Sequential Pattern Mining to Support Customer Relationship Management at Beauty Clinics

Esther Irawati Setiawan; Valerynta  Natalie; Joan  Santoso; Kimiya  Fujisawa

doi:10.31763/businta.v6i2.602

Authors

Esther Irawati Setiawan Institut Sains dan Teknologi Terpadu Surabaya
Valerynta Natalie Institut Sains dan Teknologi Terpadu Surabaya
Joan Santoso Institut Sains dan Teknologi Terpadu Surabaya
Kimiya Fujisawa Tokyo University of Technology

DOI:

https://doi.org/10.31763/businta.v6i2.602

Keywords:

Data Mining, Generalized Sequential Pattern, Sequential Pattern Mining

Abstract

The increasing competition for beauty clinics, makes management need to think of methods to survive in this competition. For that, the company needs to improve CRM in its service to customers. Customer Relationship Management is a series of activities managed in an effort to better understand, attract attention, and maintain customer loyalty.

Sequential Pattern Mining is one of the data mining techniques that is useful for finding patterns sequential / sequence of a set of items. The algorithm that is used is the Generalized Sequential Pattern (GSP). GSP performs candidate generation and support counting processes that is, the union of L1−k with itself which generates a candidate sequence that cannot exist as twin candidate, after that deletion candidate who does not meet the minimum support. While carrying out the process through existing data, is also carried out increasing the number of supports from the included candidates in data sequences. The output to be produced by the program are all frequent itemsets that satisfy minimum support in the form of rules.

Sales transaction data will be processed by using the Generalized Sequential Pattern algorithm so that it can produce a rule, namely the purchase order that meets the minimum support. The result of the rule used by management to support enterprise CRM activities such as acquiring new customers, increasing the profits from existing customers, and retaining existing customers.

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