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Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 3 (2020) · pp. 243–252

DOI
10.22146/ijccs.58107
ISSN
2460-7258, 1978-1520
Published
2020-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer segment to become product promotion for potential consumers. The method was tested by comparing product promotions based on rules and product promotions without based on rules. Based on the test results, the average percentage of transaction from product promotion based on rules is 2,622%, higher than the promotion with the latest products with an average rate of transactions only 0,315%. The hypothesis in each segment obtained from the sample can support the statement that product promotion in all segments based on rules can be more effective in increasing sales compared to promotions that use the latest products without using rules recommendations.

Keywords

Collaboration network of the authors

27 authors · 34 co-authorship links · 4 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Universitas Sulawesi Tenggara
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sigit PriyantaUniversitas Gadjah Mada2
Anifudin AzisUniversitas Gadjah Mada1
Praretno WibowoAffiliation unknown1
Juwita MaharantiAffiliation unknown1
Iqnatius Dimas NugrohoUniversitas Gadjah Mada1
Amelia NursantiUniversitas Gadjah Mada1
Tri Kuntoro PriyambodoUniversitas Gadjah Mada1
Zaied ShouranUniversitas Gadjah Mada1
Nur Azizul HaqimiUniversitas Gadjah Mada1
Friska Ayu ListyaUniversitas Gadjah Mada1
Wahyono WahyonoUniversitas Gadjah Mada1
Riska Amalia PraptiwiUniversitas Gadjah Mada1
Hendri Kurniawan PrakosaUniversitas Gadjah Mada1
Anindhita NisitasariUniversitas Gadjah Mada1
Raymond LuisUniversitas Gadjah Mada1
Sarah Rosdiana TambunanUniversitas Gadjah Mada1
Luthfia Nurma HapsariUniversitas Gadjah Mada1
Reka AlamsyahUniversitas Gadjah Mada1
Debby AlitaUniversitas Teknokrat Indonesia1
Muslikhah MuslikhahAffiliation unknown1
Elih SudiapermanaAffiliation unknown1
Moh Edi WibowoUniversitas Gadjah Mada1
Agus SihabudinAffiliation unknown1
Lukman HeryawanUniversitas Gadjah Mada1
Ahmad RiyadiUniversitas Sulawesi Tenggara1

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) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy  (Case Study: Dayra Store)</dc:title>
	<dc:creator>Ramadhan, Dayan Ramly</dc:creator>
	<dc:creator>Rokhman, Nur</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Data Mining; Sequential Rules; Rule-Growth; Customer Segmentation; RFM Model</dc:subject>
	<dc:description xml:lang="en-US">One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer segment to become product promotion for potential consumers. The method was tested by comparing product promotions based on rules and product promotions without based on rules. Based on the test results, the average percentage of transaction from product promotion based on rules is 2,622%, higher than the promotion with the latest products with an average rate of transactions only 0,315%. The hypothesis in each segment obtained from the sample can support the statement that product promotion in all segments based on rules can be more effective in increasing sales compared to promotions that use the latest products without using rules recommendations.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/58107</dc:identifier>
	<dc:identifier>10.22146/ijccs.58107</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 3 (2020): July; 243-252</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/58107/28361</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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