Download - 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