This document discusses key concepts in customer relationship management (CRM) including data warehousing, data mining, and privacy. It notes that high quality, accurate customer data is critical for intelligent CRM decisions. Data from multiple sources needs to be integrated and standardized before being stored in data warehouses, which are repositories used for both operational and analytical purposes. Data mining is then used to discover patterns and insights in the large volumes of warehoused data to help determine marketing strategies. However, customers have increasing privacy concerns about how companies collect and use their data.

1. Data Warehousing, Data
Mining
&
Privacy

2. Significance of Data in CRM:
 Customer insights based on customer information enable intelligent CRM
decisions.
 This would be made possible by high quality data on customers. Poor
quality data would lead to poor quality decisions.
 CRM data should possess the following desirable attributes: Sharable,
 Transportable, Accurate, Relevant , Timely and Secure (STARTS).
 Proficiency at acquiring, enhancing, storing, distributing and using
customer data is critical to CRM performance.

3. Structure of a database
A typical relational database created in CRM contains:
1.Files (which are equivalent to tables) which hold information related to one specific aspect
and some indexing/ common information.
2. Records, which are like rows in a table. Each row pertains one customer or transaction.

4. Structure of a database
3. Fields, which are like columns in a table. Each field represents a single data variable. Fields
contain information like name, demographics, contact details, transaction histories,
projections of future transactions, benefits to customers, expectations of customers,
preferences, benchmarks etc. Some of these are easy to collect and capture; others require
creativity in collection and capturing.

5. Data integration
Often several databases exist simultaneously in a company like
(customer data, sales data, collections data, product data, service data, channel data etc.)
These need to be integrated to provide a single, consistent view of the
customer.
This is called as Data integration

6. Data warehousing
 When companies generate huge amounts of data due to geographical spread of markets
and/or production, multiple product categories, multiple marketing channels, etc. the
data needs to be converted into information that can be used for both operational and
analytical purposes.
 This is achieved through data warehousing.
 Data warehouses are repositories of large amounts of operational, historical and
customer data. Since data could come different sources, data needs to be standardized
and cleaned before being warehoused.

7. Data Mining
 It is the search for meaning in within large volumes of data like data
warehousing.
 Data mining is used to discover patterns, find answers to questions that
help in determining marketing & CRM strategies.
 It also helps in classifying data, making predictions, modelling etc.

8. Privacy issues
 Customers are becoming increasingly concerned about the type and
amount of data companies generate about them. The data could be
misused in hands of wrong people. There have been two major responses
to the privacy
 concerns of customers: self-regulation (companies formulating their own
privacy policies) and
 legislation (cyber laws etc.).