Integration of RECA Weighting and MARCOS Methods in Decision Support Systems for the Selection of the Best Customer Recommendations

Ayuni Asistyasari; Muhammad Waqas Arshad; Iryanto Chandra; Yosep  Nuryaman; Very Hendra Saputra

doi:10.33365/jatika.v6i2.219

Ayuni Asistyasari Universitas Bina Sarana Informatika
Muhammad Waqas Arshad University of Bologna
Iryanto Chandra UIN Sunan Kalijaga
Yosep Nuryaman Universitas Bina Sarana Informatika
Very Hendra Saputra Universitas Teknokrat Indonesia

DOI: https://doi.org/10.33365/jatika.v6i2.219

Keywords: Customer Selection, Decision Support System, RECA Weighting, MARCOS Method, Multi-Criteria Decision Making (MCDM)

Abstract

In a competitive business environment, selecting the best customers is a strategic step to improve marketing efficiency and build profitable long-term relationships. However, this process is often constrained by subjectivity in determining criteria and evaluating alternatives. This study aims to develop an objective and measurable decision-making model by integrating of the Respond to Criteria Weighting (RECA weighting) and the method of measurement of alternatives and ranking according to compromise solution (MARCOS). The RECA weighting is used to determine the weight of criteria based on the response to their level of importance, while MARCOS is used to evaluate and rank customer alternatives based on proximity to the ideal solution. The final ranking of customers is determined using the RECA weighting method and MARCOS, which reflects the final value of each customer alternative; Customer 3 obtained the highest final score of 1.2339, indicating the best overall performance based on the established evaluation criteria. Furthermore, Customer 7 and Customer 1 are in second and third place with scores of 1.2096 and 1.1546, respectively, indicating that these three customers are the main candidates to be prioritized in the customer relationship strategy. The result of the integration of these two methods provides a decision support system that is able to generate accurate and logical customer ratings, and supports data-driven strategic decision-making. This model is expected to be an effective solution in improving the quality of business decisions, especially in managing customer relationships more on target and efficiently.

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