CHARLES STURT UNIVERSITY
MBA ( E-COMMERCE ) PROGRAM
ITC 551 MANAGING INTERNET MARKETING
Assignment No 1
Title : Possible Uses For Data Mining
Due Date : 23 Aug 2000 (Intake 1, Forte-IRI)
Value : 20 %
Facilitator : Mr Jeffrey Tan
Submitted by : Chan Chee Cheong
" Data mining is the process of discovering meaningful new correlations,
patterns, and trends by sifting through large amounts of data stored in repositories
and by using pattern recognition technologies as well as statistical and
mathematical techniques." [ The Gartner group ]
Data mining is a " knowledge discovery process of extracting previously
unknown, actionable information from very large databases." [ AaronZornes, The
MetaGroup ] As computers are more powerful and user-friendly, data mining tools
are developed to help businesses make better decisions that will affect the
company's bottom line. Data mining can produce reports to help monitor how
business is performing.
The goals may be descriptive or predictive. A descriptive task is one
whose goal is understanding, explanation, or knowledge discovery.
Even when the objective of data mining is predictive, it is important that the
model used is sufficiently descriptive to make it clear why a particular prediction
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Definition of Data Mining
The text by Berry and Linoff defines data mining as "the exploration and
analysis, by automatic or semiautomatic means of large quantities of data in order
to discover meaningful patterns and rules". The goal of data mining is to allow a
corporation to improve its marketing, sales, and customer support operations
through better understanding of its customers.
Two marketing problems that can be solved using data mining are :
product placement in the retail industry and marketing new products in the
telecommunication service business.
Selling as many different products as possible is the vision of the retail
entrepreneur. It is difficult to visualize the needs of individual customer and their
buying pattern. One way to accomplish the goal of maximizing their value to our
business is to understand what products or services customers tend to purchase at
the same time or later on as follow - up purchases. In a retail business where a
sales transaction contains the items being purchased by one customer, data mining
can discover where the presence of specific items in a transaction imply the
presence of other items.
Small retail business rely on their knowledge of the customer to inspire
loyalty. The owner and the staff have to recognize each customer and their
preference in brand names of certain product in their retail shop. They are also
required to know the retail shop's range of products and brands so that they can
provide advice and introduce new products to customers. The knowledge of the
customers and their preferences by sales people make customers come back. This
gives them an advantage over competitors who do not have such relationships. A
small business builds relationships with its customers by noticing their needs,
remembering their preferences and learning from past interactions how to serve
them better in the future. How can large enterprises accomplish something similar
when most customers do not interact personally with company employees. Even if
the large enterprise has a call-center to interact with customers, it could be a
different customer service officer each time the same customer called.
Data mining can be applied in product placement in large retail business
and market new products in the telecommunications service. Data mining can be
used in the typical retail business in analyzing the different products the customer
wants. One of the data mining tools is the use of Market Basket Analysis or
affinity grouping. This is to determine which things go together in a shopping
cart at the supermarket. The retail manager can use a Market Basket Analysis in
How a supermarket displays or place its goods will maximize its revenue
generated. If it can analyze the large amount of information gathered at the point
of sale, i.e. the cash collection counters from the receipt printouts, the
supermarket can determine the patterns of goods purchased. The job of market
basket analysis is to find out which things go together in a shopping cart at the
supermarket. The information will be useful to business managers to plan for :
• arrangement of items on store shelves or in a catalog so that items often
purchased together will be seen together.
• products that have a strong relationship, e.g. a rack of the finest wines
would command more sales when placed next to the chillers holding the
beef and meat stuff than wines standing alone on a rack far away, as the
two products complement each other. This is to take advantage of the
correlation between the products. Alternatively, they can be placed further
apart to increase traffic past other items.
• discounting - for example, buying two numbers of product A could enable
a customer to further purchase of product B at a fifty percent discount.
This would increase sales at no additional promotion expense. It is also
another way of clearing slow moving stocks.
• cross-marketing - for example, assuming that analysis has produced a rule
that people who have purchased beef are three times more likely to
purchase wines, people who purchased pet food are more likely to
purchase pet toys in a month's time after the pet food was purchased. This
will allow for stocking up of various kinds and quantity of toys that the
customers might want to purchase. Stocking up space could be planned and
Figure 1 shows an example of determining product affinities i.e. which
products are more likely to be purchased together. In reality, stores sell specific
items like Magnolia brand fresh milk in a 1- liter packs, Daisy fresh milk in half
- liter pack, etc. This example will generate rules that are more general for a
product hierarchy which groups similar items together : Produce, Deli, Bakery,
Seafood, Frozen, Meat, etc. This hierarchy could be determined through data
In the process of discovering affinities in the basket contents, the following
diagram illustrates the correlation between the products, the same colored words
indicate the closest relation.
Fig 1 Diagram produced in determining product - purchasing relationship
From the display above, three groups of customers stand out.
• Those who buy produce and seafood items.
• Those who buy frozen and miscellaneous items.
• Those who buy Deli, fruits, and dairy items.
This type of analysis can help companies identify groups of products or
services that customers have already demonstrated a tendency to acquire together,
or in subsequent purchases. This analysis fits into retail applications as detecting
and assessing relevant patterns can benefit any that accumulates large volumes of
Telecommunications Service Providers
In Singapore, recent events have revealed that the telecommunications
service provider industry is in a fiercely competitive arena, with each operator
competing on value-added packages. Singapore Telecommunications Ltd ( SingTel )
the former monopoly has met with fierce competition from Mobile One ( M1 ) in
the mobile phone and paging market. Starhub, another licensed service provider of
fixed-line and internet service had come into the market on 1 April 2000 and had
took away certain percentage of the market share in the mobile phone services.
As such, the prices of service plans have gone down drastically, including the
equipment ( hand-phones and pagers ), which saw the prices went down by as
much as more than fifty percent. The competition is largely to grab hold of a
larger market share of subscribers, and to sustain the present loyal customers -
that is where the revenues are dependant, using the service in wireless
SingTel, where I am working could do much to market new services and
at the same time maintain the current large user accounts. It could make use of
data mining to test market new products. The initial problem is to figure out in
advance who was likely to want to buy the new product. This can be done by
the application of data mining. The objective, is to find a low cost method, to
reach the desired number of selected prospects most likely to respond, on a
targeted market campaign rather than a mass market campaign. One solution
would be to build a model based on people who had ever responded to any offer
in the past. Such a model would be good for discriminating between people who
refuse all telemarketing calls and throw out junk mails, and those who
occasionally respond to some offers. This non-response model is important to
mass mailing when we really want our message to reach a large, broad market.
SingTel should understand the right data input. Management officers from different
functional areas including marketing, sales, and customer support could meet with
data mining consultants to brainstorm about the best way to make use of
available data. The sources available are from three databases :
A Call Detail database : The call detail database is the record of each call made
or received by every customer in the target market.
A Marketing database : The marketing database capture customer data on usage,
tenure and product history, price plans, and payment history.
A Demographic database : The demographic database contain the purchased
demographic and lifestyle data about the customers in the market.
From brainstorming sessions and preliminary analysis, more descriptive
fields could be implemented and added to the call detail data to be used as input
to the predictive model. The following categories can be used as input for data
• Minutes of use
• No of in-coming / out-going calls
• Frequency of calls
• Voice mail usage
• Roaming usage
• Scope of influence
The minutes of use is the wireless industry's measure of the quality of the
customers. The more minutes of use the better the customer. The scope of
influence is the number of conversations made at a certain period of time. It
enables one to tell the number of times the caller makes contact with customer
service and their loyalty. The customers from the scope of influence category
usually behaves differently from those of the in that their frequency of calls are
higher. These customers rely heavily on the mobile phone service and have built
up their trust in the service provider. Roamers are people who often make long
distance calls. Roamers have different needs compared with others. Sales
department must keep track of them and provide other new products to the
Data from all three databases are brought together and used to create a
computer model. The model will be used to identify the people who would buy
new products. Test showed that it has a higher percentage of the people from the
target group purchasing the new product than those randomly selected [ Data
Mining Techniques by Berry and Linoff ].
With the help of data mining, the problem of who to introduce the new
product to is solved. Once the results of the campaign to gather data are in, it is
possible to apply data mining techniques to get a better view of the responders.
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Michael J A Berry, Gordon Linoff, Data Mining Techniques, 1997, John Wiley & Sons.
Peter Cabena, Pablo Hadjinian, Rolf Stadler, Jaap Verhees, Alessandro Zanasi,
Discovering Data Mining, IBM Corp, 1998.
Dennis Adcock, Ray Bradfield, Al Halborg & Caroline Ross, Marketing, principles and
practice, Third Edition, 1998, Financial Times Professional Limited.
Stein, L.D., Web Security – a step by step reference guide, 1998, Addison Wesley
Cusumano, M. & Yoffie, D., Competing On Internet Time, 1998, The Free Press.
References : http://www.customersat.com/wp0998.htm