HOSPITALITY DATA MINING

Hospitality Data Mining

Hospitality Data Mining

Introduction

This study is basically an organized literature review on the topic “hospitality data mining”. It would include the interpretation and analysis of the implications and significance in light of the research topic defined. It would also include the critical analysis of the data mining related to restaurants and hospitality. This would facilitate in the acquisition of practice in the literature research, retrieval, reading and analysis of published sources

Background

In the event that the knowledge regarding the preferences of the customers is essential to the success of restaurant operations, data mining must be at the forefront of the technology toolbox held by the manager. Customer prediction, customer retention, and customer attention are the significant concepts of marketing in the restaurant industry and these are also the essential concepts of data mining. It has been long known in the industry of food services that there exists a need to surpass the expectations of the customers for the order of stimulating the current sales at the same time as establishing the prospect for restaurant business (Boire, 2009, pp. 2-3). Restaurants, until recently, have stored and captured transactional data in a system called POS or “Point of Sale” in a way that caused difficulty in accessing, evaluating, or applying that data to decision making. The recent technological advancements and developments now impart a more controlled and organized area for data collection (data warehouse), which enables effectual and efficient empirical analysis of that data (data mining). The procedure of data mining is aimed at identifying the trends, patterns, and relationships which might be existent among the data but are not too palpable and evident. The procedure of data mining is also aimed at converting the raw data into useful information and further converting this information into imminence (Ngai, et.al., 2009, p.n.d.). For instance, in the event that a restaurant is able to sort through the reserved data for improving its customer relations, then there is a greater prospect for the restaurant to acquire competitive advantage. The significant factors that are considered for data mining include ease of operation, reliability, and scalability. Within the restaurant industry, there are three factors to the minimum that influence the rate of adoption of the data mining software. These factors include increased knowledge regarding the end users, availability of effective techniques for data mining, and deteriorating costs of computing power. Data mining simply refers to the procedure employed for deriving useful information from the transactional data which is likely captured and stored by the restaurant regarding its clientele. The software for data mining is frequently described as a junction of business decision making, database technology, and statistics and it is frequently known as “siftware” (Kim, et.al., 2010, pp. 1-2). Siftware is aimed at changing the passive data into interactive data for the purpose of enhancing the customer relationship management (CRM), at the same time as enhancing the prospects of profitability. Data mining and data warehousing together establish a strong and influential database, which supports a robust ...