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

Dayan Ramly Ramadhan(1*), Nur Rokhman(2)

(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics; FMIPA UGM, Yogyakarta
(*) Corresponding Author


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.


Data Mining; Sequential Rules; Rule-Growth; Customer Segmentation; RFM Model

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