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    DATA MINING IN CRM

    SUBMITTED BY

    BINO JOSEPH

    M090041MS

    INTRODUCTION

    Customer relationship management enables companies to improve their profitability by

    having a better relationship with their customers. It enables a firm to identify profitable

    customers and maintain a good relationship with them and delight them by individualized

    offerings. The core of CRM is to understand customers which in turn maximises customer

    lifetime value, customer retention, loyalty and profitability. In order to have a successful CRM

    companies must be able to manage the customer life cycle effectively by matching products and

    campaigns to prospective and existing customers.

    Till a few years back the main purpose of CRM software was to manage customer

    information so that the whole process is simplified for the organisation. This is called operational

    CRM, where the focus is on storing customers information in databases that presents aconsistent picture of the customers relationship with the firm. This information is used in sales

    force automation and customer services.

    These days analytical CRM has replaced operational CRM. Data mining is a very popular

    technique used in analytical CRM. Data mining can be defined as the automated process of

    detecting relevant patterns in a huge database. The SAS Institute (2000) defined data mining as

    the process of selecting, exploring and modelling large amount of data to uncover previously

    unknown data patterns for business advantages. Data mining uses well-established statistical

    and machine learning techniques to build models and predict customer behaviour. It helps

    marketers to better understand the customer behaviour. For example, through data mining it was

    found that beer and diapers are bought together from a supermarket. Hence, the decision to place

    these to items close to each other. Such patterns which are not obvious are identified by data

    mining.

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    PURPOSE OF DATA MINING IN CRM

    Data mining is an important component of the CRM framework and software. Data mining is

    used for the following purposes in CRM.

    1. Customer ProfilingIdentifying patterns in customer database and this can be applied to the database

    containing prospective customers. This is useful for better customer acquisition. For

    example, choosing customers who are likely to buy a product and sending them the

    product catalogue.

    2. Targeted MarketingPromotions are targeted at customers who are likely to respond to it. Promotions are

    altered to suit the needs of the customer.

    3. Market-basket AnalysisIn retail market, helps retailers to understand what products are purchased together. This

    helps in deciding stock positioning and how to display the items for sale.

    4. Customer RetentionRetaining existing customers by offering attractive promotions based on the data mining

    result obtained as to which customer is likely to leave for a competitor. It is far less

    expensive to retain an existing customer than acquiring a new customer.

    5. Fraud DetectionIt is useful for banks, stock exchanges and insurance companies for detecting fraudulent

    transactions.

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    DATA MINING METHODOLOGY

    The basic steps in data mining for making CRM effective are as follows:

    1. Define business problemEach CRM application will have some specific business objective. The model is to be

    built based on this. An effective problem statement would be to include measurement

    criteria for the CRM project results.

    2. Build marketing databaseThis constitutes the core data preparation. This is the most time consuming step as it

    consists of repeated iterations.

    3. Explore dataA variety of numerical summaries including averages, standard deviations are to be

    gathered and the distribution of data is to be studied. Graphical tools and visual aids can

    be used to get new insights into the otherwise usual data.

    4. Prepare data for modellingThe final step in data preparation consists of 4 steps. Initially the variables on which the

    model is to be built is chosen. It would be better to feed all the data into the data mining

    tool and find out what are the best predictors. But practically speaking this is not viable

    since the time consumed to produce a model with so many variables is really huge. Thesecond step would be to construct new predictors from the raw data. Next a sample or

    subset of the data is chosen on which the model is to be built. A properly selected sample

    will ensure that the model is accurate and robust. Finally the variables are transformed in

    accordance with the algorithm that has been chosen.

    5. Model buildingModel building is an iterative process and alternate models that are most suitable in

    resolving business issues have to be explored. Many of the CRM applications use

    supervised learning protocol. The customer information for which the outcome is already

    known is taken. This is split into two and on one the model is trained and the other data

    set is used to test this model.

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    6. Evaluate modelThe results of are evaluated based on its accuracy and lift (the improvement achieved by

    using this particular model). A prediction that nobody will respond is 99% accurate but

    100% useless. It is preferable to look at the return on investment as a measure of the

    success of the model.

    7. Incorporating data mining in CRM solutionCustomer interaction can be classified into two: they contact company (inbound) or

    company contacts them (outbound). The deployment of data mining in CRM applications

    depends on the type of customer interaction. For outbound interactions the profiles of

    good prospects shown by the model are matched with the profile of the people to whom

    the advertisement, mail or campaign would reach.

    In case of inbound interactions like internet order the data mining model is embedded in

    the application and actively recommends an action.

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    CLASSIFICATION OF DATA MINING TOOLS

    Data mining tools are classified into 3 types based on their functionalities:

    y Description and visualisationy Association and clusteringy Classification and estimation

    DESCRIPTION AND VISUALISATION

    This helps in understanding a given data set by detecting the hidden patterns in the data;

    especially complex and non-linear patterns. This is usually performed before modelling.

    Summary statistics like measures of central tendency and dispersion and graphical

    representations like plots, pie charts are all common description tools. Visualisation tools are

    high on graphic elements and are also highly interactive. For example, the rotating multi-

    dimensional plot allows users to define the multiple variables as well as direction and angle of

    rotation to understand complex relationships. These tools can be used to study relationships

    among variables, to understand people, products and processes. These tools are used as a step in

    constructing better models for data mining.

    ASSOCIATION AND CLUSTERING

    Market basket analysis is a perfect example of association tool in data mining. It

    identifies which items are purchased together. The association tool too identifies which variables

    go together. The result of this can be used in store layout, discount and promotion decisions,

    items bundling etc.

    Clustering requires that a particular group contains items that are similar and items from

    different groups are dissimilar. The common clustering tools used are cluster analysis and self-

    organising map. Customer segmentation is a major application of clustering tool.

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    CLASSIFICATION AND ESTIMATION

    Data mining involves prediction most of the time. Classification can be defined as the

    prediction of a target variable that is categorical in nature. The prediction of a target variable

    based on metrics is estimation. Data mining tools like multiple or logistic regression, neural

    networks and decision trees are used to construct prediction models. Logistic regression is

    similar to regression and is a traditional statistical method. Neural networks are patterned on the

    human brain that has highly inter-connected neurons. This is very useful recognising patterns in

    data.

    Observations are divided into mutually exclusive and exhaustive subgroups for decision

    tree based predictions. The solution is a graphical tree-like structure which is used to model

    complex interactive and non-linear relationships.

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    APPLICATIONS

    CRM covers a range of functions and data mining is not the core for all of these

    functions. Functions like marketing, sales force automation, lead generation can be improved. In

    order to analyse CRM data, the exploration has to be done from different angles and has to be

    looked at from various aspects. This requires the application of different types of data mining

    techniques and their application to different slices of data in an interactive and iterative fashion.

    Many companies in order to outsmart their competitors are opting for data mining and this is

    used as tool to identify new customers and lower costs.

    Data is generally obtained from different sources. If it is CRM data it can come in from

    various departments of the organisation. Hence before an actual data mining is performed the

    data from multiple sources has to be integrated to remove duplication. Integrated mining of

    diverse and heterogeneous data is required.

    There is a lot of inconsistency in data due to missed hits, crawlers etc. This causes

    problems in cleaning the data. Patterns discovered by data mining are often considered as

    hypotheses that need to be tested on new data using rigorous statistical tests for actual acceptance

    of the results.

    Current customer models are built based on the purchase patterns and click patterns of

    customers at web sites. These are very shallow and do not have an in-depth understanding of thecustomers and their individualities. Predictions based on such models tend to be wrong.

    ACQUIRING NEW CUSTOMERS VIA DATA MINING

    Identifying prospects and converting them into customers is the first step of CRM. Data

    mining helps in managing the costs and improving the effectiveness of a customer acquisition

    campaign.Banks and credit companies use direct mail campaigns to acquire new customers. Getting

    people to fill up an application for credit card is just the first step. The bank must decide whether

    the applicant is a good risk and accept them as a customer. It is generally the poor credit risks

    who are more likely to accept the offer than good credit risks. The Big Bank and Credit Card

    Company (BB&CC) uses data mining to improve the returns on acquiring customers. The cost of

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    mailing is about $1.00 per piece for a total cost of $1,000,000. Although this wont precisely

    identify the eventual credit card customers, but will help in focusing marketing efforts much

    more cost-effectively.

    At the outset BB&CC did a test mailing of 50,000 and analysed the results, building a

    predictive model of who would respond (using a decision tree) and a scoring model for credit

    cards (using a neural net). The two models were merged together to identify good credit risks

    and those who are most likely to respond to the offer. The model was applied to 950,000 people

    of which 700,000 were chosen for mailing. The result was that 9,000 acceptable applications for

    credit cards were obtained.

    INCREASING THE VALUE OF EXISTING CUSTOMERS

    CROSS-SELLING VIA DATA MINING

    In order to retain the existing customers they must be provided more value for their

    money. It is a well known fact that getting new customers is much more expensive than retaining

    the existing ones. By cross-selling more value can be added to existing customers and data

    mining is used for this purpose.

    Let us take the example of Guns and Roses (G&R) which specialises in selling antique

    mortars and cannons as outdoor flower pots. Usually around 12 million homes receive their

    catalogue. When a customer calls to place an order, the caller is identified using his caller ID or

    his phone number is asked and this is looked up in the database. This provides G&R an excellent

    opportunity to cross-sell things. But they are reluctant to take it up for the fear of the suggestion

    failing or irritating the customer.

    With the use of data mining this has completely changed. Using the customer information

    in the database and what is ordered by the customer, the model tells to the sales executive what is

    to be recommended. The model even suggested who are likely to be offended if such

    recommendations are made. This was done through a small telephone survey and people who

    declined to participate in the survey were considered to be the ones who would not be interested

    in cross-selling. This was verified by making recommendations to a small subset of people who

    had refused to participate in the survey. But their assumption was not warranted. A second model

    that would predict which offer would be most acceptable was also developed. G&R better

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    understood their customer needs through data mining. The cross-selling models helped in

    increasing their profitability by 2 percent.

    PERSONALISATION VIA DATA MINING

    When each customer is recognised by a companying on an individual basis it adds more

    value for the customer. Big Sams clothing exactly did this by greeting customers by their name

    once they have registered in the website. Based on the customers past transactions, new products

    that are likely to be of use for the customer are suggested in a subtle manner.

    Their initially site did not have any of this greeting by name. An online version of their

    catalogue was put and this did not take advantage of the various sales opportunities available

    online. Data mining boosted their sales on the website. Catalogues usually group items by types

    so that it is easier for the user to select. But in case of online stores the product groups aredetermined by choosing items that will complement the product in consideration. Big Sams site

    can not only look at the item you are going to purchase but also can look into your shopping cart

    and recommend things that will complement it.

    Big Sams used clustering to identify the product groups based on what customers

    purchased together. Some of these groups were very obvious like pants and shirts, while others

    gave new insights like books about snake bit kits and desert hiking. These groups were used to

    make suggestions to customers when one item of the group was purchased. A customer profile

    was developed to identify those customers who would be interested in purchasing the new

    products that were periodically added to their catalogue. This solidified their relationship with

    the customer.

    This personalisation effort did well to Big Sams with significant and measurable

    increase in repeat sales and average size of a sale.

    RETAINING CUSTOMERS VIA DATA MINING

    Acquiring a new customer is more expensive than retaining existing customers for every

    company. Data mining can be of help in this field by identifying those customers who are

    profitable to the organisation and keep them with the company by offering incentives.

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    The customer retention programme of Know Service, an Internet Service Provider (ISP)

    is a classic example for this. They had a very high attrition rate of 8% per month. Replacing

    these customers required $200 for each and considering they had one million customers the cost

    of replacement is very high. Know Service needed to predict the customers who are likely to

    leave. For this they needed to select variables from the customer database that would help in this

    prediction.

    They also needed to identify the profitable customers based on calculations of

    profitability or customer lifetime value. A model to build profiles of their profitable and

    unprofitable customers was developed. This model enabled them to not only retain customers but

    also enabled them to identify customers who would become profitable in the future but who are

    not yet profitable now.

    Using data mining they understood the customer profiles of people who were likely to

    churn. Based on this they even developed programs that would entice people to continue with

    Know Service. The churn project used three models. One model identified people who were

    likely to leave, the second model identified profitable customers who had to be retained and the

    third one tried to entice potential churners with appropriate offers. This reduced their churn rate

    to 7.5% from 8%.

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    DRAWBACKS OF DATA MINING

    It is well understood that data mining has wide variety of applications in customer

    relationship management. Along with this there are some drawbacks in using data mining for

    CRM. An exhaustive mining of a large data set will definitely throw up some interesting patterns

    but these may not be a result of the consumer behaviour but could be just some random

    relationship. Therefore the patterns found might not be useful in improving sales.

    Data mining can be used extensively for predicting based on modelling but this will not

    help in assessing the effectiveness. Using data mining for fishing in the hope of finding patterns

    will be unsuccessful.

    For a data mining application to be successful the user not only has to be well versed with

    the domain area of application but also must be knowledgeable about the data mining

    methodology and tools. Considering these things a data mining team must possess the following:

    1. Domain knowledge2. IT and data mining knowledge and skills3. Statistical and research expertiseEven if all the above mentioned limitations are tackled still a data mining project could

    be a failure due to reasons like lack of management support, inadequate data mining expertise,

    unrealistic expectations of users and improper project management.

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    CONCLUSION

    CRM initiatives have become popular with the advent of technologies like data

    warehousing and data mining applications. For small businesses CRM comes naturally, but as

    businesses expand this becomes more and more difficult for the organisation. In such a situation

    technology driven CRM comes to the rescue. Here the behaviour of the customer is predicted

    based on the information available like previous transactions, customer details etc. Data mining

    can lead to important insights that will help companies to have a closer relationship with their

    customers. The route to a successful business requires that customers and their requirements are

    well understood and data mining if used appropriately can be very helpful in this.

    REFERENCES

    Chye, K. H., & Gerry, C. K. (n.d.). Data Mining and Customer Relationship Marketing in the

    Banking Industry. Singapore Management Review .

    CRM and data mining. (n.d.). Retrieved December 15, 2010, from CRM2Day:

    http://www.crm2day.com/content/t6_librarynews_1.php?id=EpFEkVEFuVdgxAUUNG

    Data Mining Defined. (n.d.). Retrieved December 15, 2010, from Information Management:

    http://www.information-management.com/infodirect/19991220/1744-1.html

    Edelstein, H. (n.d.). Building Profitable Customer Relationships with Data mining.

    Xu, S., & Qiu, M. (2008). A Privacy Preserved Data Mining Framework for Customer

    Relationship Management.Journal of Relationship Marketing, 309-322.