Data mining for customer relationship management crm

Why Data mining in CRM? | Alsys on CRM

data mining for customer relationship management crm

First, we classify the selected customers into clusters using RFM model to identify high-profit, gold customers. Subsequently, we carry out data mining usi. For each data record fed to the Data Mining for Customer Relationship Management CRM Processes Three main process areas Data. Data Mining Application in Customer Relationship Management for Hospital positioning (STP) strategy and the RFM model with data-mining in CRM.

In this view, it appears that maintaining existing customers is favorable than creating new customers in terms of a strategic hospital management.

Data Mining Application in Customer Relationship Management for Hospital Inpatients

As a result, rather than using a customer acquisition strategy towards unspecified individuals, now hospitals select loyal customers by analyzing accumulated information with those who have maintained a relationship for a long period of time and are introducing customer relationship management CRM marketing regarding them [ 78 ]. In other words, for management deterioration due to customers leaving and opportunity costs, CRM is becoming an important issue to the hospitals management.

CRM is a process of marketing by segmenting customers to better understand them and for the purpose of improving long-term relationships with valuable customers [ 8 ].

That is, a most distinct feature of CRM is not a traditional method of collecting the most number of customers but, CRM is a customer centered marketing which provides a service that meets individuals based on their characteristics and consuming patterns [ 6 ].

This technique of marketing arose from the segmentation, targeting, positioning STP strategy which can be seen as the core of marketing and STP separates a market of large-scale customers segmentation and selects the target market targeting and then positioning a service or product into their minds for recognition positioning.

Therefore, in order to perform CRM, it is extremely important to select targets that the hospitals can provide intensive services, discover high fidelity customers with an accurate understanding their characteristics, and further, predicting fidelity customers is necessary.

This study was performed to suggest a practical method of data-mining in CRM of hospitals. A detailed research aims are discovering loyal customers from a large scale database of discharged patients by combining data-mining with STP strategy and recency, frequency, monetary RFM model that are being used as marketing strategies among general companies.

RFM model is used for customer value analysis and applied for market segmentation. It is a behavior-based model to analyze the customers' purchasing patterns by using customers' information in large scale database. RFM model is composed of three measures, namely recency, frequency, and monetary [ 10 - 12 ].

  • Data Mining Techniques for Customer Relationship Management

Methods This study used a database of discharged patients from a university hospital in Seoul between January 1st and December 31st Among a total of 16, discharged patients, we excluded unsuitable patients for this study purpose younger than 19, foreigners, and patients who were participating clinical trialsand 14, patients were selected as final subjects. In order to discover fidelity customers, segmentation and targeting was performed by applying a core marketing strategy, i.

It is also being used as an extremely important method when commencing marketing activity or assessing customers' values [ 1011 ].

What is CRM?

And more recently, this is being used as a classification method of fidelity customers to perform CRM in hospitals [ 12 ].

In this study, the variables representing consuming frequency was the number of admission and visiting out-patient department OPD prior to one year of index admission and the variables representing monetary were expenses for being discharged from the hospital and the expenses per visit for out-patient care. Independent variables consisted of the factors associated with loyal customers revealed in previous study [ 112 ]. And it can be classified into three characteristics: Also, International Classification of Diseases ICD code of main diagnostic criteria at the time of discharge was used for disease group characteristics.

The analyzing method for market segmentation and target market selection, k-means algorithm cluster analysis was performed by applying RFM variables and comparing the groups' differences through a t-test.

Furthermore, to compare target market of fidelity customers and general customers' demographic characteristics, medical service use characteristics, disease group characteristics, t-test and chi-square test were performed. Lastly, the medical use pattern modeling was performed by applying a decision tree. This study relied on the training data to create a model; it applied the created model to the validation data [ 13 - 15 ]. To assess the decision tree, it has been compared with a logistic regression by using root asymptotic standard error ASEmisclassification rate and receiver operating characteristic ROC curve which are the most fundamentally used for comparing the predictive power of models.

For data analysis, t-test and chi-square tests were performed using the statistical package SAS ver. Classification of Fidelity Patients For market segmentation and target market selection, the customers were classified into two groups on the basis of RFM standards and the differences of the two groups are as follows Table 1.

Generating the decision tree given the trained model as input is the core issue in mining. So we try to propose a simple decision tree algorithm through probabilistic methods.

data mining for customer relationship management crm

In The Due Process we build up a probabilistic classification tree. Before that the following CRM issues are to be addressed. Defining the problem The following are some important aspects to be kept in mind while defining the problem. The scope of the project. The accuracy level, which would be required.

The output would be able to generate. Time and cost effectiveness of the output. Pick something well defined and small. Be aware of your limitations before defining the problem. Understand the existing CRM process. Better understand marketing strategies and the practical CRM process.

data mining for customer relationship management crm

Defining the user Build a profile for each user. Try to get the interest and the background of the user. Give him queries to tap knowledge about him. Use quick start programmers to tell about your future. Project yourself as an emerging firm. Highlight the benefits to the user.


Defining the data Locate the data dictionary. Get details about the Meta data. Obtain various constraints on data. Find the range of values or the possible values the data can take.