Data Mining Data Mining Techniques for CRMTechniques for CRM

Paul J.C. ChangEneida Lau

Ximena Salazar Lester Arellano

José-Pablo González Edith Quispe

Data Mining in CRM ...Data Mining in CRM ...

“ ...through data mining – the extraction of hidden predictive information from large databases – organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions.”

AgendaAgenda Introduction, Definition: Paul

The Evolution & Apps. of Data Mining: Eneida

Internal Considerations & Data mining techniques: Ximena

Data mining and CRM – relationship & customer privacy: Lester

Case Studies (Neural Networks, CHAID): JPG

CHAID vs neural nets; Conclusions: Edith

IntroductionIntroduction Product-oriented view VS. Customer-oriented view

Design-build-sell VS. sell-build-redesign One-on-one marketing VS. mass marketing Goal of revolution: Establish a long term relationship with each customer

The advent of the Internet and technological tools accelerate modern CRM revolution CRM is important for B2C or C2B, and even more crucial in B2B environments

Why Data Mining?Why Data Mining? Between businesses and customers…

Collecting customer demographics and behavior data makes precision targeting possible Helps to devise an effective promotion plan when new products developed Creates and solidifies close customer relationships

Between businesses… Helps to smooth transactions, communications and collaboration Simplifies and improves logistics and procurement process

What is Data Mining?What is Data Mining? “…a sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data.” “…another way to find meaning in data.” Data mining is part of a larger process called knowledge discovery

What Data Mining is ~NOT~What Data Mining is ~NOT~

• Data mining software does notnot eliminate the need to know the business, understand the data, or be aware of general statistical methods.

• DM does notnot find patterns or knowledge without verification

• DM helps to generate hypotheses, but it does notnot validate the hypotheses

Evolutionary Stages of Data MiningEvolutionary Stages of Data Mining

(1960’s)

•Retrospective,static data delivery

•Summations or averages

•Computers, tapes, disks

•IBM, CDC

Data Collection

Data Access

Data Navigation

Data Mining

(1980’s)

•Retrospective,dynamic data delivery at record level

•Branch sales at specific period of time

•RDBMS, SQL, ODBC

•Oracle, Sybase, Informix, IBM, Microsoft

(1990’s)

•Retrospective,dynamic data delivery at multiple level

•Global view or drill down

•OLAP, multidimensional databases, data warehouses

•Pilot, IRI, Arbor, Redbrick

(2000’s)

•Retrospective,Proactive information delivery

•Online analytic tools, feedback and information exchange

•Adv. Algorithms, multiprocessor, computers, massive databases

•Lockheed, IBM, SGI

Breakdown of Data Mining from a Breakdown of Data Mining from a Process OrientationProcess Orientation

Data Mining

Discovery Predictive Modeling

ForensicAnalysis

•Conditional Logic

•Affinities and Associations

•Trends and Variations

•Outcome Prediction

•Forecasting

•Deviation Detection

•Link Analysis

Applications of Data MiningApplications of Data Mining

RetailRetail BankingBanking TelecommunicationsTelecommunications

1. Performing basket analysis

2. Sales forecasting

3. Database marketing

4. Merchandise planning and allocation

1. Card marketing

2. Cardholder pricing and profitability

3. Fraud detection

4. Predictive life-cycle management

1. Call detail record analysis

2. Customer loyalty

OTHER APPLICATIONSOTHER APPLICATIONSCustomer

Segmentation

Manufacturing

Warranties

Frequent flierincentives

Discrete segments by

adding variables Customize Products.

Predict features

No. clients who will ask for claims

Identify groups who can receive

incentives

INTERNAL CONSIDERATIONSINTERNAL CONSIDERATIONS

Skillsets and technologies must be available to integrate them

Data mining Decision-making process

Knowledgegained

through DM

• Sell to and service customers• Manage inventory• Supervise employees • Work to correct and prevent loss

-An algorithm for scoring

-A score for particular customer, employee

-An action associated with a customer, employee or transaction

DATA MINING TECHNIQUESDATA MINING TECHNIQUES

They are applied to tasks of predictive modeling and forensic analysis

DMApproaches

Data Retained

Data distilled

NearestNeighbor

Case-BasedReasoning

Logical

CrossTabulational

Equational

Numeric and Non-numeric

NumericData

Non-numericData

They extract patterns and then use for various purposes

CUSTOMER RELATION MANAGEMENTCUSTOMER RELATION MANAGEMENT

• Know• Target• Sell• Service

Definition

CRM: Development of the offer

3 Which’s

2 Stage Concept

1 - From product to customer orientation- Market Strategy from outside-in

2 -Push the development of customer orientation-Innovating value proposition

Components of CRMComponents of CRM

Customer Information Customer

Data

Internal Customer

Data

Outside Source Data

•Billing Records

•Surveys

•Web logs, Credit Card records

Data Warehouse

•External data sources

Current Address, Web page viewing profiles.

Historical Data

Analyze the Data Data Mining Techniques

+ Customer Oriented

Campaign Execution &

Tracking

Interactions between MKT, information, Tech and sales channels

Data Mining & CRMData Mining & CRM• The Relationship

– Customer Life Cycle• Prospects• Respondents• Active Customers• Former Customers

Inputs

What information is available

Data Mining Output

What is likely to be interested

Data Mining & CRMData Mining & CRM

• Inputs– Prospects Data Warehouse in other industries– Click Stream Information

• Market Data Intelligence– DM can predict behavior of customer (CLC) and match it

with any market event (a,i. I pod nano)

• Data Mining and Customer Privacy– Privacy Bill of Rights, Independent verification of

security policies. – Create an anonymous architecture for handling

customer info.

Case StudiesCase Studies

Neural Networks vs. CHAID

Case #1Case #1

Neural Networks

Neural NetworksNeural Networks

• The exact way in which the brain enables thought is one of the great mysteries of science

NeuronsNeurons

NeoVistas Solutions’ Decision SeriesNeoVistas Solutions’ Decision Series

• For retail, insurance, telecommunications, and healthcare.

• Includes discovery tools based on neural networks, clustering, genetic algorithms, and association rules

The problemThe problem

• Large retailer• Over $1 billion in sales• Overstocked on slow-moving products • Under-stocked on most popular items at

critical selling periods.

SolutionSolution

• With Clustering and and NN:– Review point-of-sale history and equate

store groupings to sales patterns.– Forecast stocking requirements on a

store-by-store basis.

ResultsResults

• Management is able to forecast seasonal trends at the store-item level.

• The Decision Series tools showed that clustering similar items into actionable groups streamlined the ordering process.

• Revenues increased by 11.6%

Case #2Case #2

CHAID

Applied MetrixApplied Metrix

• Uses a combination of CHAID segmentation and logistic regression response probability modeling to establish predictive models that are deployed over a proprietary Internet system

The problemThe problem

• Home equity marketer that extended home equity lines of credit at the national level.

• The client’s goal was to increase the efficiency of targeting current mortgage customers who might be interested in the client’s service.

The SolutionThe Solution

• CHAID identified 16 distinct market segments.

• In particular, one particular segment accounted for 65% of responses to the mailing.

ResultsResults

• The highest-rated group from the predictive model had by far the highest response rate to the equity line of credit campaign—85% above average for the direct mailing,

• The goal of the program was a 10% increase in response rate, but the actual response rate increased 30%.

• The firm was able to increase profits by over one million dollars in the first year after implementation.

CHAID vs. Neural NetworksCHAID vs. Neural Networks

Clarity and explicabilityClarity and explicability- CHAID model is understandable as a set of rules- Neural Network is obscure

Implementation/integrationImplementation/integration- The CHAID model is much easier to be

implemented that a Neural Network.- The risk of missing code by an IT department is

slim for a CHAID model and higher for a Neural Network.

Data RequirementsData Requirements- The data for both techniques requires some

pre-processing. - Neural Network require the data be

transformed into binary format.

Accuracy of modelAccuracy of model- Neural Networks provide more accurate

models, especially for complex problems.

Construction of modelConstruction of model- CHAID is easier and quicker to construct.- Neural Networks have many parameters that

must be set and require more skilled manipulation.

CostCost- Building a Neural Network is more costly then

building a CHAID model.

AplicationsAplications- CHAID and Neural Networks can create

predictive models.

- Neural Networks can handle both categorical and continuous independent variables, but these have to be transformed to 0/1 input variables.

- When all or most of the independent variables are continuous, neural networks should perform better than CHAID.

AplicationsAplications- The Neural Networks and CHAID can be used

to solve sequence prediction problems.

- Neural Networks can be used to solve estimation problems.

- CHAID provides good solutions to classification problems, can be used for exploratory analysis and can provide descriptive rules.

- An interesting development is the combination of these two techniques to create “neural trees”.

CONCLUSIONS CONCLUSIONS - The choice among different

options is not as the choice to use data mining technologies in a CRM initiative.

- Data Mining represents the link from the data stored over many years through various interactions with customers in diverse situations, and the knowledge necessary to be successful in relationship marketing concepts.

- Through the full implementation of a CRM program, which must include data mining, organizations foster improved loyalty, increase the value of their customers, and attract the right customers.

- As customers and businesses interact more frequently, businesses will have to leverage on CRM and related technologies to capture and analyze massive amounts of customer information.

CONCLUSIONS CONCLUSIONS

CONCLUSIONS CONCLUSIONS

- CRM solutions focus primarily on analyzing consumer information for economic benefits, and very little touches on ensuring privacy.