STRATEGI PENJUALAN PEDAGANG PASAR MODERN BERBASIS CUSTOME DATA MINING

Authors

  • Hastuti Naibaho Management Study Program - Universitas Pembangunan Jaya
  • Yohanes Totok Suyoto Management Study Program - Universitas Pembangunan Jaya
  • Dion Dewa Barata Management Study Program

DOI:

https://doi.org/10.25273/jta.v5i1.4479

Keywords:

Customer Data Mining, Bundling Product, Sales Strategy, Strategi Penjualan

Abstract

Abstract. Business competition between merchant in the modern market which is is getting tighter needs effective marketing strategies. An effective sales strategy can be arranged based on the knowledge of consumers. The increasingly competitive business environment that causes businesses must continue to provide the best service to customers for the development and success of trading businesses in the present and who will date. This problem can be addressed properly if you have accurate information about customers. Accurate information about these consumers can be obtained through customer data collection methods that is called customer data mining. Customer data mining is a method of finding consumer data which includes various kinds of aspects ranging from characteristics to the way consumers purchase. Using the instrument questionnaire, this research is a consumer survey. This paper is a brief report on the results of consumer data excavation, analysis of the results of statistical data based on the results of consumer data processing, and formulation of recommendations regarding promotion and sales strategies for merchant in the modern market

Abstrak. Persaingan bisnis antar pedagang yang semakin ketat menuntut pedagang manciptakan strategi penjualan yang efektif. Strategi penjualan yang efektif dapat disusun berdasarkan pengetahuan tetang perilaku konsumen. Lingkungan bisnis yang semakin kompetitif menyebabkan pelaku usaha harus terus berupaya memberikan pelayanan terbaik kepada konsumen demi perkembangan dan kelangsungan usaha dagang di masa sekarang dan yang akan dating. Masalah ini dapat diatasi dengan baik jika pedagang mempunyai informasi akurat mengenai perilaku konsumen. Informasi akurat mengenai perilaku konsumen tersebut dapat diperoleh melalui metode pengalian data konsumen (customer data mining). Customer data mining merupakan metode mencari data konsumen yang mencakup berbagai macam aspek mulai dari karakteristik sampai dengan perilaku pembelian yang dilakukan konsumen. Menggunakan instrumen kuesioner, penelitian ini merupakan seuatu survei konsumen. Tulisan ini merupakan merupakan laporan singkat mengenai hasil penggalian data konsumen, analisis hasil data statistik berdasarkan hasil pengolahan data konsumen, dan rumusan rekomendasi mengenai strategi promosi dan penjualan bagi pedagang pasar modern.

 

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Author Biography

Hastuti Naibaho, Management Study Program - Universitas Pembangunan Jaya

Dr. Hastuti Naibaho has an educational background and work experience in organizational behavior, both at the macro and micro level. She has completed a doctoral degree with a Cumlaude from the Management Science Doctoral Program at Universitas Gadjah Mada, Yogyakarta. In 2012, she also obtained a certified human resources professional.

 

Before becoming a lecturer, she worked in a British company in the field of telecommunications contractor for 7 years at the project control section, in a Japanese manufacturing company for 1 year at the General Affair section, and in a Non-Profit Organization for 1 year at an information data center. Currently, Dr. Hastuti Naibaho works as a head of  Management Study Program at Pembangunan Jaya University

References

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Griva, A., Bardaki, C., Pramatari, K., Papakiriakopoulos, D. (2018). Retail business analytics: Customer visit segmentation using market basket data. Expert Syst. Appl. 100, 1–16. https://doi.org/10.1016/j.eswa.2018.01.029

Liao, S., Chen, Y., Hsieh, H. (2011). Mining customer knowledge for direct selling and marketing. Expert Syst. Appl. 38, 6059–6069. https://doi.org/10.1016/j.eswa.2010.11.007

Miguéis, V.L., Camanho, A.S., Falcão e Cunha, J. (2012). Customer data mining for lifestyle segmentation. Expert Syst. Appl. 39, 9359–9366. https://doi.org/10.1016/j.eswa.2012.02.133

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Published

2020-01-30

Issue

Vol. 5 No. 1 (2020)

Section

Articles

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