application of data mining for direct marketing 1

8/8/2019 Application of Data Mining for Direct Marketing 1

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APPLICATION OF DATA MINING FOR

DIRECT MARKETING

A Seminar Report

Submitted by

V S MURTHY

In partial fulfillment for the award of the degree

0f

BATCHELOR OF TECHNOLOGY

IN

INFORMATION TECHNOLOGY&ENGINEERING

At

BVC Institute Of Technology & Science

Department of CSE & IT

AMALAPURAM

NOVEMBER-2010


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ABSTRACT

Most data mining Algorithms and Tools when applied to Industrial problems such asCRM(Customer Relationship Management) are useful in pointing out customers who are likelyattritors and customers who are Loyal,but they require human experts to post process thediscovered knowledge manually for Campaigning.This separation of the data mining andCampaign management software introduces considerable inefficiency and opens the door forhuman errors.Tightly integrating the two disciplines present an opportunity for companies togain competitive advantage.

Many industries follow two approaches to sell their products and

services.The first approach is mass marketing which uses mass media such as TV,Radio andNewspapers to advertisement is less effective.

Another approach of promotion is direct marketing.Instead of promoting to customersindiscriminatively,direct marketing studies charecertistices and needs and select certaincustomers,in out case Unloyal(who likely to be loyal in near future)as a target for promotions.Forcollecting huge amount of information on customers is kept in database,data mining applicationcsn be effective for direct marketing.Data mining uses different techniques to automate the process of searching the huge amount of data to find patterns that are good predictors of purchasing behaviors.After mining the data,marketers feed the results into compaignmanagement software which manages the compaign directed at the defind market segments.Italso calculate the Net Profit after this promotions.


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TABLE OF CONTENTS

CHAPTER NO. TITLE PAGE NO.

ABSTRACT ii

ACKNOWLEDGMENT iii

INTRODUCTION DATA MINING

2.1 Defination2.2 Purpose of datamining2.3 Scoring the model

3. DIRECT MARKETING3.1 Defination3.2 Types of direct marketing3.3 Direct marketing working3.4 Diff between direct marketing&adv.

4. SOME DEFINATIONS

4.1 A Datawarehouse4.2 Database marketing

4.3 Compaign management4.5 Scoring on the fly or dynamic scoring4.6 Attrition

5. DATA MINING TECHNIQUES5.1 Supervised classification5.2 Decision tree

5.2.1 Building of decision tree5.2.2 C4.5 algorithm5.2.3 Advantages5.2.4 Applications

6. COMPAIGN MANAGEMENT SOFTWARE

7. INCREASING CUSTOMERVALUE8. INTEGRATING DATAMINING&COMPAIGN

MANAGEMENT SOFTWARE(CMS)

9. PROBLEM DEFINATION & SOLUTIONS

10. DATA MINING DEVELOPMENT PROCESS

10.1 Training data10.2 Entropy Or Information gain10.3 Cost matrix & Net profit calculation


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11. DATAMINING&COMPAIGN MANAGEMENT

IN THE REAL WORLD

11.1 Evaluating the Data mining model(DMM)11.2 Results

12. BENEFITS OF INTEGRATING DMM & CMS

13. CONCLUSIONREFERENCES

INTRODUCTION

Almost all industries that sell products and services need to advertise and promote their productsand services.Banks,Insurance companies and retail stores are typical examples.There aregenerally two approaches to advertisement and promotions:Mass Marketing which uses massmedia such as TV,Radios and Newpapers.However todays world where products areoverwhelming and the market is highly competitive,mass marketing is less effective.The second

Approach of promotion is direct marketing.Instead of promoting to customersindiscriminatively,direct marketing studies customer characterstices and needs and select certaincustomers,in our case Unloyal as a target for promotions.

Now a days,huge amount of information on customers is kept in database.Thusdata mining can be very effective for direct marketing. To be successful, database marketersmust, first, identify market segments containing customers or prospects with high profit potentialand, second, build and execute campaigns that favorably impact the behavior of theseindividuals.

The first task, identifying market segments, requires significant data about prospective customers

and their buying behaviors. In theory, the more data the better. In practice, however, massivedata stores often impede marketers, who struggle to sift through the minutiae to find the nuggetsof valuable information.Recently, marketers have added a new class of software to their targetingarsenal;Data mining application automate the process of searching the huge of data to findpatterns that are good predicators of purchasing behaviors.After mining the data,marketers mustfeed the results into campaign management software that,as the name implies,manages thecampaign direct at the defined market segments.

Data mining,an integration of machine learning,computer visualization andstatistics,has been widely used in direct marketing to target customers.


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DATA MINING:

Data Mining definition:

Data Mining, by its simplest definition, automates the detection of relevant patterns in adatabase. For example, a pattern might indicate that married males with children are twice aslikely to drive a particular sports car than married males with no children. If you are a marketingmanager for an auto manufacturer, this somewhat surprising pattern might be quite valuable.

However, Data Mining is not magic. For many years, statisticians have manually "mined"databases looking for statistically significant patterns.

Today, Data Mining uses well-established statistical and machine learning techniques to buildmodels that predict customer behavior. The technology enhances the procedure by automatingthe mining process, integrating it with commercial data warehouses, and presenting it in arelevant way for business users.

The leading Data Mining products, such as those from companies like SAS and IBM, are nowmore than just modeling engines employing powerful algorithms. Instead, they address thebroader business and technical issues, such as their integration into todays complex informationtechnology environments.

In the past, the hyperbole surrounding Data Mining suggested that it would eliminate the needfor statistical analysts to build predictive models. However, the value that an analyst providescannot be automated out of existence. Analysts will still be needed to assess model results andvalidate the reasonability of the model predictions. Since Data Mining software lacks the humanexperience and intuition to recognize the difference between a relevant and an irrelevantcorrelation, statistical analysts will remain in high demand.

The purpose of Data Mining


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Data Mining helps marketing professionals improve their understanding of customer behavior. Inturn, this better understanding allows them to target marketing campaigns more accurately and toalign campaigns more closely with the needs, wants and attitudes of customers and prospects.

If the necessary information exists in a database, the Data Mining process can model virtuallyany customer activity. The key is to find patterns relevant to current business problems.

Typical questions that Data Mining answers include:

Which customers are most likely to drop their cell-phone service? What is the probability that a customer will purchase at least $100 worth of merchandise

from a particular mail-order catalog?

Which prospects are most likely to respond to a particular offer?Answers to these questions can help retain customers and increase campaign response rates,which, in turn, increase buying, cross-selling and return on investment (ROI).

Scoring the model

Data Mining builds models by using inputs from a database to predict customer behavior. This behavior might be attrition at the end of a magazine subscription, cross-product purchasing,willingness to use an ATM card in place of a more expensive teller transaction, and so on.

The prediction provided by a model is usually called a score. A score (typically a numericalvalue) is assigned to each record in the database and indicates the likelihood that the customerwhose record has been scored will exhibit a particular behavior.

DIRECT MARKETING:

1.What is Direct Marketing?

Direct marketing is just what it sounds like - directly reaching a market (customers and potential

customers) on a personal (phone calls, private mailings) basis, or mass-media basis(infomercials, magazine ads, etc.).

Direct marketing is often distinguished by aggressive tactics that attempt to reach new customersusually by means of unsolicited direct communications. But it can also reach out to existing orpast customers. A key factor in direct marketing is a "call to action." That is, direct marketingcampaigns should offer an incentive or enticing message to get consumers to respond (act).

Direct marketing involves the business attempting to locate, contact, offer, and make incentive-based information available to consumers.


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2. Types of Direct Marketing

Three main types of direct marketing include:

Telemarketing: Direct marketing that involves calling people at home or work to ask fordonations, an opinion, or for sales purposes.

Email Direct Marketing: This form of direct marketing targets consumers through theirEmail accounts. Email addresses can be harvested from websites, forums, or purchased.Some companies require you to receive announcements to use their websites.

Direct Mail Marketing: Advertising material sent directly to home and businessaddresses.

Other types of direct marketing include: distributing flyers; door-to-door solicitations; curbsidestands; FAX broadcasting; television marketing (i.e., infomercials); coupon ads in print media;and voice mail marketing.

3. Does Direct Marketing Work?

That depends on how you define "work." Direct marketing does ensure people know about your

business. But aggressive, misleading, or annoying direct marketing can leave people with a badimpression about your business.

Be sure to adhere to privacy and contact laws because there are stiff fines and penalties for directmarketers that violate direct marketing laws.

4. Should I Consider Direct Marketing?

Every business owner should consider direct marketing. However, the type of direct marketingthat will work for your business depends on your industry, your business ethics, and your budget.

Is There a Difference Between Marketing and Advertising?

There are many technical and complicated definitions of both advertising and marketing and the

differences between them. But it can be stated rather simply:

Advertising tells a story about something to attract attention. Advertising is a step in themarketing process.

In business, marketing is the planning of, and steps taken, to bring merchants and consumerstogether. s for a targeted marketing campaign.

Some Definitions:1..A data warehouse is a repository for relevant business data. While traditional databases primarily store current operational data, data warehouses consolidate data from multipleoperational and external sources in order to attain an accurate, consolidated view of customersand the business.

2.Database Marketing uses information in computerized databases to target offerings tocustomers and prospects.


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3.Campaign Management uses information in a data warehouse or marketing database to plan,manage and assess marketing campaigns designed to impact customer behavior.

4.A customer segment is a group of prospects or customers who are selected from a databasebased on characteristics they possess or exhibit.

5.Scoring on the fly or dynamic scoring is the ability to score an already-defined customersegment within a campaign-management tool. Rather than scoring an entire database, dynamicscoring works with only the required customer subsets, and only when needed.

6.Attrition, sometimes known as churn, occurs when a customer terminates his or herrelationship with a service provider. Marketing efforts usually focus on minimizing churnbecause the cost of bringing a customer back is usually much greater than the cost of retainingthe customer in the first place.

Data mining techniques

Supervised Classification:

Classification is probably the most widely used data mining technique. Most decision making

models are usually based upon classification methods. These techniques, also called classifiers,enable the categorisation of data (or entities) into pre-defined classes.It is separation or orderingof objects(or things) into classes.Classifiction consists of training the system so that new objectsis presented to trained system it is able to assign the object to one of the existing classes.Thisapproach is called Supervised Learning.In supervised learning scheme it is assumed that we havesufficient traning phase. There are many algorithms that can be used for classification, such asdecision trees, neural networks, logistic regression, etc.

Using this data mining technique, the data mining tool learns from examples or the data (datawarehouses, databases etc) how to partition or classify certain objects (it can be an object, anaction, or any other information, that can be formalised). As a result, data mining softwareformulates classification rules.

Decision tree:

A Decision tree is a popular classification technique that result in flowchart like tree structurewhere each node denotes test on a attribute value and each branch represent classes.UsingTraining data Decision tree generate a tree that consists of nodes that are rules and each leaf noderepresent a classification or decision.The data usually plays important role in determining thequality of the decision tree.If there are number of classes,then there should be sufficient trainingdata available that belongs to each of the classes.Decision trees are predictive models,used tographically organize information about possible options,consequences and end value.They areused in computing for calculating probabilities.

Building a Decision tree:

Decision tree learning algorithms,such as ID3 or C4.5 are among the most powerful and popular predictive methods for classification.In Direct Marketing applications,a Decision tree can bebuilt from a set of examples(customers) described by a rich set of attributes including customer personal information such as name,sex and dirthday etc. financial information (annualincome,expenditure),purchase information (number of purchase made,logins made),feedbackinformation (product availability,service,cost of products)so on.


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In this we implemented C4.5 algorithm,it is used to generate a decision tree developed by RossQuinlan.The decision tree generated by C4.5 used for classification.

C4.5 Algorithm:

C4.5 Builds decision tree from set of training data using the concept of Information entropy.Thetraining data is a set S=s1,s2of already classified samples.Each sample si=x1,x2is a vectorwhere x1,x2.represent attributes or features of the sample.The training data is augmented witha vector C=c1,c2,..where c1,c2, represent the class to which each sample belongs.At each nodeof the tree,C4.5 choose one attribute of the data that most effectively splits its set of samples intosubsets enriched in one class or the other.Its criterion is the normalized information gain thatresults from choosing an attribute for splitting the data.The data with the highest normalizedinformation gain is chosen to make the decision.The C4.5 algorithm then recourses on thesmaller sublists.In general,steps in C4.5 algorithm to build decision tree are:

1.Choose attribute for root node.

2.Create branch for each value of that attribute.

3.Split cases according to branches.

4.Repeat process for each branch until all cases in the branch have the same class.

Choosing which attribute to be a root based on highest gain of each attribute.To count thegain,we use Information Gain formula

Gain(S,A)=Entropy(S)-(|St|/|S|)xEntropy(Si)..(1)

Where {S1..SiSn}=partitions of S according to values of A.

N=Number of attributes A.

|Si|=Number of cases in the partition Si,

|S|=Total number of cases in S.

While Entropy is given by Formula 2

Entropy(S)= -p

Where

S=Case set

n=Number of cases in the partition S,

pi=proportion of Si to S.

Decision tree advantages:

Amongst other data mining methods, decision tree is the method that has several advantages

Intuitively comprehensible classification model. People are able to understand decisiontree models after a brief explanation.

Data preparation for a decision tree is basic or unnecessary. Other data miningmethods often require data normalisation, dummy variables need to be created and blankvalues to be removed.

Rules generation in the fields where experts have difficulties with formalising theirknowledge.


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Decision tree is a white box model. If a given situation is observable in a model theexplanation for the condition is easily explained by boolean logic. An example of a black box model is an artificial neural network since the explanation for the results isexcessively complex to be comprehended.

It is possible to validate a model using statistical tests, neural nets and others. Thatmakes it possible to account for the reliability of the model.

Is robust, perform well with large data in a short time. Large amounts of data can beanalysed using personal computers in a time short enough to enable stakeholders to takedecisions based on its analysis.

Because of these and many other reasons, decision trees technique is an important data miningmethod for any scientist dealing with data analysis, no matter if he is a theorist or an expert.

FIELDS of Decision Trees Applications:

Decision trees are an excellent tool in decision-makingand data mining systems. They can be of good service to any analyst or manager.In business,decision trees are constructed in order to help with decision making process.Decision trees aresuccessfully used to solve real-world problems in the following fields:

Banking. Estimation of clients creditworthiness when giving credits. Industry. Production quality control (faults identification), non-destructive tests (like

checking weld quality), etc.

Medicine. Diagnostics of various diseases. Molecular biology. Analysis of amino acids composition.

This is by no means full list of the fields where decision trees can be of use.

The role of Campaign Management software:

Database marketing software enables companies to deliver to customers and prospects timely, pertinent, and coordinated messages and value propositions (offers or gifts perceived asvaluable).

Todays Campaign Management software goes considerably further. It manages and monitorscustomer communications across multiple touch-points, such as direct mail, telemarketing,customer service, point-of-sale, e-mail and the Web.

Campaign Management automates and integrates the planning, execution, assessment andrefinement of possibly tens to hundreds of highly segmented campaigns running monthly,weekly, daily or intermittently. The software can also run campaigns that are triggered inresponse to customer behavior or milestones such as the opening of a new account.

Increasing customer lifetime value


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Consider, for example, customers of a bank who only use the institution for a checking account.An analysis reveals that after depositing large annual income bonuses, some customers wait fortheir funds to clear before moving the money quickly into their stock-brokerage or mutual fundaccounts outside the bank. This represents a loss of business for the bank.

To persuade these customers to keep their money in the bank, marketing managers can use

Campaign Management software to immediately identify large deposits and trigger a response.The system might automatically schedule a direct mail or telemarketing promotion as soon as acustomers balance exceeds a predetermined amount. Based on the size of the deposit, the

triggered promotion can then provide an appropriateincentive that encourages customers to invest their money in the banks other products.

Finally, by tracking responses and following rules for attributing customer behavior, theCampaign Management software can help measure the profitability and ROI of all on goingcampaigns.

Integrating Data Mining and Campaign Management

The closer Data Mining and Campaign Management work together, the better the business

results. Today, Campaign Management software uses the scores generated by the Data Miningmodel to sharpen the focus of targeted customers or prospects, thereby increasing response ratesand campaign effectiveness.

Unfortunately, the use of a model within Campaign Management today is often a manual, time-intensive process. When someone in marketing wants to run a campaign that uses model scores,he or she usually calls someone in the modeling group to get a file containing the databasescores. With the file in hand, the marketer must then solicit the help of someone in theinformation technology group to merge the scores with the marketing database.

This disjointed process is fraught with problems:

The large numbers of campaigns that run on a daily or weekly basis can be difficult toschedule and can swamp the available resources.

The process is error prone; it is easy to score the wrong database or the wrong fields in adatabase.

Scoring is typically very inefficient. Entire databases are usually scored, not just thesegments defined for the campaign. Not only is effort wasted, but the manual processmay also be too slow to keep up with campaigns run weekly or daily.


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The solution to these problems is the tight integration of Data Mining and CampaignManagement technologies. Integration is crucial in two areas:

First, the Campaign Management software must share the definition of the defined campaignsegment with the Data Mining application to avoid modeling the entire database. For example, amarketer may define a campaign segment of high-income males between the ages of 25 and 35

living in the northeast. Through the integration of the two applications, the Data Miningapplication can automatically restrict its analysis to database records containing just thosecharacteristics.

Second, selected scores from the resulting predictive model must flow seamlessly into thecampaign segment in order to form targets with the highest profit potential.

The integrated Data Mining and Campaign Management process:

This section examines how to apply the integration of Data Mining and Campaign Managementto benefit the organization. The first step creates a model using a Data Mining tool. The secondstep takes this model and puts it to use in the production environment of an automated database

marketing campaign.Step 1: Creating the model

An analyst or user with a background in modeling creates a predictive model using the DataMining application. This modeling is usually completely separate from campaign creation. Thecomplexity of the model creation typically depends on many factors, including database size, thenumber of variables known about each customer, the kind of Data Mining algorithms used andthe modelers experience.

Interaction with the Campaign Management software begins when a model of sufficient qualityhas been found. At this point, the Data Mining user exports his or her model to a CampaignManagement application, which can be as simple as dragging and dropping the data from one

application to the other.This process of exporting a model tells the Campaign Management software that the modelexists and is available for later use.

Step 2: Dynamically scoring the data

Dynamic scoring allows you to score an already-defined customer segment within yourCampaign Management tool rather than in the Data Mining tool. Dynamic scoring both avoids


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mundane, repetitive manual chores and eliminates the need to score an entire database. Instead,dynamic scoring marks only relevant customer subsets and only when needed.

Scoring only the relevant customer subset and eliminating the manual process shrinks cycletimes. Scoring data only when needed assures "fresh," up-to-date results.

Once a model is in the Campaign Management system, a user (usually someone other than the person who created the model) can start to build marketing campaigns using the predictivemodels. Models are invoked by the Campaign Management System.

When a marketing campaign invokes a specific predictive model to perform dynamic scoring,the output is usually stored as a temporary score table. When the score table is available in thedata warehouse, the Data Mining engine notifies the Campaign Management system and themarketing campaign execution continues.

Problem Definition and Solutions

Problem Definition:Campaigning

The Business for example Mobile store that sell products like Mobile Phones,ipods and so

on,collects huge amount of information on customers and kept in databases.The response rate,thepercent of customers who actullay buy the product after visiting the site or login,is often low.Foreffective selling of these products there two cases:

1.Transfer this database to campaign management software which suggest actions consideringcurrent market segments.

2.Mass Marketing i.e. Paper media(newspaper,letters,fax),Electronic Media(TVCommercials,Radio,email,SMSs)irrespective of the customer status such as Loyal orUnloyal.However in Todays world where products are overwhelming and marketing is highlycompietative,The responses rate ,the percent of the people who actually buy the product after isoften low.Both the solutions are not cost effective.

Reality of business:1.it is more expansive to win back a customer after they left thanit is keep them satisfied in thefirst place.

2.Companies must spend far more money to get a new customer than to retain as existingcustomer.

3.it is far easier to sell a new product to an existing customer than it is to a new customer.

Solutions:Direct marketing using data mining

Instead of promoting to customer indiscriminatively,direct marketing studies customer profileand select certain customers (Unloyal) as target for promotions.This application increase the net

profit by decreasing campaign cost.The DFD(Data Flow Diagram)for this application is show infig1.

Data mining model development process

Traning data:For building a data mining model we consider an artificial example of buildingdecision tree classification model for online mobile store as show in table 1.There are 12 samplesand two classes.The frequencies of two classes are:Yes=7 No=5


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Customertype Gender Availabilityof product Product isreasonable Referencesmade No.oflogins Status Select forcampaignIndividual Male Satisfied True 3 8 Loyal NoRetailer Male Satisfied True 0 7 Loyal NoIndividual Male Satisfied True 5 1 Unloyal YesDistributor Male Unsatisfied True 2 2 Unloyal YesDistributor Female Satisfied False 3 2 Unloyal YesIndividual Male Unsatisfied True 4 3 Loyal NoIndividual Male Satisfied True 2 3 Unloyal YesIndividual Male Satisfied True 5 5 Loyal NoDistributor Female Satisfied False 2 4 Unloyal YesDistributor

Female

Unsatisfied

False

1

2

Unloyal

Yes

Retailer Female Unsatisfied False 0 3 Unloyal YesRetailer Female Satisfied False 0 1 Unloyal No

Table 1:Training data for online mobile store

Entropy or information gain:

Information gain or entropy in data due to uncertainty of customer regarding the risk class eachcustomer belongs to is given by,

E=-(7/12)log(7/10)-(5/12)log(5/12)=0.98

Similarly we consider using each attribute in turn as a candidate to split the sample.Byusing formula 1 & 2 in C4.5 algorithm we got values of Gain(S,A) and Entropy(S) a shown intable 2.

The Gain at attribute status in larger than any other attribute,hence status is a split attribute.Nowwe reduce the data by removing the attribute status the decision terr building algorithm givenearlier continues until either all leaf nodes are single nodes or no more attribute are available forsplitting a node that has objects of more than one class.

Potential split attribute Entropy before split Entropy after split GainCustomer type 0.98 0.632 0.348Gender

0.98

0.812

0.162

Availability of product 0.98 0.573 0.407Cost is reasonable? 0.98 0.820 0.160Reference made 0.98 0.586 0.394

No of logins 0.98 0.592 0.388


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Status 0.98 0.542 0.438Table 2:Gain calculation for seven attribute

Cost matrix & net profit calculation:

Actions:In order to change the classification of a customer X from loyal and unloyal, one may

need to apply more than one attribute-value change action. An action A is defind as a change toan attribute value fir as attribute attr.

Suppose that for a customer X,the attribute attr has an original value u.To change its value tov,an action is needed,U is probability that we got,V is what we expecting.

Therefore action A is to be taken on customer so that he is loyal,and profit is also not affected.This action A is defind as A=(attr,u-v).

Cost matrix: . To improve classification decision trees and to get better models with such'skewed data', the Tree heuristic automatically generates an appropriate cost matrix to balancethe distribution of class labels when a decision tree is trained. You can also manually adjust thecost matrix.

A cost matrix (error matrix) is also useful when specific classification errors are more severethan others. The Classification mining function tries to avoid classification errors with a higherror weight. The trade-off of avoiding 'expensive' classification errors is an increased number of'cheap' classification errors. Thus, the number of errors increases while the cost of the errorsdecreases in comparison with the same classification without a cost matrix.

Here we calculating the probability for each customer respectively. Then on the basis of this weclassify then as Loyal or Unloyal. Afterwards we have taken action for a particular Unloyalcustomer for that we have to specify values like cost for action,destination probability and totalprofit accordingly we calculate the net profit, for that we use the following formulas

Probability=Destination probability-Customer probability

Then,

Net profit=(total profit*(Probability))-cost of action

Data Mining and Campaign Management in the real world

Ideally, marketers who build campaigns should be able to apply any model logged in theCampaign Management system to a defined target segment. For example, a marketing managerat a cellular telephone company might be interested in high-value customers likely to switch toanother carrier. This segment might be defined as customers who are nine months into a twelve-month contract, and whose average monthly balance is more than $150.

The easiest approach to retain these customers is to offer all of them a new high-tech telephone.

However, this is expensive and wasteful since many customers would remain loyal without anyincentive.

Instead, to reduce costs and improve results, the marketer could use a predictive model to selectonly those valuable customers who would likely defect to a competitor unless they receive theoffer.

Evaluating the Benefits of a Data Mining Model


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Figure 1-1, which shows a "gains chart," suggests some benefits available through data mining.The diagonal line illustrates the number of responses expected from a randomly selected targetaudience. Under this scenario, the number of responses grows linearly with the target size.

Figure 1-1Gains Chart

The top curve represents the expected response if you allow the model scores to determine thetarget audience. The target is now likely to include more positive responders than in a randomselection of the same size. The shaded area between the curve and the line indicates the qualityof the model. The steeper the curve, the better the model.

Other representations of the model often incorporate expected costs and expected revenues toprovide the most important measure of model quality: profitability. A profitability graph such asFigure 1.2 can help determine the number of prospects to include in a campaign.

Figure 1-2Profitability Chart

In this example, it is easy to see that contacting all customers will result in a net loss.

Results:

The application proposed in this paper is combination of data mining and campaign managementwhich increases net profit,we are proposing this using the following table3.

Table :Net profit Estimation

Previous system Our applicationNumber of customers selected for campaign 1,00,000 30,000(30%)Campaign cost(TV,radio etc) 1,00,000.00(Rs) 0.00


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Data mining software&campaign management software cost 80,000.00(separated) 50000.00(combine)A-total promotion cost(Rs) 1,80,000.00 50,000.00Response rate 1% 3%

Number of sales 1000 900B-profit from sales(Rs.70 each) 70,000.00 63,000.00

Net profit from promotion(A-B) -1,10,000.00 13000.00Lift analysis has been widely used in database marketing previously.A lift reflects theredistribution of responders in the testing data after they are ranked,using this lift curve for ourapplication,we are proposing that selecting number of customers between 10% to 30% who areUnloyal will give Maximum Net Profit.

The Benefits of integrating Data Mining and Campaign Management:

For marketers:

Improved campaign results through the use of model scores that further refine customerand prospect segments.

Records can be scored when campaigns are ready to run, allowing the use of the most recentdata. "Fresh" data and the selection of "high" scores within defined market segments improvedirect marketing results.

Accelerated marketing cycle times that reduce costs and increase the likelihood ofreaching customers and prospects before competitors.

Scoring takes place only for records defined by the customer segment, eliminating the need toscore an entire database. This is important to keep pace with continuously running marketingcampaigns with tight cycle times.

Accelerated marketing "velocity" also increases the number of opportunities used to refine andimprove campaigns. The end of each campaign cycle presents another chance to assess resultsand improve future campaigns.

Increased accuracy through the elimination of manually induced errors. The CampaignManagement software determines which records to score and when.

For statisticians:

Less time spent on mundane tasks of extracting and importing files, leaving more time forcreative building and interpreting models. Statisticians have greater impact oncorporate bottom line.


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CONCLUSION:

Our proposed application have powerful capabilities that will benefited for companiesdoing online business statisties released by the European Unions Eurostat data agencyindicate that in 2003,32 percent of individuals aged between 16 and 74 bought at leastone item or service on the internet within the past twelve months.Those aged between 25and 34 accounted for the highest share of online purchase will definitely increase infuture too. Today the business world is facing a recession and applying techniques suchas Deduction in man power,salary,other Miscellaneous expenses to stay profitable. Our

application is powerful solution to stay profitability in recession.

REFERENCES:


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1.Qiang Yang,senior Member,IEEE,Jie Yin,Charless Ling,and Rong Pan,(2007)Extracting Actionable knowledge from decision trees.IEEE transaction on knowledge anddata engineering.

2.M.S.Chen,J.Han,and P.S.Yu(1996),Data Mining:An Overview from a databaseperspective,IEEE Trans.Knowledge and data Eng.,vol.8,pp.866-883.

3.J.R.Quinlan,Morgan Kaufmann,(1993)C4.5 Program for Machine Learning.

4.G.K.Gupta,(2006),Introduction to data mining with case study,Prentice Hall of India.

5.http://www.thearling.com/text/whexcerpt/whexcerpt.htm.

6.http://bx.businessweek.com/European-e-commerce.

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