An impact of knowledge mining on satisfaction of consumers in super bazaars


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An impact of knowledge mining on satisfaction of consumers in super bazaars

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 426 AN IMPACT OF KNOWLEDGE MINING ON SATISFACTION OF CONSUMERS IN SUPER BAZAARS Dr. S. D. Mundhe1 , Mr. D.R. Vidhate2 1 (Sinhgad Institute of Management and Computer Application (SIMCA), S.No. 49/1, Off Westerly Bypass Pune-Mumbai Highway, Narhe-Top, Pune -411041, India) 2 (College of Computer Application for Women, Satara, C/O Kanya Shala, Bhavani Peth, Rajpath, Satara-415002, India) ABSTRACT Customer satisfaction in super bazaars is important in the situation of intense competition where super bazaars fight for every individual customer. It is a dynamic field, critically important to the success of super bazaars. So, the measurement of customer satisfaction provides important information for the super bazaars and serves as a warning signal about future business developments. The best way for the super bazaars is to find out what their customers think about them is to conduct a customer satisfaction survey. The objective of the research is to identify possibilities for the increase in customer satisfaction. Different knowledge mining techniques are suggested by studying satisfaction level of consumers for successful retention by different super bazaars to improve business performance. Therefore, we proposed a model for efficient implementation of knowledge mining for studying customer satisfaction. The paper also summarizes researchlimitationsaswell astheworkcontributionandfuture researchguidelines.The findings of the study states that consumer satisfaction is linked with knowledge mining. Keywords: Customer Behaviour, Customer Satisfaction, Knowledge Mining, Retail Outlets, Super Bazaar. 1. INTRODUCTION The concept of customer satisfaction has attracted much attention in recent years. Customer satisfaction is widely recognized as an important factor in the formation of consumers' future purchase intentions. A satisfied customer is more valid than a good advertisement or ads. A satisfied customer stays loyal longer, buys more, talks positively INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 3, May-June (2013), pp. 426-431 © IAEME: Journal Impact Factor (2013): 6.1302 (Calculated by GISI) IJCET © I A E M E
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 427 about the super bazaar and its products, pays less attention to competitors and is less price- sensitive. Satisfaction with a product or service is a construct that requires experience and use of a product or service. [4] Organizations that try to analyze this concept should begin with an understanding of various customer satisfaction parameters. It is a state that reflects the evaluation of relationship between customer and super bazaar. As customers are increasingly more aware of the conveneince and their lifestyle, there are many cases preferring to go for super bazaar for their everyday shopping rather than small departmental store around. So, customer satisfaction is important for growth of this sector. Customer satisfaction is simple term stated as level of shopping experience in super bazaar where customers expected service level is met with actual service provided by retailer. The customer of super bazaar are mainly higher income group people.So, they want satisfactory level of products and services rather than traditional shopping places. The customers also want to minimize their time and hazards for shopping. The management and owners of super bazaar have to consider the influencing factors of customer satisfaction to hold the market growth. The achievement of customer satisfaction leads to bazaar loyalty and product repurchase. Knowledge mining combines the statistic and artificial intelligence to find out the rules that are contained in the data, letters, and figures.[2] The central idea of knowledge mining for customer retention is that data from the past that contains information that will be useful in the future. Appropriate knowledge mining tools, which are good at extracting and identifying useful information and knowledge from enormous customer databases, are one of the best supporting tools for making different customer retention decisions. Within the context of customer retention, knowledge mining can be seen as a business driven process aimed at the discovery and consistent use of profitable knowledge from organizational data. By applying knowledge mining techniques we can discover customer behavior, customer satisfaction, and loyalty or background of the customer. Assessment and analysis in this model may strengthen customer behavior and loyalty for particular super bazaar. Using knowledge mining techniques customer would be satisfied under the bazaar policy and limitations. The most commonly used techniques include artificial neural networks, Decision trees, Nearest-neighbor method, Genetic algorithm, Rule induction and Data visualization. 1.1 Artificial neural network It is non-linear, predictive models that learn through training which can then be used to make predictions on data with unknown outcomes. 1.2 Decision tree It is tree shaped decision models that utilize rules to classify a data set. Each model represents sets of decisions. For example, whether the organization is using an appropriate cost-effective marketing strategy that is based on the assigned value of the customer, such as profit. 1.3 Nearest-neighbor method It classifies dataset records based on similar data in a historical dataset. 1.4 Genetic algorithms It is a technique that is based on the concepts of natural evolution using genetic combination, mutation and natural selection as a form of optimization.
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 428 1.5 Rule induction It is the use of if-then rules from data based on statistical significance. 1.6 Data visualization It is the pictorial representation of relationships among data. 2. KNOWLEDGE MINING Knowledge mining tools take data and construct a representation of reality in the form of a model. The resulting model describes patterns and relationships present in the data. Knowledge mining is used to construct six types of models aimed at solving business problems: classification, regression, time series, clustering, association analysis, and sequence discovery. The first two, classification and regression are used to make predictions, while association and sequence discovery are used to describe behavior. Clustering can be used for either forecasting or description. Each business is interested in predicting the behavior of its customers through the knowledge gained in knowledge mining. Super bazaar business collects large amount of data on sales and customer shopping history. The quantity of data collected continues to increase rapidly. It provides a rich source for knowledge mining. Knowledge mining helps super bazaar betterunderstand their business, be able to better serve their customers and increase the effectiveness of the bazaar in the long run. For super bazaars, knowledge mining can be used to provide information on product sales trends, customer buying habits and preferences etc. Knowledge Mining functions: 2.1 Classification It aims at building a model to predict future customer behaviors through classifying data records into a number of predefined classes based on certain criteria .For example, assigning customers to market segments. In this case the target variable is the category and the techniques discover the relationship between the other variables and the category. When a new record is to be classified, the technique determines the category and the probability that the record belongs to the category. 2.2 Regression Regression is used to map a data item to a real valued prediction variable. It assumes that the target data fit into some known type of function and then determine best function of this type that models the given data. For example, super bazaar owner wishes to reach certain level of sale before specific month. Periodically, he predicts what his sale would be based on its current value and several past values. He uses simple linear regression formula to predict this value by fitting past behavior to a linear function then using this function. 2.3 Time Series analysis Time series analysis comprises those knowledge mining techniques that are applied to the analysis of time ordered data records. These knowledge mining techniques attempt to detect similar sequences or subsequences in the ordered data. It is used in sales forecasting. In super bazaar business, it is used to find amount of sale by super bazaar for a period of week, month or year.
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 429 2.4 Prediction Prediction is predicting future state rather than current state. It is about predicting the value of a continuous variable from the other variables in a data record. For example, predicting the likely expenditure of a customer from their age, gender and income group. The most familiar value prediction techniques include linear and polynomial regression. 2.5 Clustering It is the term for a range of techniques which attempts to group data records on the basis of how similar they are. For example, super bazaars owner could store description of each of their customers. In this case clustering would group similar customers together, at the same time maximizing the differences between the different customers groups formed in this way. Common tool for clustering include neural networks. 2.6 Summarization Summarization is the abstraction or generalization of data. A set of task-relevant data can be abstracted and summarized, resulting a smaller set which gives general overview of the data and usually with aggregation information. For example, purchase by all customers in bazaar can be summarized into weekly purchases, monthly purchases, product-wise purchases etc. Such high level summary information instead of detailed purchase is presented to bazaar owners for customer analysis. 2.7 Association rule Association rule describes a family of techniques that determines associations between data records. The most well known type of association rules is market basket analysis. It discovers the combinations of items that are purchased by different customers in super bazaar, and by association we can build up a picture of which types of product are purchased together. Common tools for association modeling are statistics and apriori algorithms. 2.8 Sequence Discovery It is used to determine sequential patterns in data. These patterns are based on time sequence of actions. These patterns are similar to associations in that data are found to be related but relationship is based on time. It discovers the combination of items that are purchased by different customers in super bazaar but item are purchased over time in some order. 3. STATEMENT OF PROBLEM In a super bazaar, behavior of buyer is important because the buyers are scattered according to their convenience of purchasing. So, research is carried out on super bazaar to study satisfaction level of consumers using different knowledge mining methods. This study is useful for effective decision making by studying customer purchase patterns and trends over time.
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 430 4. OBJECTIVES OF RESEARCH 1. To study the factors of satisfaction influencing buying decisions of the consumer visiting super bazaars. 2. To extract the information using knowledge mining to measure the level of satisfaction of consumers from super bazaars. 5. CUSTOMER SATISFACTION SURVEY PROCESS The customer satisfaction survey process can be started by designing well structured questionnaire. Customer satisfaction survey can be carried out by filling questionnaire from consumers of super bazaar. Knowledge can be extracted by studying survey data. Different knowledge mining techniques are applied to mine data. This data can be tested for accuracy of knowledge. If results are satisfactory then it is used for customer satisfaction survey process otherwise redesigning a questionnaire is needed for survey process. The proposed model is represented in following steps: No Yes Design Questionnaire Customer Satisfaction Survey Study Knowledge Gained Apply Knowledge Mining Techniques Accurate Knowledge Data Analysis Testing Discovered Knowledge Apply Knowledge to Study Customer Satisfaction
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME 431 6. CONCLUSION The research model presented in this paper is for the study of customer satisfaction in super bazaar. Beside the monitory benefits to the super bazaar, it will understand the customer's problem and will enhance the customer satisfaction. More and more customer will interact easily to the super bazaar. This approach will be applicable to those super bazaars which have problems with the customers understanding. Knowledge mining tools are important to study the customer satisfaction for any super bazaar. Adopting a knowledge mining based business will create foundation for improved business efficiency. It will help increase the profit of super bazaars. Application of knowledge mining techniques in customer satisfaction of super bazaar business is an emerging trend in the global economy. It has attracted the attention of practitioners and academics. 7. FUTURE RESEARCH The research model presented in this paper will be updated through the customer survey or questionnaire. This will enhance our model for customer satisfaction. We can improve our model structure by surveying customers and generating new rules and patterns that will give some fruit full results to the super bazaar. By using different knowledge mining techniques the model can lead to more enhanced. REFERENCES [1] Joan L.Anderson, Laura D. Jolly, Ann E. Fairhurst, Customer relationship management in retailing, A content analysis of retail trade journals of Retailing and consumer Services, 14(6), 2007, 394-399. [2] E.W.T.Ngai, Li Xiu, D.C.K. Chau, Application of data mining techniques in customer relationship management: A literature review and classification, Expert Systems with Applications, 36(2), 2009, 2592-2602. [3] Margaret H. Dunham, S. Sridhar, Data Mining: Introductory and Advanced Topic (Pearson Education, Inc., 2006). [4] U.Dineshkumar, P.Vikkraman, Customers’ Satisfaction towards Organized Retail Outlets in Erode City, IOSR Journal of Business and Management (IOSRJBM),3(4), 2012, 34-40. [5] TR. Kalai Lakshmi, S.S.Rau, Facets of Buyer Behavior in Retail Supermarket Chain Stores, International Conference on Technology and Business Management, 2013, 18-20. [6] Briony J Oates, Researching Information Systems and Computing (South Asian Edition, 2006). [7] Zhaohua Deng, Yaobin Lu, Kwok Kee Wei, Jinlong Zhang, Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China, International Journal of Information Management, 30(4), 2010, 289-300. [8] Joan Anderson, Antigone Kotsiopulos, Enhanced Decision Making using Data Mining: Applications for Retailers, Journal of Textile and apparel Technology and Management, 2(3), 2002. [9] Richard L. Oliver, Satisfaction: A Behavioral Perspective on the Consumer, (Boston: McGraw - Hill, 1997). [10] Dhivya Sathish and Dr. D. VenkatramaRaju, “Satisfaction of Buyers Towards Retail Outlets”, International Journal of Management (IJM), Volume 3, Issue 1, 2012, pp. 115 - 120, ISSN Print: 0976-6502, ISSN Online: 0976-6510