Current CRM Systems such as Siebel and BroadVision provide a 
significant step forward  in the ability of an organisation to manage customer data in order to understand, 
evaluate and respond to customers more effectively. 
Call Centres and Web Sites are creating a range of new "touch points" from which to gather customer data.
This data can then be analysed to identify patterns of buying and usage.
A marketing strategy must be geared toward achieving a One-to-One relationship with the Customer, and an 
"individual feedback loop" where customers feel that they are known, remembered, listened to, and offered 
the appropriate product or service. 
Building such an intimate relationship with customers not only makes them loyal, but it increases 
their lifetime value and profitability.
CRM Data
This creates a need to handle Customer data consistently across the organisation and 
provide faciltiies for integration within the organisation.
The integration of this data from a range of source with exsting Customer data is of 
vital importance in achieving a unified view of the customer.
If the ultimate goal of an enterprise is to develop synergy with customers through 
interactive relationships, then it is necessary to build an enterprise view of that customer. 
Inherently, this requires creating an "infostructure" that integrates data from all 
Business Units and operational systems, enriches it with external data, and provides 
analysis facilities for CRM Planning and Delivery. 
A comprehensive marketing database will contain consolidated historical records of 
billing, usage and service data across all lines of business with current transaction
This consolidated Customer Data is a strategic asset for deployment of multi-phase 
marketing and customer relationship programs.
Data Integration
The goal of data integration is to identify, validate and consolidate strategic data about 
your customers into a central repository to be accessed for decision support. 
The successful integration of marketing and operational data depends on two key factors:
1. Data integration (See Chart) of disparate data sources
2. Creation of application specific data marts
Elements of the data integration process include:
Data Knowledge and Awareness
To gain a thorough understanding of source data, your analysis must include: 
Domain/Range Investigation: What is the definition of accurate data? 
Data Transformation Rules: Determine rules and conditions for converting business data to 
meet end-user needs. 
Frequency Analysis : How accurate is the data? What is the frequency of update? 
Quality Assurance : Is the data reliable, are there inconsistencies that can be identified
and corrected ?
Conversion : Multi-sourced data files need to be edited and reformatted in a uniform manner. 
Sophisticated algorithms are used to standardize data by identifying the location, length 
and accuracy of each data field. The end result is a set of uniform data records with 
identifiable lines of customer address and associated names. Professional titles, gender 
codes or unique ID numbers can be added to each record for personalized responses at 
customer touch points. 
Standardization : All customer address information is verified and corrected to meet U.S. 
Postal Service deliverability standards. In addition to gathering precise delivery point 
data on home, business and SOHO accounts, Address Standardization results in better service
 at the customer touch point. 
As a common thread of information that links records across legacy systems, accurate 
address data is a vital cross-reference for the delivery of interactive customer reference,
 verification, sales and follow-up direct marketing programs. 
Consolidation And Enhancement 
Sophisticated merge/purge software is a powerful tool for consolidating household and 
business data from various legacy systems and lines of business. 
By linking customer records with common data elements, typically name and address, information 
about a firm or individual from across the organization is combined to achieve a unified view 
of the customer’s product and service history. External demographic and lifestyle data can 
be appended to enhance customer knowledge even further by supplying intelligence on a 
customer’s age, finances, household, autos and numerous other profile characteristics. 
Application Specific Data Marts
As a net result of the data integration process, a storehouse of information is formed to 
allow customized communications at every customer touch point. Yet the very size of 
integrated marketing databases, perhaps into the terabyte range, can limit the real-time 
delivery of data so critical in a call center environment. 
To resolve this issue, and to support the multitudinous needs of other touch points using 
customer and marketing data, Application Specific Data Marts are created.
Perhaps best viewed as a specialized extract of information derived from the marketing 
database, Application Specific Data Marts offer the benefit of delivering data that is 
precisely tailored to meet the unique requirements and objectives of that touch point. 
Obviously, data content and detail will vary with the business application. 
For example, a 1-800 order fulfillment center may only need access to customer name and 
address information, whereas a customer service center would require an expanded data set 
on transactions, contact history, credit status, etc. Other touch points, such as help 
desk, sales, e-mail or the Internet, can design a mix of customer data that meets their 
particular needs to ensure speedy response time and lower database overhead. And since 
data marts are interactive and dynamic, information gleaned from new customer contacts 
can be processed to update account records in the marketing database.
Put It To Use
The power and flexibility gained from this approach in leveraging data resources have 
already led to exciting innovations in the call center marketplace. 
Some of these include: 
Profile Routing: incoming callers are identified by Automatic Number Identification (ANI) 
codes, scored by potential value or service history, and automatically routed to an agent 
with the appropriate skills sets and product knowledge. 
Event Driven Offers: multiple customer contacts within a certain time frame trigger 
product offers based on RFM (recency, frequency, monetary value) codes stored in the 
database. 
Value Based Offers: demographic and historical billing records are utilized to classify 
customers for up-sell and cross-sell opportunities. Sales scripts can be customized 
according to a customer’s product set and propensity to buy. 
Automated On-Line Fulfillment: reverse appending of ANI phone codes with customer name and 
address to allow Interactive Voice Response (IVR) verification of delivery points for 
catalogs and promotional responses. 
Fraud Detection: requires callers to verify phone number, address or other data stored in 
customer records to initiate activation of offer or service (e.g., credit card, cellular 
phone) 
Telemarketing Campaigns: point- and-click segmentation analysis of demographically enhanced 
customer databases to produce sophisticated profiles, name counts, contact histories and 
extract files for communication programs. 
Conclusion
The interactive nature of call center operations presents a great challenge and opportunity
for achieving one-to-one customer relationships. Transactional data from sales and service 
touch points across an organization create an abundance of information about the customer. 
However, too often, this data resides in discrete and possibly redundant systems segregated 
by line of business or product area. The data integration process is a means of linking 
data on an enterprise level to forge a single view of the customer. 
Once achieved, a company is positioned to identify, differentiate and respond to customers 
based on an understanding of individual needs and behavior. 
The essence of 1:1 Marketing relies on the use of information technology to create customer
knowledge as a means of building loyalty and lifetime value. 
The proper integration and deployment of database resources, therefore, is a fundamental 
step in achieving this goal