Data Mining techinques

7/29/2019 Data Mining techinques

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Foundations of DataMining Process of using raw data to infer important business relationships.

Collection of powerful techniques intended for analyzing largeamounts of data.

There is no single data mining approach, but rather a set of techniquesthat can be used stand alone or in combination with each other.

The non-trivial extraction of novel, implicit, and actionable knowledge

from large datasets. Extremely large datasets

Discovery of the non-obvious

Useful knowledge that can improve processes

Can not be done manually


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Data Mining Is NOT Data warehousing

Software Agent

Online Analytical Processing Data Visualization

Presenting data in different ways

Blind application of algorithms Brute-force crunching of bulk data


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Data Mining Applications


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Applications - Retail Performing basket analysis

Sales forecasting

Database marketing

Merchandise planning and allocation


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Applications Bank Card marketing

Cardholder pricing and profitability

Fraud detection

Predictive life-cycle management


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What can be done withdata mining ? Fraud/Non-Compliance Anomaly detection

Isolate the factors that lead to fraud, waste and abuse

Target auditing and investigative efforts more effectively

Service Delivery and Customer Retention

Build profiles of customers likely to use which service

Recruiting/Attracting customers

Maximizing profitability (cross selling, identifying profitablecustomers


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The whole process of collecting, storing, organizing, and analyzing data

using data warehouse systems is called data warehousing.

Data mining, on the other hand, is the process of making use of

collected data for analysis and statistics.

Enterprise

Database

Customers

Etc

Vendors Etc

Orders

Data

Warehouse

Transactions

Data Mining


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Data Warehouse

For organizational learning to take place, data from many sourcesmust be gathered together and organized in a consistent and useful

way hence, Data Warehousing (DW)

DW allows an organization (enterprise) to remember what it has

noticed about its data

Data Mining techniques make use of the data in a DW


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Data Warehousing Objectives Keep the warehouse data current

Ensure that the warehouse data is accurate

Make the warehouse data secure and easilyavailable to authorized users

Maintain descriptions of the warehouse data

for system developers and users can

understand the meaning of each element.


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A Data Warehouse SystemModel


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Data Warehouse Security


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CharacteristicsSubject oriented Data are organized by how users

refer to it

Integrated Inconsistencies are removed in

both nomenclature and

conflicting information; (i.e. data

are clean)

Non-volatile Read-only data. Data do not

change over time.

Time series Data are time series, not current

status


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Summarized Operational data are mapped into

decision usable form

Larger Time series implies much more data isretained

Non normalized Data can be redundant

Metadata =Data about data

Input Unintegrated, operational en-vironment

(legacy systems)


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Steps in data warehousingproject cycle Requirement Gathering

Physical Environment Setup

Data Modeling

ETL

OLAP Cube Design

Front End Development

Report Development

Performance Tuning

Query Optimization Quality Assurance

Rolling out to Production

Production Maintenance

Incremental Enhancements


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Customer relationship management (CRM) means generating high levels of

profitable customer satisfaction through the use of knowledge generated

from CRM applications using corporate and external data.

CRM is based on the simple notion that the better one knows ones

customers, the better one can maintain long-lasting, valuable relationships

with them.

The goal of CRM is to maximize relationships with customers over time,

focusing on all aspects of the business, from marketing, sales, operations

and service, to establishing and sustaining mutually beneficial customer

relations.

In order to accomplish that, the organization must develop a single,

integrated view of each customer.


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Definitions is a business strategy with outcomes

that optimize profitability, revenue and customer satisfaction

by organizing around customer segments,

fostering customer-satisfying behaviors and

implementing customer-centric processes.

is a strategy

used to learn more about customers' needs and behaviors

in order to develop stronger relationships with them.


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History of CRMB&S CIMS CRMRM

Time line

e-CRM

Late 80s Mid 90s 2002 - FutureEarly 90s

B&S Buying & Selling

RM Relationship Marketing

CIMS Customer Information Management Systems

CRM Customer Relationship Management

e-CRM- A subset of CRM that focuses on enabling customer interactions via e-channels


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Underpinning Theory Customers have many points of contact with an organization

Retaining customers is far most cost effective than recruiting

new ones

Some customers are more profitable than others

The 80/20 rule

For most firms, 80 percent ofprofitcomes from 20 percentof customers

Use of Technology


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Complete customer-centric end-to-end processes through ConnectedCRM

Seamless integration of Partners beyond enterprise boundaries throughCollaborative CRM

Business process support benefiting from 30 years of SAP industryexperience - Industry-specific CRM

Ease of use for all employees, partners and customers involved through- People-Centric CRM

Why CRM


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CRM A complete Solution


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Three phases of CRM Acquiring New Relationships

acquire new customers by promoting companys product and

service leadership.

Enhancing Existing Relationships enhance the relationship by encouraging excellence in cross-

selling and up-selling, thereby deepening and broadening the

relationship.

Retaining Customer Relationships Retention focuses on service adaptability delivering not what

the market wants but what customers want.


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CRM Applications


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Data Mining in CRM Customer Life CycleThe customer life cycle consists of the different stages in the

relationship between a customer and a business.

KEY STAGES

Prospects: people who are not yet customers but are in thetarget market

Responders:prospects who show an interest in a product orservice

Active Customers: people who are currently using the productor service

Former Customers:may be bad customers who did not paytheir bills or who incurred high costs


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What marketers want ?

- Increasing customer revenue- Customer profitability

- Up-sell

- Cross-sell

- Keeping the customers for a longer period of time

Solution- Applying data mining

Data Mining helps to

Determine the behavior surrounding a

particular lifecycle event

Find other people in similar life stages and

determine which customers are following similar

behavior patterns


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PROCESSING CUSTOMERINFORMATION USING DATAWAREHOUSING


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The Benefits of CRM to IndustriesWorldwideCall center efficiency increases

Marketing campaigns are made easier

Account information

Overall revenue increases

Cost reduction is achieved

Better customer service is achieved

Organizations can gain the competitive edge

Organizations can concentrate more on production

Constant supply of vital customer data

Customers receive satisfaction

Routine tasks are easier to handle

Marketing and support expenses are reduced

Sales teams can be effectively monitored

Teamwork within the organization is achieved

Communication channels are improved


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CRM for Large Industries

CRM for Small Industries

Documents

Data Mining techinques