Page 514 - Handbook of Modern Telecommunications

Network Organization and Governance 4-45

detail information. Both of these options pose serious limitations and challenges, resulting in incomplete
information for decision making or costly and time-consuming system development and maintenance.
There is tremendous value latent in call detail information for CRM (Customer Relationship
Management), revenue assurance, fraud detection, and network usage analysis. Performing real-time
analysis of voluminous call detail data with complex queries requires much more performance than leg-
acy general-purpose systems can provide. Effective BI programs open the door for increased profitability,
while eliminating the barriers to accessing and analyzing dynamic, detailed information, and offering car-
riers’ performance, value, and simplicity in a data warehouse system. For the first time, carriers can lever-
age their terabytes of CDR data for real-time, better-informed, and more strategic business decisions.

4.3.7.3 Using Client Data for Intelligence
Before you can establish meaningful relationships, however, companies must be able to answer—with
precision and confidence—one seemingly easy question. Exactly who are my customers? In an increas-
ingly competitive world, using the client database smartly, to gain a better understanding of the orga-
nization’s number one asset, customers, can make or break the success of the organization. BI answers
questions such as the following:
1. Who are my most/least profitable customers and products?
2. To whom should I address my marketing action/campaign?
3. What are the sales performances of this period with respect to my objectives and with respect to
the same period last year?
4. Which are currently my best performing products in Germany?
5. Which customers are about to churn; who are fraudulent?
6. At what price should I sell my service/product in Geneva?
Most enterprises use databases to store information about their current customers, previous customers,
business partners, and potential customers. The challenge lies in finding a way to harness the useful infor-
mation contained within these high-volume databases in order to produce intelligent business solutions.
Analyzing the information that an organization stores in connection with all customer interactions
can reveal a lot of remarkable facts about the buying behavior of customers, what motivates them, and
what might make them stop buying from you. It also provides a scientific method to monitor an orga-
nization’s own business performance.
Detailed analysis of customer data will also provide insight into their needs and wants. The exercise
will analyze and segment customers’ buying patterns and identify potential services that are in demand.
Organizations can use this information to shorten response times to market changes, which then allows
for better alignment of products and services with customers’ needs.
An in-depth understanding of customers, provided through comprehensive data analysis, will also allow
picking and targeting better prospects, achieve a higher response rate from marketing programs, and at the
same time identify reasons for customer attrition and create or alter programs and services accordingly.
An important point to consider is whether the analysis is guided by predefined questions. Predefined
points of analysis are aimed at understanding certain types of behaviors by analyzing relationships
between various predecided influencing factors. For example, a predefined analysis of customer service
sales would illustrate the effect of good and bad customer service on sales, and would answer questions
such as how important customer service is to customers and how much it influences future sales. On the
contrary, the objective of an open-ended analysis is to discover trends that are not anticipated by ordi-
nary immersion in day-to-day business. Performing an open-ended analysis internally is often impaired
by the expectations brought on by individuals working within the organization.
The techniques used to analyze data are complex. In order for an organization to be able to use the
results of the data analysis, it is crucial that the results not be clouded by the complexity of the calcula-
tions but are delivered in a straightforward manner. It is important for an organization to recognize
that a good understanding of its customers is useful only to the extent to which this knowledge can be