Please wait a minute...
Advanced Search
数据分析与知识发现  2018, Vol. 2 Issue (4): 1-9     https://doi.org/10.11925/infotech.2096-3467.2017.1118
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
移动购物用户信息浏览特征及对购买的影响研究*——基于移动电商APP点击流日志的分析
周翔, 张鹏翼(), 王军
北京大学信息管理系 北京 100871
Impacts of Information Browsing Behaviors on Mobile Shopping: Case Study of Commerce APP Click Stream Analysis
Zhou Xiang, Zhang Pengyi(), Wang Jun
Department of Information Management, Peking University, Beijing 100871, China
全文: PDF (715 KB)   HTML ( 6
输出: BibTeX | EndNote (RIS)      
摘要 

目的】针对移动购物用户在进行商品信息浏览时的特征对购买的影响进行研究。【方法】采用日志分析方法, 对某移动电商APP的2 752名用户在2015年3月的44 932 715条日志进行分析。【结果】会话内浏览复杂度越高、浏览广度越广、浏览深度越深, 会话内用户越有可能产生购买行为; 单会话购物过程中浏览复杂度高于多会话购物过程; 多任务购物过程中浏览复杂度高于单任务购物过程。【局限】基于某一移动电商APP的研究结论普适性有待检验。【结论】移动购物中用户商品信息浏览特征及对购买的影响研究有助于理解移动购物用户的信息浏览行为及其与购买行为之间的关系。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
周翔
张鹏翼
王军
关键词 移动电商信息浏览购买决策日志分析    
Abstract

[Objective] This paper analyzes the mobile shoppers’ information browsing behaviors, aiming to find their influences on purchasing decisions. [Methods] We studied 44,932,715 browsing logs generated by 2,752 users of a mobile shopping APP in March, 2015. [Results] We found that users’ purchasing behaviors were affected by the complexity, breadth and depth of browsing patterns. The complexity of single-session was higher than the multi-session ones, while the complexity of multi-task process was higher than their single-task counterparts. [Limitations] More research is needed to examine data from other m-commerce platforms. [Conclusions] Mobile shoppers’ information browsing behaviors could help us better understand purchasing decisions.

Key wordsM-Commerce    Browsing Behavior    Purchase Decision    Log Analysis
收稿日期: 2017-11-08      出版日期: 2018-05-11
ZTFLH:  G252.0  
基金资助:*本文系国家自然科学基金项目“面向电子商务生态平衡的目录导购机制研究”(项目编号: 71373015)的研究成果之一
引用本文:   
周翔, 张鹏翼, 王军. 移动购物用户信息浏览特征及对购买的影响研究*——基于移动电商APP点击流日志的分析[J]. 数据分析与知识发现, 2018, 2(4): 1-9.
Zhou Xiang,Zhang Pengyi,Wang Jun. Impacts of Information Browsing Behaviors on Mobile Shopping: Case Study of Commerce APP Click Stream Analysis. Data Analysis and Knowledge Discovery, 2018, 2(4): 1-9.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1118      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I4/1
购物过程所含会话数 购物过程数 所占百分比 累计百分比
1 11 516 29.83% 29.83%
2 6 984 18.09% 47.92%
3 4 771 12.36% 60.27%
4 3 359 8.70% 68.97%
5 2 355 6.10% 75.07%
6 1 850 4.79% 79.86%
7 1 316 3.41% 83.27%
8 1 076 2.79% 86.06%
9 869 2.25% 88.31%
≥10 4 513 11.69% 100.00%
  购物过程所含会话数分布
购物过程所含任务数 购物过程数 所占百分比 累计百分比
1 20 948 54.26% 54.26%
2 8 397 21.75% 76.01%
3 4 011 10.39% 86.39%
4 2 104 5.45% 91.84%
5 1 199 3.11% 94.95%
6 760 1.97% 96.92%
7 421 1.09% 98.01%
8 255 0.66% 98.67%
9 175 0.45% 99.12%
10 339 0.88% 100.00%
  购物过程所含购物任务数分布
浏览商品详情页数 会话数 所占百分比 累计百分比
1 18 056 19.11% 19.11%
2 9 209 9.75% 28.86%
3 11 467 12.14% 41.00%
4 5 380 5.69% 46.69%
5 4 225 4.47% 51.16%
6-10 16 454 17.42% 68.58%
11-20 14 006 14.83% 83.41%
21-30 6 384 6.76% 90.16%
31-50 5 499 5.82% 95.98%
>50 3 795 4.02% 100.00%
  会话浏览商品详情页数分布
  涉及不同渠道来源的会话数量分布
浏览店铺数 会话数 所占百分比 累计百分比
1 21 395 22.65% 22.65%
2 10 212 10.81% 33.46%
3 11 602 12.28% 45.74%
4 5 633 5.96% 51.70%
5 4 381 4.64% 56.34%
6 5 094 5.39% 61.73%
7 3 075 3.25% 64.98%
8 2 659 2.81% 67.80%
9 3 006 3.18% 70.98%
10 2 064 2.18% 73.16%
11-20 12 985 13.74% 86.91%
>20 12 369 13.09% 100.00%
  会话浏览商品对应店铺数分布
浏览商品种类数 会话数 所占百分比 累计百分比
1 20 059 21.23% 21.23%
2 11 753 12.44% 33.67%
3 11 802 12.49% 46.16%
4 7 103 7.52% 53.68%
5 6 059 6.41% 60.10%
6 5 550 5.87% 65.97%
7 4 327 4.58% 70.55%
8 3 670 3.88% 74.44%
9 3 225 3.41% 77.85%
10 2 708 2.87% 80.72%
11-20 13 264 14.04% 94.76%
>20 4 955 5.24% 100.00%
  会话浏览商品对应商品种类数分布
每个店铺平均浏览商品数 会话数 所占百分比 累计百分比
n≤1 59 918 63.42% 63.42%
1<n≤2个 28 556 30.23% 93.65%
2<n≤3个 2 987 3.16% 96.81%
n>3 3 014 3.19% 100.00%
  会话内店铺平均浏览商品数分布
平均每个商品种类浏览商品数 会话数 所占百分比 累计百分比
n≤1 43 861 46.43% 46.43%
1<n≤2个 36 424 38.55% 84.98%
2<n≤3个 9 782 10.35% 95.33%
n>3 4 408 4.67% 100.00%
  会话内商品种类平均浏览商品数分布
Z 序列趋势 购物过程数 占比
Z≤-1.96 下降趋势 2 324 8.15%
-1.96<Z<1.96 无明显趋势 18 501 64.86%
Z≥1.96 上升趋势 7 698 26.99%
  浏览复杂度变化趋势分布
[1] 中国互联网络信息中心. 第39次中国互联网络发展状况统计报告[EB/OL]. [2017-11-08]. .
[1] (CNNIC. The 39th China Internet Network Development State Statistic Report [EB/OL]. [2017-11-08].
[2] 艾瑞咨询. 2017中国移动电商行业研究报告[EB/OL]. [2017-12-20]. .
[2] (iResearch. 2017 China Mobile Commerce Industry Report [EB/OL]. [2017-12-20].
[3] Nalchigar S, Weber I, Lak P, et al.A Large-Scale Study of Online Shopping Behavior [OL]. arXiv PreArXiv Preprint, arXiv: 1212.5959.
doi: 10.1145/2938503.2938534
[4] Gao J, Zhang C, Wang K, et al.Understanding Online Purchase Decision Making: The Effects of Unconscious Thought, Information Quality, and Information Quantity[J]. Decision Support Systems, 2012, 53(4): 772-781.
doi: 10.1016/j.dss.2012.05.011
[5] 邓发云, 林志新. 网络消费者购买决策的信息行为研究[J]. 商业时代, 2013(36): 70-72.
[5] (Deng Fayun, Lin Zhixin.Information Behavior Research of Online Consumer Purchasing Decisions[J]. Commercial Times, 2013(36): 70-72.)
[6] 许应楠. 消费者在线购物决策中的商品参数浏览偏好分析——以数码相机为例[J]. 现代图书情报技术, 2012(12): 52-57.
[6] (Xu Yingnan.Analysis of Commodity Parameters Browsing Preference in Consumer’s Online Shopping Decision-making——Taking Digital Camera for Example[J]. New Technology of Library and Information Service, 2012(12): 52-57.)
[7] Park C H, Kim Y G.Identifying Key Factors Affecting Consumer Purchase Behavior in an Online Shopping Context[J]. International Journal of Retail & Distribution Management, 2003, 31(1): 16-29.
doi: 10.1108/09590550310457818
[8] Moe W W.Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream[J]. Journal of Consumer Psychology, 2003, 13(1/2): 29-39.
doi: 10.1207/S15327663JCP13-1&2_03
[9] 袁兴福, 张鹏翼, 刘洪莲,等. 基于点击流的电商用户会话建模[J]. 图书情报工作, 2015, 59(1): 119-126.
doi: 10.13266/j.issn.0252-3116.2015.01.016
[9] (Yuan Xingfu, Zhang Pengyi, Liu Honglian, et al.Modeling E-commerce User Session Behaviors Based on Click-through Sequences[J]. Library and Information Service, 2015, 59(1): 119-126.)
doi: 10.13266/j.issn.0252-3116.2015.01.016
[10] 袁兴福, 张鹏翼, 王军. 电商用户“状态-行为”建模及其在商品信息搜索行为分析的应用[J]. 现代图书情报技术, 2015(6): 93-100.
[10] (Yuan Xingfu, Zhang Pengyi, Wang Jun.“State-Behavior” Modeling and Its Application in Analyzing Product Information Seeking Behavior of E-commerce Websites Users[J]. New Technology of Library and Information Service, 2015(6): 93-100.)
[11] 刘洪莲, 张鹏翼, 王军. 多会话商品信息搜寻行为、情境及影响因素研究[J]. 现代图书情报技术, 2016(4): 1-7.
[11] (Liu Honglian, Zhang Pengyi, Wang Jun.Multi-session Product Information Seeking Behaviors, Motivation, and Influencing Factors[J]. New Technology of Library and Information Service, 2016(4): 1-7.)
[12] 刘洪莲, 张鹏翼, 王军, 等. 多会话网络购物商品信息搜寻行为研究[J]. 图书情报工作, 2015, 59(14): 117-125.
doi: 10.13266/j.issn.0252-3116.2015.14.017
[12] (Liu Honglian, Zhang Pengyi, Wang Jun, et al.Product Information Seeking Behavior of Multi-session Online Shopping Tasks[J]. Library and Information Service, 2015, 59(14): 117-125.)
doi: 10.13266/j.issn.0252-3116.2015.14.017
[13] Kooti F, Lerman K, Aiello L M, et al.Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior[C]//Proceedings of ACM International Conference on Web Search and Data Mining. 2015: 205-214.
[14] Raman A.Web Services and e-Shopping Decisions: A Study on Malaysian e-Consumer[J]. International Journal of Computer Applications, 2011, 2: 54-60.
[15] Hernández B, Jiménez J, Martín M J.Age, Gender and Income: Do They Really Moderate Online Shopping Behaviour?[J]. Online Information Review, 2011, 35(1): 113-133.
doi: 10.1108/14684521111113614
[16] Moshrefjavadi M H, Dolatabadi H R, Nourbakhsh M, et al.An Analysis of Factors Affecting on Online Shopping Behavior of Consumers[J]. International Journal of Marketing Studies, 2012, 4(5). DOI: 10.5539/ijms.v4n5p81.
doi: 10.5539/ijms.v4n5p81
[17] 云小风. 消费者在线购物车放弃行为影响因素的实证研究[J]. 图书情报工作, 2011, 55(2): 139-142, 148.
[17] (Yun Xiaofeng.An Empirical Research on the Determinants of Consumers’ Online Shopping Cart Abandonment[J]. Library and Information Service, 2011, 55(2): 139-142,148.)
[18] 魏占宏, 张阳东. 第三方信息对网络购物决策的影响分析——以淘宝网为例[J]. 价格理论与实践, 2009(9): 71-72.
[18] (Wei Zhanhong, Zhang Yangdong.Influence of Third-party Information on Purchase Decisions: An Analysis of Taobao[J]. Price:Theory & Practice, 2009(9): 71-72.)
[19] 陈亚菲, 王展鸿, 王霞. 影响网上购物的不确定性因素分析[J]. 计算机工程与应用, 2014, 50(4): 114-117.
doi: 10.3778/j.issn.1002-8331.1212-0148
[19] (Chen Yafei, Wang Zhanhong, Wang Xia.Analysis of Uncertain Influence Factors of Online-shopping[J]. Computer Engineering and Applications, 2014, 50(4): 114-117. )
doi: 10.3778/j.issn.1002-8331.1212-0148
[20] Moe W W, Fader P S.Capturing Evolving Visit Behavior in Clickstream Data[J]. Journal of Interactive Marketing, 2004, 18(1): 5-19.
doi: 10.1002/dir.10074
[21] Buckinx W R, Van den Poel D. Predicting Online Purchasing Behavior [R]. Working Papers of Faculty of Economics & Business Administration Ghent University Belgium, 2003, 166: 557-575.
[22] Park J K, Chung H.Consumers’ Travel Website Transferring Behaviour: Analysis Using Clickstream Data-time, Frequency, and Spending[J]. Service Industries Journal, 2009, 29(10): 1451-1463.
doi: 10.1080/02642060903026254
[23] Yeo J, Kim S, Koh E, et al.Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site[C]// Proceedings of International Conference Companion on World Wide Web, 2016: 133-134.
[24] 张文君, 王军, 徐山川. 电商用户需求状态的聚类分析——以淘宝网女装为例[J]. 现代图书情报技术, 2015(3): 67-74.
[24] (Zhang Wenjun, Wang Jun, Xu Shanchuan.The Probing of E-commerce User Need States by Page Cluster Analysis——An Empirical Study on Women’s Clothes from Taobao.com[J]. New Technology of Library and Information Service, 2015(3): 67-74.)
[25] He D, Göker A.Detecting Session Boundaries from Web User Logs[J]. Lannée Psychologique, 2000, 100(4): 585-627.
doi: 10.3406/psy.2000.28665
[26] Cai S, Xu Y, Yu J.The Effects of Web Site Aesthetics and Shopping Task on Consumer Online Purchasing Behavior[C] // Proceedings of CHI’08 Extended Abstracts on Human Factors in Computing Systems, 2008: 3477-3482.
[1] 池毛毛,潘美钰,王伟军. 线索一致性对共享住宿平台用户购买决策的影响研究:房客文本信息和房源图片信息的交互效应*[J]. 数据分析与知识发现, 2020, 4(11): 74-83.
[2] 张鹏翼, 王丹雪, 焦祎凡, 陈秀雨, 王军. 基于用户浏览日志的移动购买预测研究*[J]. 数据分析与知识发现, 2018, 2(1): 51-63.
[3] 陈和. 运用开源软件Logstash和ElasticSearch实现DSpace日志实时统计分析[J]. 现代图书情报技术, 2015, 31(5): 88-93.
[4] 陈勇, 李红莲, 吕学强. 网络用户搜索行为特征分析[J]. 现代图书情报技术, 2014, 30(12): 10-17.
[5] 张学宏. 北京大学图书馆的主页日志分析[J]. 现代图书情报技术, 2005, 21(5): 81-83.
[6] 姜传菊. 网络日志分析在网络安全中的作用[J]. 现代图书情报技术, 2004, 20(12): 58-60.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn