Data mining customer relationship

Data Mining in CRM

data mining customer relationship

Customer Relationship Management or CRM is an important part of every business. It is, like what the name implies, a strategy for. Using Data Mining. Gaurav Gupta and Himanshu Aggarwal. Abstract—Customer Relationship Management (CRM) refers to the methodologies and tools that. PDF | Data mining has various applications for customer relationship management. In this article, we introduce a framework for identifying.

Organizing and interpretation of data are essential for this purpose.

Customer Relationship Management Through Data Mining 0

It is extremely important to collect the right data and group it accordingly. Reliable business studies have proved that good CRM management can optimize the business potential of an organization by up to forty percent. Data mining is the best solution for fulfilling the above-mentioned criteria. It involves extracting patterns from a huge amount of data sets by using statistics and artificial intelligence. In this manner, you can achieve the desired business intelligence and gain an edge over competitors.

Data mining is used in a variety of functions like promotion, surveillance, detection of fraud etc.

Data Mining Application in Customer Relationship Management for Hospital Inpatients

With rapid advancement in technology, data mining can play an important role in customer relationship management applications. 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.

Data Mining Techniques for Customer Relationship Management - IOPscience

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 ]. 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.

data mining customer relationship

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 ]. 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 ].

data mining customer relationship

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.

data mining customer relationship

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.

Data Mining Techniques for Customer Relationship Management

It will also aid the marketing team in designing personalized marketing campaigns and promotion offers. Customer segmentation Learn which customers are interested in purchasing your products and design your marketing campaigns and promotions keeping their tastes and preferences in mind.

Product Customization Manufacturers can customize products according to the exact needs of customers. In order to do this, they must be able to predict which features should be bundled to meet the customer demand.

data mining customer relationship

Fraud detection By analyzing past transactions that were later determined to be fraudulent, a business can take corrective measures and stop such events from occurring in the future. Banks and other financial institutions will benefit from this feature immensely. Warranties Manufacturers need to predict the number of customers who will submit warranty claims and the average cost of those claims.

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This will ensure efficient and effective management of company funds. Anomalies can provide actionable information because they deviate from the average in the data set. Association Rule Learning Discover relations between data items in huge databases.

data mining customer relationship

With Association Rule Learning, hidden patterns can be uncovered and the information gained may be used to better understand customers, learn their habits, and predict their decisions.