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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) · hlm. 440–450

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

Abstrak

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.

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40 penulis · 92 tautan kepenulisan bersama · 46 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universitas Sumatera Utara
  • Medan Area University
  • Binus University
  • Universitas Raharja
  • Nasional University
  • Universitas Syiah Kuala
  • Universitas Muhammadiyah Sumatera Utara
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Herman MawengkangUniversitas Sumatera Utara4
Suherman SuhermanUniversitas Sumatera Utara4
Al-Khowarizmi Al-KhowarizmiUniversitas Muhammadiyah Sumatera Utara2
Habib SatriaMedan Area University2
Syafii SyafiiAndalas University2
Mahyuddin K. M. NasutionUniversitas Sumatera Utara2
Dadang PriyantoUniversitas Bumigora2
Mochamad WahyudiUniversitas Bina Sarana Informatika2
Hengki Tamando SihotangAfiliasi tidak diketahui2
Desi VinsensiaAfiliasi tidak diketahui2
Poltak SihombingUniversitas Sumatera Utara2
Marwan RamliUniversitas Syiah Kuala2
Sayuti RahmanMedan Area University2
Rahmad SyahMedan Area University1
Al KhowarizmiAfiliasi tidak diketahui1
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 HusnaUniversitas Raharja1
ZulhamMedan Area University1
Zulkarnain LubisMedan Area University1
Muhammad Reza AuliaUniversitas Teuku Umar1
Sherly AstutiSun Yat-sen University1
Muhammad SalamuddinUniversitas Raharja1
Ucuk DarusalamNasional University1
Purnomo Sidi PriambodoUniversity of Indonesia1
Novi Dian NathasiaNasional University1
Gunawan GunawanUniversitas Halu Oleo1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<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>
</oai_dc:dc>