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A novel approach to optimizing customer profiles in relation to business metrics

IAES International Journal of Artificial Intelligence · Vol. 13 No. 1 (2024) · pp. 440–450

DOI
10.11591/ijai.v13.i1.pp440-450
ISSN
2252-8938, 2089-4872
Published
2024-03-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.

Keywords

Collaboration network of the authors

40 authors · 88 co-authorship links · 23 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Sumatera Utara
  • Binus University
  • Medan Area University
  • Andalas University
  • Telkom University
  • Universitas Bina Sarana Informatika
  • Universitas Raharja
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Herman MawengkangUniversitas Sumatera Utara4
Rahmad B. Y. SyahAffiliation unknown2
Al-Khowarizmi Al-KhowarizmiAffiliation unknown2
Arif Ridho LubisAffiliation unknown2
Habib SatriaMedan Area University2
Syafii SyafiiAndalas University2
Rahmad SyahMedan Area University2
Al KhowarizmiAffiliation unknown2
Dadang PriyantoUniversitas Sumatera Utara2
Mochamad WahyudiUniversitas Bina Sarana Informatika2
Hengki Tamando SihotangAffiliation unknown2
Desi VinsensiaAffiliation unknown2
Poltak SihombingUniversitas Sumatera Utara2
Edi AbdurachmanBinus University1
Arief RamadhanTelkom University1
Gunadi EmmanuelBinus University1
Agung TrisetyarsoBinus University1
Erna Budhiarti NababanUniversitas Sumatera Utara1
Heru MardiansyahUniversitas Sumatera Utara1
Saib SuwiloUniversitas Sumatera Utara1
Naemah MubarakahUniversitas Sumatera Utara1
Fahmi FahmiUniversitas Sumatera Utara1
Eka Purnama HarahapUniversitas Raharja1
Lina Naelal HusnaAffiliation unknown1
ZulhamAffiliation unknown1
Zulkarnain LubisAffiliation unknown1
Muhammad Reza AuliaAffiliation unknown1
Sherly AstutiAffiliation unknown1
Muhammad SalamuddinAffiliation unknown1
Ucuk DarusalamNasional University1
Purnomo Sidi PriambodoUniversity of Indonesia1
Novi Dian NathasiaAffiliation unknown1
Gunawan GunawanUniversitas Halu Oleo1
Sutarman SutarmanHasanuddin University1
Sayuti RahmanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A novel approach to optimizing customer profiles in relation to business metrics</dc:title>
	<dc:creator>Elveny, Marischa</dc:creator>
	<dc:creator>Nasution, Mahyuddin K. M.</dc:creator>
	<dc:creator>Zarlis, Muhammad</dc:creator>
	<dc:creator>Efendi, Syahril</dc:creator>
	<dc:creator>Syah, Rahmad B. Y.</dc:creator>
	<dc:subject xml:lang="en-US">Deep Learning, Bussines Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Business metrics; Customer profile; Multivariate adaptive regression spline; Prediction; Robust;</dc:subject>
	<dc:description xml:lang="en-US">Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/22733</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i1.pp440-450</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 1: March 2024; 440-450</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22733/13846</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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