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SERVICE LEVEL ANALYSIS IN CUSTOMER RELATIONSHIP DECISION MANAGEMENT FOR FINDING CUSTOMER SATISFACTION PATTERN
Yudhistira Chandra Bayu, Taufik Djatna
Division of Industrial System Engineering, Department of Agro-Industrial Technology, Faculty of Agricultural Technology, Bogor Agricultural University, Bogor, West Java, Indonesia
E-mail: yudhistira.chandrabayu93gmail.com
,
taufik.djatnaipb.ac.id
ABSTRACT Customer in this era not only desire for good product, but also desire for good quality of service. Not
concerning customer desire, will increase the risk of customer to switch to to other company. A study to determine customer satisfaction toward service need to be done to retain customer. Customer
relationship management CRM is a strategy to build relationships with customers and it focuses on efforts to maintain a profitable customer. The focuse elements of CRM in this paper is service. Service
level analyse as a part of CRM is a method to describe or analyse the quality of service itself and Data Mining is a tool that can used to perform the analyse. The objetives of this paper are to identify services
that affect the level of customer satisfaction and to determine the pattern of satisfaction level towards the affect services. Identification was done by distributing questionnaire against customers using Liker scale
and the result was analysed using Relief method. The result of Relief would show the attributes that affect the level of customer satisfaction. Decision Tree DT is used to recognize the pattern of satisfaction
level. Result shows that there are 9 from 18 attributes of service affecting customer satisfaction. There are six pattern for determining customer satisfaction level obtained from DT calculation.
Keyword
:
Customer Satisfaction, Important Services, Service Level Analysis
1. INTRODUCTION
Nowadays, customer not only desire for good quality of product, but also desires more on good quality of service. Not concerning customer desires, will surely increase the risk of increasing the
propensity of consumers to switch to other company Purnasari, 2012. Customer relationship management CRM, as one of the method to retain customer, is a strategy to build relationships with
customers and it focuses on efforts to maintain a profitable customer. CRM is defined in the four simple elements: know, target, sell, service Rygielski et.al, 2002. Companies need to find out their customers
better know. Knowledge of customer is used to target the most profitable customers target. CRM establish a way to offer the product to the customer sell and retain customers by providing good service
to the customer service. During past few years, analysing customer data has been recognizing to be an effective strategy of business analytics and CRM. It leads to significant development in business
applications. They’ve been used to reduce customer attrition and improve customer profitability. To reduce customer attrition, company should know more about their customers and their wishes for service
that is provided by company to increase loyalty Data mining, as the key to discovering interesting patterns from data, is an assistive tool that can be
used to perform analyzing in CRM Han et.al, 2012. The goal of data mining in the field of CRM as well as this paper, is doe
s to increase the customer relationship by understanding customer’s needs. In the term of services, it can be used to determine the customer preference, to suggest recommendations, to identify
customer behaviour due to company service, and to recognize the service that affect to customer satisfaction level Berry and Linoff, 2004. Survey about service must be conducted to identify customer
needs in company, in which case company would like to take corrective decision and action
corresponding to customer’s voice. Information of customer satisfaction toward service that obtained by data mining analysis, will support the company in developing business strategies and services.
A service level as part of information processed in data mining is an agreed measure which might include one of the following elements to describe the performance of a service delivery: quantity, quality,
timeliness, and cost. Service level is similar to standards, but this term is usually reserved for organisation wide performance and as an aid for public scrutiny. An analysis of the service level should be conducted
to determine whether the existing services are met with the customers satisfaction NSW, 1999. One measurement method of determination of service level is by conducting survey. Some advantages in
analysing the service-satisfaction level using survey are understand of customer needs and priorities, and growing customer relationships Karten, 2003. Customer relationship decision aims to decide whether to
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and they should be maintained. In retaining customers, customer satisfaction comprehension must be applied to meet customer expectation.
This paper is focused on customer relationships related with the services provided by the company and discovering which services are critical for customers to improve its performance by using data mining
techniques. The challenge of this paper is the use of data mining to find out about the customer. Therefore this study compiled with the following objectives 1 to identify the attributes of services that affect
customer satisfaction 2 to analyse pattern using DT from result of previous method.
2. METHODOLOGY
Identification Important Attributes
The first step is identify the attributes of service that company provided. Identification attributes of service conducted to determine the parameters used in this study. Attributes of services will be divided
into five variables including reliability, assurance, tangible, empathy, and responsiveness. The identification was done by conducting interviews to the company and studying related literature. Result of
identification applied into questionnaires which was used to interview customers and to determine customer satisfaction of each service. Overall level of satisfaction which used as a class in Relief and
Decision Tree analysis asked in the interview The scale used in the questionnaire is the Liker scale with a range of one to five. The sampling used in this study is judgement sampling which the character of
customers that will be the respondent is already determined. A respondent in this study is the customer who always conducts direct transactions with the company within three weeks and only limited to
customers in traditional markets. The respondents are the agent and snack retailer in traditional markets.
Kira and Randell 1992 stated a method to select features of services that necessary to describe the target concept which determine the important service that affect to customer satisfaction. Relief method
eliminates the irrelevant features of service, so the result becomes effective. Relief is capable to estimate the quality of service attributes in classification problems with strong dependencies between attributes
Kononenko and Sikonja, 1997. Based on Kira and Rendell 1992, here is the formulation of Relief that would be used in this paper.
i i
= + | - near - miss x | - | - near - hit x |
i i
x x
i i
w w
1
w
i
is the weight of attributes-i, near-miss
i
is the expected to be different with x
i
and near-hit
i
is the expected to be very close to x
i
. x
i
is the instance of attribute-i. Attributes which has a weight value of the top 10, will be declared the 10 attributes that most affect the level of customer satisfaction with the
services. 10 of these attributes will be used to predict or see a pattern of behaviour towards the satisfaction of customers using decision tree. Kononenko 1994 describes Relief method as pre-
processing method before learning a model. In this paper, Relief was used before DT model was performed. The positive weight value will be used for DT analysis
Service Level Analysis Using Decision Tree
According to reference Han et.al 2012 Decision Tree DT is classification techniques of data mining analysis. DT was selected because of its ability to perform exploration on large dataset and find
the most meaningful variable towards customers on a dataset Berry and Linoff, 2004. DT provides predictions in the form of patterns and used to understand the target or customers Tsiptis and
Chorianopoulos, 2009. The results of the decision tree will give answers about customer satisfaction patterns existing today. DT formulations have to be followed to perform the splitting or separation which
will form the root node, internal nodes and leaf nodes. The formulations are to be followed is the determination of entropy and information gain, and the following is formulation Bramer, 2007.
2 3
4
D is the entropy of training set, where p
i
is nonzero probability that an arbitrary tuple in D belongs to class C
i
. In this paper it’s just have two classes, that is “Quite Satisfy” and “Satisfy”, and this class is used
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to for Relief method. m is the number of partitions in the training set, in this paper the value of m is 20. DT provides a flowchart that resembles a tree structure, where each internal node denotes a test on an
attribute, each branch represents a class or class distribution. In this paper the overall customer satisfaction level for the companys customer service performance assigned as class and the data used is
the level of customer satisfaction on the performance for each services. As in the formula 4, attributes that have the greatest gain serve as the root node or an internal node and split will perform in according to
the attribute values. Attributes that serve as root or internal node, will not be used against for the next calculations. The split and grow procedure of a node stop when: all records in a specific node have the
same value for the class field and a significant predictor cannot be found, and the separation cannot be improved further although the result is not perfect.
3. RESULT AND DISCUSSION
Identification the Important Attributes
The results of interview to the company and literature studies indicate there are 18 attributes where each attribute is derived from the five variables used. Variables used to identify the attributes of the
company are: there is no fault caused by the company in carrying out the duties and services reliability, competent workers and staff, polite and convincing in carrying out their responsibilities assurance,
physical facilities are adequate tangible, workers and staff care to the customer empathy, and the responsiveness of workers to the problems experienced by customers which is related with a service that
company gives responsiveness. These variables need to be translated into quality attributes to measure satisfaction or dissatisfaction of customers. Table 1 is a list of quality attributes for each of variables that
have already been identified in company. Variables and attributes that had been identified applied into questionnaire. The questionnaire used to determine customer satisfaction with each service attribute and
used as a tool in conducting interviews to customers. Result of the questionnaire used as data in Relief and DT analysis.
The results of Relief calculation show the weight value of each service attribute. Reference [9] describe that the threshold weight value must be determined. The purpose of threshold value
determination was to determine which features have an influence to customer satisfaction level. Range of threshold values is from 0 to 1. The threshold value in this paper is 0. So, we decided to take only
attributes which have positive weight value. The parameter with positive weight value is considered to be relevant or affect the customer satisfaction level. Table 1 show the weights value on each attribute.
Table 1. Weight Value of Each Attributes No Attribute
Attribute of Customer Service Weight Value
1 On time in delivery
0,0128 2
The promptness of response to the order 0,0075
3 Product item which delivered according
the order -0,0322
4 No damaged product when it arrive to
customer 0,0375
5 Easiness of product returns in case the
product mistake -0,0622
6 Easiness and convenience in transaction
-0,0050 7
Competent staff and good distribution facilities
0,0169 8
Availability of ordered products 0,0061
9 Customer profile documentation
0,0255 10
Minimum threshold for ordering products -0,0544
11 Hospitality in service
-0,0231 12
Call centre for service complaints -0,0350
13 Service about product information
0,0111 14
Availability for help in case of errors -0,0306
15 Easiness in ordering products
-0,0175
