Predicting Customer Behavior with Combination of Structured and Unstructured Data

被引:3
|
作者
Afolabi, Ibukun T.
Worlu, Rowland E.
Adebayo, O. P.
Jonathan, Oluranti
机构
来源
3RD INTERNATIONAL CONFERENCE ON SCIENCE AND SUSTAINABLE DEVELOPMENT (ICSSD 2019): SCIENCE, TECHNOLOGY AND RESEARCH: KEYS TO SUSTAINABLE DEVELOPMENT | 2019年 / 1299卷
关键词
Data Mining; Classification Algorithm; Marketing; e-marketing; m-marketing; Structured data; Unstructured data;
D O I
10.1088/1742-6596/1299/1/012041
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Presently, there are numerous e-marketing and m-marketing mediums that exist such as YouTube, SMS, What Sapp, Google, twitter, yahoo, Facebook, LinkedIn, email and personal blogs. These mediums are beginning to be used for marketing purposes, particularly by the SMEs in Nigeria. The aim of this research is to address the problem of deciding which of the mediums mentioned above is mostly appropriate to target customer of a particular SME and also to discover the type of data that is most appropriate for analysis in making this decision. In order to achieve this, data was gathered by administering questionnaires and pre-processed based on structured and unstructured data sources. The J48 decision tree classification algorithm was used to mine the data, relevant predictions were made from the structured and unstructured data and the results were evaluated. The results revealed that predicting from unstructured data expresses more of popular opinion, so decision can start from unstructured results and be fined tuned or validated with predicting from structured data. Though structured prediction appears to be better than unstructured, unstructured prediction is still very valuable in situations where there are no structured data such as analysing text messages. Also, Models developed for predicting customer behaviour as regards the marketing channels studied, will form the foundation for marketing decision making, in small and medium businesses in Nigeria.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] The laws of choice: Predicting customer behavior
    Agarwal, J
    Carbno, C
    JOURNAL OF MARKETING RESEARCH, 1998, 35 (04) : 502 - 503
  • [32] The laws of choice: Predicting customer behavior
    Tolley, S
    JOURNAL OF ADVERTISING RESEARCH, 2001, 41 (02) : 64 - 66
  • [33] Structured multigrid agglomeration on a data structure for unstructured meshes
    Hannemann, V
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, 2002, 40 (3-4) : 361 - 368
  • [34] Adding Structured Data in Unstructured Web Chat Conversation
    Wu, Min
    Bhowmick, Arin
    Goldberg, Joseph H.
    UIST'12: PROCEEDINGS OF THE 25TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, 2012, : 75 - 82
  • [35] A Proposed Technique for Conversion of Unstructured Agro-data to Semi-structured or Structured data
    Sambrekar, Kuldeep
    Rajpurohit, Vijay. S.
    Joshi, Jui
    2018 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA), 2018,
  • [36] Structured and unstructured modulation and reconstruction of DoFP image data
    Flannery, Connor J.
    Li, Qiwei
    Kurtz, Joseph
    Alenin, Andrey S.
    Tyo, J. Scott
    POLARIZATION SCIENCE AND REMOTE SENSING X, 2021, 11833
  • [37] Network analytics of structured and unstructured data: an evolutionary solution
    Lichtarge, Olivier
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2016, 252
  • [38] Unstructured data research in business: Toward a structured approach
    de Haan, Evert
    Padigar, Manjunath
    El Kihal, Siham
    Kubler, Raoul
    Wieringa, Jaap E.
    JOURNAL OF BUSINESS RESEARCH, 2024, 177
  • [39] Converting unstructured and semi-structured data into knowledge
    Rusu, Octavian
    Halcu, Ionela
    Grigoriu, Oana
    Neculoiu, Giorgian
    Sandulescu, Virginia
    Marinescu, Mariana
    Marinescu, Viorel
    2013 ROEDUNET INTERNATIONAL CONFERENCE (ROEDUNET): NETWORKING IN EDUCATION, 11TH EDITION, 2013,
  • [40] Predictive Business Process Monitoring with Structured and Unstructured Data
    Teinemaa, Irene
    Dumas, Marlon
    Maria Maggi, Fabrizio
    Di Francescomarino, Chiara
    BUSINESS PROCESS MANAGEMENT, BPM 2016, 2016, 9850 : 401 - 417