International Journal of Research and Reviews in Information Technology (IJRRIT) Vol. 1, No. 2, June 2011 ISSN: 2046-6501 Copyright © Science Academy Publisher, United Kingdom www.sciacademypublisher.com Science Academy Publisher
CCHUM - An adaptive Business Intelligence strategy to improve Customer Retention using bee-hive classification algorithm Sakthikumar Subramanian1 and Arunkumar Thangavelu2 1
Secova eServices Private Limited, Chennai, India School of Computing Sciences and Engineering, Vellore Institute of Technology, Vellore, India
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Email:
[email protected],
[email protected]
Abstract – Analysis of customer buying pattern for a consumer marketing process carries vital importance in any business system. Various methods of predicting the customer buying pattern and adaptive classification is required over variable period of time for any business establishment. The paper aims to suggest a bee-hive based a classification approach using the customer buying pattern for different products. CCHUM approach works with heuristic predictive rule module to provide support in identifying customer interests and supporting customer retention. The results show an improved customer retention percentage when compared with traditional classification and genetic approach. Keywords – BI, CRM, Bee-hive classification model, buying pattern analysis
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Introduction
The basic property of a customer buying a product remains as a vital parameter in identifying the interest of customer. To understand and identify the property along with related product buying patterns can aid which form the successful CRM strategy for improving Business Intelligence (BI) using soft computing tools. BI in CRM has multiple dimensions such as Customer Identification, Customer Attraction, Customer Retention and Customer Development. Mining in CRM requires adaptive forecasting in identifying the customer based on change in market and business trends [14]. Data Mining basically deals with clustering, association, classification, forecasting pattern / sequence discovery, regression and visualization. For almost all marketing firms, the cost of acquiring new customers exceeds the cost of maintaining “good” old customers. Based on data collected from a super market[10] firm, which predicts that out of 80,000 customers, if 8000 customers leave each month, then it would cost around $200 each month. The challenge taken due to industry attrition rate was high to 8% which adds to overall cost in additional each month. Methods to predict a „useful‟ customer[11] depend on variables such as customer interest on products, demand, salary, region, age and other organizational [4] specific issues. This data would help to identify the profitable and non-profitable customers who may be retained or detained. The dynamic aspect of customer retention focuses on model building from available customer and product buying data. Many data mining research organization and product marketing firms help in analysis of customer – buying pattern based on collected /stored asset of data about their current
customers, potential customers, suppliers and business partners. However, the inability of a system to discover valuable, hidden information of customers from dataset for customer retention is always misguided [6]. The technical process of CRM involves in capturing customer's data over a long period of time for the whole marketing firm, consolidating all the internal existing data in a central database. System analyzes in consolidated data, distributes the results of analysis to contact points with customer as well finally uses that information to identify customer interest and views related to product. According to Gartner Group[20], "CRM is a strategy aiming at understanding and anticipating current customer needs and company‟s potentials to fulfill them ". This paper discusses on: i) Survey and analysis of classification algorithms related to Customer Retention Model and customer interest pattern analysis. ii) To provide an adaptive bee-hive algorithm as classification approach, which can adapt to variable customer interests. iii) Model to improve customer retention, based on customer identification, product buying / selling patterns. Classification approaches of Data mining help to accomplish fore-mentioned goals by extracting or detecting hidden customer characteristics, need and behaviours[4] from large databases. Several parameters [Table-1] work together to enable a customer to buy a product or retain using the same product. Interesting measures had also been identified in such cases, such as physiological aspects of customer, environmental issues and network suggestions, which are
International Journal of Research and Reviews in Information Technology (IJRRIT) also accountable in our work. Identifying the product interests of customer and retaining the customer for the market is an interesting problem that can be solved using Classification approach in Data Mining research. Many classification approaches had been proposed [13] [18], but the need for a dynamic classification algorithm is always felt among the business researching community. Different machine learning methods of data-mine modeling and analysis [2][7] can be employed based on data characteristics and business requirements for retaining customers. Appropriate data 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 CRM decisions. This work deals with Bee-hive classification approach [19][3], which is adaptive to dynamic customer interest and update. This approach also adopts nuclear segmentation of customer based on change in customer behavior, need and highly variable requirements. Predictive rule engine is heuristic which works by gathering query, processes based on knowledge update and provides solutions. This paper discusses on CCHUM, an adaptive classification algorithm to identify customer retention based on regular customer product buying pattern. A detailed survey on bee colony algorithms had been carried out with specific suggestions on classification based on bee behaviors criteria and honey gathering analysis. The algorithms are based on foraging behavior of bees in the bee colony and food source searching behavior related to nest site searching and storing. The paper is categorized into six primary sections, including Section-1 which introduces the CRM, its issues and need for customer retaining in a BI system. Section-2 focusing on related literature reviews, section-3 proposes the CCHUM model adopted in this work, section-4 elaborates on the architecture of CCHUM with working functionality and algorithm while Section-5 discusses on data set collected and experiment carried out, with performance analysis / results. The detailed summary of work done and need for future work is discussed in Section-6.
2.
Related work
Classification algorithms are considered as the most common learning models in data mining [17]. These models aim at improving CRM management and prediction of future customer behaviours through classifying database records into a number of predefined classes based on specific criteria [7] [13]. Few common tools used for classification are neural networks, decision trees and if then-else rules. But these models aim at providing a solution to predict the effective or chances of providing a profitable CRM strategy through the prediction of customer behaviors, where as it does not help in optimizing the chances of improving the model. George‟s [11] widely accepted model of customer retention examines factors contributing to decision of customer to retain buying same product or from same firm. The primary focus of this model is the customer's interest towards the product or social integration of product with firm. Another model developed by Peterson [13] focuses on the psychological and behavioral factors of customer related to
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product or firm. Gao Jie [4], recommend a special attention to customer‟s churn. Customer‟s churn brings great expenses to the organizations, and they demand measurement to reduce the rate. The mathematical function, eqn. (1), below illustrates the relationship between retention rate and customer‟s lifetime [4]. It is possible to calculate the impact of that customers‟ retention rate would have on the company‟s profitability and also the increase effect of a customer‟s lifetime. The reason for customer‟s churn must be investigated, trying to correct the directions in order to stop customers‟ evasion. There is nothing to do with customers that were changed, however customers that stopped buying from the company for reasons of dissatisfaction are recoverable, since problems can be solved through conversation. 2.1. CCHUM – Model and Approach CCHUM works on a simple decision making system to suggestion on customer interest to retain them as an asset of marketing firm. Data Mining helps in acquiring knowledge of any domain and works with rules on large numbers of data. The information is connotative, unknown, which creates interest among decision makers. Most of data mining classification models depend on extensible classification knowledge and conductive knowledge. Extensive Classification Knowledge procedure is adopted in this work. The model can be defined as “If u satisfies the condition Li, and there is u E(T) under the transformation T, then this knowledge obtained with transformation T causes „u‟ to generate positive qualitative change”, which can be generated as (1) Customer Relation Management depends on Extensive Knowledge Classification procedure, since the interest of customer „u‟ depends on buying the product. The rule “satisfies“ is represented as “Li”, where the customer interest is the primary component. The transformation is a vector quantity, which may retain the customer or change the attitude of customer to detain. The extensive capacity to define data as positive quality knowledge, negative quality knowledge for any change in data is termed as Extensive Classification Knowledge. Approach adopted by CCHUM is explained as follows: Step 1: The algorithm starts with „n‟ scout bees being placed randomly in the search space of „m‟. Step 2: Each scout bee „B‟ is identified and trained with the data „K‟. for K= 0; K
