Download An assignment submitted in partial fulfillment of the

INFORMATION AND COMMUNICATIONS UNIVERSITY
SCHOOL OF ENGINEERING
MANAGEMENT INFORMATION SYSTEMS 2
An assignment submitted in partial fulfillment of the requirements for
the BA Degree in ICT
Assignment No.
1
Student Name:
CHITIMUKULU, BWEMBYA
SIN#
1407168294
Lecturer’s Name:
Year:
2 SEMESTER 4
1. Discuss the weakness and strength of customer relationship management (CRM) system
Zambian Revenue Authority ZRA an enterprise information system.
Overview
Customer Relationship Management helps businesses keep track of their customers and come up
with more efficient ways to market to them. Both small and large businesses have found ways to
implement CRM practices in their business operations in an effort to understand their customers
better, serve them better and ultimately increase sales and build loyalty. ZRA has devised
software’s to enable its clientele relate well with the institution. Clients can now enter their
details for easier feedback. CRM as used by ZRA has advantages as outlined below;
Provide Better Customer Service
By collecting information that identifies customers' buying habits, including preferences and
frequency, CRM systems give businesses a closer look at their customers' wants and needs so
they can provide better customer service solutions. These improvements lead to more sales
because customers are more likely to be repeat buyers if they receive a quality product and
exceptional service. They are also more likely to suggest those products and services to friends
and family. Through a CRM system, customer service representatives have detailed information
on their customers readily available so that they can adapt their approaches as needed.
Simplified Marketing and Targeting
CRM makes a wide range of data available to business owners and their department heads. This
information allows them to target specific consumers with marketing that is based on their
buying behaviors. The ability to target so precisely ensures that customers get the products and
services they want and need in a timely fashion. The data can also help companies determine.
Just like any other system, CRM has weaknesses
CHALLENGES OF CRM
Learning Curve
Like most systems, there's a learning curve when it comes to getting acquainted with a CRM
program. Management might have to bring in training professionals to offer support to their sales
and customer service teams as they learn how to use the CRM system in their day-to-day
interactions with customers and potential customers.
Staff Resistance
Employees might not see the immediate advantages in using a CRM system in their business
interactions. Because of this, managers and business owners might have to deal with moments of
staff resistance as they attempt to get the entire team on board with the process. From offering
interactive training to providing the sales and customer service teams with real, live case studies
that show the benefits of CRM, business owners and managers can demonstrate the features of
the system and adequately outline how it will benefit customers, daily work flow, employees and
the business overall.
Which types of offers customers respond best to. Equipping your sales team with these details
can help them creatively and strategically pitch new product offers to customers, which can
increase sales.
Costs
Direct marketing is typically more expensive per customer than other forms. Because there is a
higher level of personalization, it might be more time-consuming for a small business to
communicate with its customers on an individual basis. It might also be difficult to decide what
type of customer information to capture and store, since only some of it may prove useful. A
small-business owner and his staff might need to receive training on how to interpret customer
data and buying behavior. This may not be a disadvantage of CRM as used by ZRA because it is
a larger institution that caters for all citizens in Zambia
Security
The security issues associated with maintaining sensitive data are a major disadvantage of
customer relationship marketing. Personal customer information is often stored on servers and in
computerized databases, which puts the business at risk for liabilities. Some customers will
refuse to share some of their information, making it more difficult to take full advantage of the
concepts behind customer relationship marketing. Protecting personal data is costly for
businesses because electronic security measures must be executed. In addition, companies need
to tell customers how their data is used, when it might be shared and why.
2. Discuss the features of Transaction Processing Systems, which are important in
creating systems and solutions.
TPS: Transaction Processing Systems
Definition: A Transaction Processing System (TPS) is a type of information system that collects,
stores, modifies and retrieves the data transactions of an enterprise.
A transaction is any event that passes the ACID test in which data is generated or modified
before storage in an information system
Features of Transaction Processing Systems
The success of commercial enterprises depends on the reliable processing of transactions to
ensure that customer orders are met on time, and that partners and suppliers are paid and can
make payment. The field of transaction processing, therefore, has become a vital part of effective
business management.
Transaction processing systems offer enterprises the means to rapidly process transactions to
ensure the smooth flow of data and the progression of processes throughout the enterprise.
Typically, a TPS will exhibit the following characteristics: Rapid Processing
The rapid processing of transactions is vital to the success of any enterprise – now more than
ever, in the face of advancing technology and customer demand for immediate action. TPS
systems are designed to process transactions virtually instantly to ensure that customer data is
available to the processes that require it.
Reliability
Similarly, customers will not tolerate mistakes. TPS systems must be designed to ensure that not
only do transactions never slip past the net, but that the systems themselves remain operational
permanently. TPS systems are therefore designed to incorporate comprehensive safeguards and
disaster recovery systems. These measures keep the failure rate well within tolerance levels.
Standardization
Transactions must be processed in the same way each time to maximize efficiency. To ensure
this, TPS interfaces are designed to acquire identical data for each transaction, regardless of the
customer.
Controlled Access
since TPS systems can be such a powerful business tool, access must be restricted to only those
employees who require their use. Restricted access to the system ensures that employees who
lack the kills and ability to control it cannot influence the transaction process.
Transactions Processing Qualifiers
In order to qualify as a TPS, transactions made by the system must pass the ACID test. The
ACID tests refers to the following four prerequisites:
Atomicity
Atomicity means that a transaction is either completed in full or not at all. For example, if funds
are transferred from one account to another, this only counts as a bone fide transaction if both the
withdrawal and deposit take place. If one account is debited and the other is not credited, it does
not qualify as a transaction. TPS systems ensure that transactions take place in their entirety.
Consistency
TPS systems exist within a set of operating rules (or integrity constraints). If an integrity
constraint states that all transactions in a database must have a positive value, any transaction
with a negative value would be refused.
Isolation
Transactions must appear to take place in isolation. For example, when a fund transfer is made
between two accounts the debiting of one and the crediting of another must appear to take place
simultaneously. The funds cannot be credited to an account before they are debited from another.
Durability
Once transactions are completed they cannot be undone. To ensure that this is the case even if
the TPS suffers failure, a log will be created to document all completed transactions.
These four conditions ensure that TPS systems carry out their transactions in a methodical,
standardized and reliable manner.
Types of Transactions
While the transaction process must be standardized to maximize efficiency, every enterprise
requires a tailored transaction process that aligns with its business strategies and processes. For
this reason, there are two broad types of transaction:
Batch Processing
Batch processing is a resource-saving transaction type that stores data for processing at predefined times. Batch processing is useful for enterprises that need to process large amounts of
data using limited resources.
Examples of batch processing include credit card transactions, for which the transactions are
processed monthly rather than in real time. Credit card transactions need only be processed once
a month in order to produce a statement for the customer, so batch processing saves IT resources
from having to process each transaction individually.
Real Time Processing
In many circumstances the primary factor is speed. For example, when a bank customer
withdraws a sum of money from his or her account it is vital that the transaction be processed
and the account balance updated as soon as possible, allowing both the bank and customer to
keep track of fund
3. Discuss the importance of data mining in telecommunication industries.
Telecommunication companies generate a tremendous amount of data. These data include call
detail data, which describes the calls that traverse the telecommunication networks, network
data, which describes the state of the hardware and software components in the network, and
customer data, which describes the telecommunication customers. This chapter describes how
data mining can be used to uncover useful information buried within these data sets. Several data
mining applications are described and together they demonstrate that data mining can be used to
identify telecommunication fraud, improve marketing effectiveness, and identify network faults.
. The telecommunications industry generates and stores a tremendous amount of data. These data
include call detail data, which describes the calls that traverse the telecommunication networks,
network data, which describes the state of the hardware and software components in the network,
and customer data, which describes the telecommunication customers. The amount of data is so
great that manual analysis of the data is difficult, if not impossible. The need to handle such large
volumes of data led to the development of knowledge-based expert systems. These automated
systems performed important functions such as identifying fraudulent phone calls and identifying
network faults. The problem with this approach is that it is time consuming to obtain the
knowledge from human experts (the “knowledge acquisition bottleneck”) and, in many cases, the
experts do not have the requisite knowledge. The advent of data mining technology promised
solutions to these problems and for this reason the telecommunications industry was an early
adopter of data mining technology.
Telecommunication data pose several interesting issues for data mining.
The first concerns scale, since telecommunication databases may contain billions of records and
are amongst the largest in the world. A second issue is that the raw data is often not suitable for
data mining. For example, both call detail and network data are time-series data that represent
individual events. Before this data can be effectively mined, useful “summary” features must be
identified and then the data must be summarized using these features. Because many data mining
applications in the telecommunications industry involve predicting very rare events, such as the
failure of a network element or an instance of telephone fraud, rarity is another issue that must be
dealt with. The fourth and final data mining issue concerns real-time performance: many data
mining applications, such as fraud detection, require that any learned model/rules be applied in
real-time. Each of these four issues are discussed throughout this chapter, within the context of
real data mining applications.
Three main sources of telecommunication data (call detail, network and customer data) were
described, as were common data mining applications (fraud, marketing and network fault
isolation). This chapter also highlighted several key issues that affect the ability to mine data,
and commented on how they impact the data mining process. One central issue is that
telecommunication data is often not in a form—or at a level—suitable for data mining. Other
data mining issues that were discussed include the large scale of telecommunication data sets,
the need to identify very rare events (e.g., fraud and equipment failures) and the need to operate
in realtime
(e.g., fraud detection).
Data mining applications must always consider privacy issues. This is especially true in the
telecommunications industry, since telecommunication companies maintain highly private
information, such as whom each customer calls. Most telecommunication companies utilize this
information conscientiously and consequently privacy concerns have thus far been minimized. A
more significant issue in the telecommunications industry relates to specific legal restrictions on
how data may be used. In the United
States, the information that a telecommunications company acquires about their subscribers is
referred to as Customer Proprietary Network Information
(CPNI) and there are specific restrictions on how this data may be used. The telecommunications
industry has been one of the earliest adopters of data mining technology, largely because of the
amount and quality of the data that it collects. This has resulted in many successful data mining
applications. Given the fierce competition in the telecommunications industry, one can only
expect the use of data mining to accelerate, as companies strive to operate more efficiently and
gain a competitive advantage.