Mining purchasing decision rules from service encounter data of retail chain stores

  • Authors:

  • Fu-Ren Lin;Rung-Wei Po;Claudia Valeria Orellan

  • Affiliations:

  • Institute of Service Science, National Tsing Hua University, Hsinchu, Taiwan, ROC 30013;Institute of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, ROC 30013;Institute of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, ROC 30013

  • Venue:
  • Information Systems and e-Business Management
  • Year:
  • 2011

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Abstract

In this explorative research, we aim to find the most important service experience variables that determine customer purchasing decision and the clerks' influence on customers' purchases. This study was conducted as a case study of a children's apparel company, denoted Company L, which has 243 retail stores. Company L has implemented Point of Sale (POS) systems in its retail stores, and would like to know what functions could be added to induce storefront employees to deliver better customer service. We, therefore, focus on observing the services provided by storefront employees and their reflection on a customer's purchasing decision in a retail store. The study generated decision trees via Weka, a data mining open source software platform, to analyze multiple data sources to (1) understand what makes a good service experience for a customer, (2) get explicit knowledge from service encounter information, and (3) externalize the tacit knowledge of storefront service experiences. These findings can be used to improve Company L's POS system to guide storefront employees to learn from trained decision rules. Moreover, the company can internalize service experience knowledge by aggregating learned rules from the company's retail stores.