Design of marketing scenario planning based on business big data analysis

Seungkyun Hong, Sungho Shin, Young min Kim, Choong Nyoung Seon, Jung ho Um, Sa kwang Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

As the amount and the type of data for business decision making are rapidly increasing, the importance of big data analytics is gradually critical for making effective business strategy. However, big data analytics based decision making systems basically requires distributed parallel computing capability in order to make timely business strategy recommendation via processing huge amount unstructured as well as structured business data. We introduce a big data analytics system for automatic marketing scenario planning based on big data platform software such as Hadoop and HBase. The analytics methodology for scenario planning is based on prescriptive analytics which is the most advance methodology consisting of generation of business scenarios and their optimization, among the three analytics of descriptive, predictive, and prescriptive analytics. Additionally, we developed a prototype of marketing scenario planning system and its graphical user interface, as well as the system architecture based on Hadoop eco-system based distributed parallel computing platform.

Original languageEnglish
Title of host publicationHCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings
EditorsFiona Fui-Hoon Nah, Chuan-Hoo Tan
PublisherSpringer Verlag
Pages585-592
Number of pages8
ISBN (Print)9783319208947
DOIs
StatePublished - 2015 Jan 1
Event2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015 - Los Angeles, United States
Duration: 2015 Aug 22015 Aug 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9191
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015
CountryUnited States
CityLos Angeles
Period15/08/215/08/7

Fingerprint

Marketing
Data analysis
Planning
Scenarios
Industry
Parallel processing systems
Decision making
Parallel Computing
Distributed Computing
Decision Making
Graphical user interfaces
Methodology
Graphical User Interface
Business
Design
Big data
System Architecture
Ecosystem
Recommendations
Processing

Keywords

  • Big data
  • Business intelligence
  • Marketing scenario
  • Prescriptive analytics
  • Scenario optimization

Cite this

Hong, S., Shin, S., Kim, Y. M., Seon, C. N., Um, J. H., & Song, S. K. (2015). Design of marketing scenario planning based on business big data analysis. In F. F-H. Nah, & C-H. Tan (Eds.), HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings (pp. 585-592). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9191). Springer Verlag. https://doi.org/10.1007/978-3-319-20895-4_54
Hong, Seungkyun ; Shin, Sungho ; Kim, Young min ; Seon, Choong Nyoung ; Um, Jung ho ; Song, Sa kwang. / Design of marketing scenario planning based on business big data analysis. HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. editor / Fiona Fui-Hoon Nah ; Chuan-Hoo Tan. Springer Verlag, 2015. pp. 585-592 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Hong, S, Shin, S, Kim, YM, Seon, CN, Um, JH & Song, SK 2015, Design of marketing scenario planning based on business big data analysis. in FF-H Nah & C-H Tan (eds), HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9191, Springer Verlag, pp. 585-592, 2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015, Los Angeles, United States, 15/08/2. https://doi.org/10.1007/978-3-319-20895-4_54

Design of marketing scenario planning based on business big data analysis. / Hong, Seungkyun; Shin, Sungho; Kim, Young min; Seon, Choong Nyoung; Um, Jung ho; Song, Sa kwang.

HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. ed. / Fiona Fui-Hoon Nah; Chuan-Hoo Tan. Springer Verlag, 2015. p. 585-592 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9191).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Design of marketing scenario planning based on business big data analysis

AU - Hong, Seungkyun

AU - Shin, Sungho

AU - Kim, Young min

AU - Seon, Choong Nyoung

AU - Um, Jung ho

AU - Song, Sa kwang

PY - 2015/1/1

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N2 - As the amount and the type of data for business decision making are rapidly increasing, the importance of big data analytics is gradually critical for making effective business strategy. However, big data analytics based decision making systems basically requires distributed parallel computing capability in order to make timely business strategy recommendation via processing huge amount unstructured as well as structured business data. We introduce a big data analytics system for automatic marketing scenario planning based on big data platform software such as Hadoop and HBase. The analytics methodology for scenario planning is based on prescriptive analytics which is the most advance methodology consisting of generation of business scenarios and their optimization, among the three analytics of descriptive, predictive, and prescriptive analytics. Additionally, we developed a prototype of marketing scenario planning system and its graphical user interface, as well as the system architecture based on Hadoop eco-system based distributed parallel computing platform.

AB - As the amount and the type of data for business decision making are rapidly increasing, the importance of big data analytics is gradually critical for making effective business strategy. However, big data analytics based decision making systems basically requires distributed parallel computing capability in order to make timely business strategy recommendation via processing huge amount unstructured as well as structured business data. We introduce a big data analytics system for automatic marketing scenario planning based on big data platform software such as Hadoop and HBase. The analytics methodology for scenario planning is based on prescriptive analytics which is the most advance methodology consisting of generation of business scenarios and their optimization, among the three analytics of descriptive, predictive, and prescriptive analytics. Additionally, we developed a prototype of marketing scenario planning system and its graphical user interface, as well as the system architecture based on Hadoop eco-system based distributed parallel computing platform.

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SN - 9783319208947

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 585

EP - 592

BT - HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings

A2 - Nah, Fiona Fui-Hoon

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PB - Springer Verlag

ER -

Hong S, Shin S, Kim YM, Seon CN, Um JH, Song SK. Design of marketing scenario planning based on business big data analysis. In Nah FF-H, Tan C-H, editors, HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. Springer Verlag. 2015. p. 585-592. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-20895-4_54