer and environment parameters. This research combines two data mining methods to develop a product recommendation method for interactive digital signage systems installed at the area near a transportation hub. The research data is a 6 month digital signage transaction data set provided by a digital signage system company. We utilize these data to build our model to improve the system’s prediction power. The experimental results show that our proposed model outperforms the existing product recommendation model. The results of this research can serve as reference for developing future digital signage product recommendation systems. "

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The Research of Using O2O IOT Context-Aware Platform in Beauty Retail Store to Predict Consumer Purchase Intentions

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