Decision support system for potential sales area of product marketing using classification and clustering methods  

Oleh:

Sipayung, Evasaria M. ; Fiarni, Cut ; Tanudjaya, Randy

Jenis: Article from Proceeding
Dalam koleksi: PROCEEDING The 8th International Seminar on Industrial Engineering and Management (8th ISIEM). Atria Hotel & Conference, Malang, Indonesia March 17 – 19, 2015, page DSS-33.
Topik: Decision support system; marketing strategy; data mining; clustering; classification.
Fulltext: Paper 42 Evasaria M Sipayung - Harapan Bangsa Institute of Technology.pdf (571.93KB)

Isi artikel

Increasing use of information system and technology in business especially in marketing, helping organization in many ways. Especially on big data era, where data of transactions already being kept by an organization and need to be analyzed to gain information and knowledge.Unfortunately, the marketing strategy based on product characteristic and transaction patterns are still not largely extracted and implement due to the different characteristics of data and information need to be analyzed first to find the most suitable methods in order to gain the intended knowledge. In this paper we concentrated on analyzing and implementing a decision support system to help gain marketing strategy in the certain sales area based on product’s characteristics such as: type, material and accessories. The proposed system will gain pattern analysis of product characteristics and costumer’s need as a basic source of knowledge in marketing decision making. This pattern is gained from sales transaction data, so the sales volume for each sales area can be predicted. A systematic model that implements clustering method of data mining technique is proposed to manage the marketing intelligence in order to support marketing strategy and decision. We used clustering method to form groups of data transaction and gained a correlation between potential product and sales area as a base method for business analysis.Then the proposed system will use classification method of data mining technique to create patterns for product correlation as based method for business intelligence for new products. This research concludes a prototype of sales area decision support system with rate of precision 98.7% and a rate of recall 70.27%.

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