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Customer Relationship Management

Notes Neural networks are also intuitively appealing, based as they are on a crude low-level model of
biological neural systems. In the future, the development of this neurobiological modelling
may lead to genuinely intelligent computers.

Applications for Neural Networks

Neural networks are applicable in virtually every situation in which a relationship between the
predictor variables (independents, inputs) and predicted variables (dependents, outputs) exists,
even when that relationship is very complex and not easy to articulate in the usual terms of
“correlations” or “differences between groups.” A few representative examples of problems to
which neural network analysis has been applied successfully are:
1. Detection of medical phenomena: A variety of health-related indices (e.g., a combination
of heart rate, levels of various substances in the blood, respiration rate) can be monitored.
The onset of a particular medical condition could be associated with a very complex (e.g.,
nonlinear and interactive) combination of changes on a subset of the variables being
monitored. Neural networks have been used to recognize this predictive pattern so that
the appropriate treatment can be prescribed.

2. Stock market prediction: Fluctuations of stock prices and stock indices are another example
of a complex, multidimensional, but in some circumstances at least partially-deterministic
phenomenon. Neural networks are being used by many technical analysts to make
predictions about stock prices based upon a large number of factors such as past
performance of other stocks and various economic indicators.
3. Credit assignment: A variety of pieces of information are usually known about an applicant
for a loan. For instance, the applicant’s age, education, occupation, and many other facts
may be available. After training a neural network on historical data, neural network
analysis can identify the most relevant characteristics and use those to classify applicants
as good or bad credit risks.
4. Monitoring the condition of machinery: Neural networks can be instrumental in cutting
costs by bringing additional expertise to scheduling the preventive maintenance of
machines. A neural network can be trained to distinguish between the sounds a machine
makes when it is running normally (“false alarms”) versus when it is on the verge of a
problem. After this training period, the expertise of the network can be used to warn a
technician of an upcoming breakdown, before it occurs and causes costly unforeseen
“downtime.”
5. Engine management: Neural networks have been used to analyze the input of sensors
from an engine. The neural network controls the various parameters within which the
engine functions, in order to achieve a particular goal, such as minimizing fuel consumption.

5.2.4 Data Mining Application in Customer Segmentation

Businesses have become increasingly sophisticated in their efforts to capture consumer
information, but the process of exploiting consumer information remains relatively immature.
Data mining is a process that applies the techniques of artificial intelligence to the task of
discovering useful patterns in data, and is proving particularly powerful in the identification of
customers sharing the same characteristics. This segmentation of customers into affinity clusters
presents new possibilities for customer segmentation.
Many segmentation approaches have been devised and each of these has merit. Experience
suggests that most enterprises use a combination of approaches to deliver maximum benefit.
The ability to deliver sophisticated segmentation techniques to the business enhances the ability

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