Example research paper on Data Mining:
Part One Data Mining
The rapid progress of computers and databases has enable companies to store data about customers and transactions for future use. The sheer amounts of data to be analyzed in order to make better decisions require dramatically improved new automated data modeling technologies. A concept of Data Mining is developed. There are two foundation of using data mining techniques: the availability of large amount of data and the data mining modeling techniques. The latter will be discussed in the second part.
1.1 The concept of data warehousing
An organization – Data Warehouse is its centralized store of detailed information about each of their customers, their behaviors, and their preference. (D.Bird, p45) The data warehouse is typically a combination of detailed demographic data on a customer, combined with a historical transactional history, which may include not only the purchases that were made by the customer, but also include contact or interaction data such as what type of promotions were made to each customer, which ones did they respond to, have they called on their own with support related questions, or inquire about a certain product.
A Data Warehouse framework will be presented as follow:
From the diagram*, a few points can be raised:
- External data and customer data will be stored and maintained in data warehouse.
- Marketing department will retrieve customer data from data warehouse.
- Using data mining techniques, companies can develop the marketing strategies and customer campaign.
- Data warehouse can be used by different departments within the same organization.
The reason of building up customer database can be explained by applying Pareto’s Principle to business, which is known as the 80/20 rule:
Twenty per cent of your customers will provide you with 80 per cent of your profits. (adapted from A.Tapp, p58)
This means a small number of the customers provide a disproportionate amount of the profits. Therefore, if companies can identify the most important customers from their database, and provide the tailored service to these customers based on their behaviour pattern that has been analyzed from the database.
As a result, data mining is vital for the modern marketing practice, especially in direct marketing.
1.2 The definition of data mining
Turban defined Data Mining as follow:
Data Mining is a process of looking for unknown relationships and patterns and extracting useful information volumes of data in data warehouse. (Turban, Rainer & Potter, p162)
Data Mining, by its simplest definition, automates the detection of relevant patterns in a database. For example, a pattern might indicate that married males with children are twice as likely to drive a particular type of sport cars than married males with no children. As an auto manufacture marketing manager, this surprising pattern might be quite valuable.
Turban has also identified five main functions of data mining, which will show in the following table. (Turban, p163-165)
Function How they operate
Classification Infers the defining characteristics of a certain group (such as customers who have been lost to competitors)
Clustering Identifies groups of items that share a particular characteristics (Clustering differs from classification in that no pre-defining characteristic is given in classification.)
Association Identifies relationship between events that occur at one time (such as the contents of a shopping basket)
Sequencing Similar to association, except that the relationship exists over a period of time (such as repeat visits to a supermarket or use of a financial planning product)
Forecasting Estimates future values based on patterns within large sets of data (such as demand forecasting)
1.3 The benefit of using data mining
Data Mining helps marketing professionals improve their understanding of customer behavior. In turn, this better understanding allows them to target marketing campaigns more accurately and to align campaigns more closely with the needs, wants and attitudes of customers and prospects.
There are several benefits of using data mining. Customized targeting at the right time
Data Mining enables companies to reach consumers with the right product and the right offer at the right time.
Book and record clubs illustrate this point well. Some clubs no longer send the same set of options to all members. For example, Doubleday book club customizes offers based on a member previous selections and purchases as well as demographic and lifestyle information captured through previous communications.
Thus, customizing by treating different types of members differently not only helps minimize the expense of sending offers that are not appropriate for certain customers or prospects, but also helps enhance the company’s relationship because it encourages the customer to fell that this company understands me and knows what I like, what I am interested in.
- Assign customers and prospects to segments.
The assumption underlying all segmentation analyses is that a single customer or prospectus file consists of a small number of relatively similar market segments and that each market segment consists of individuals whose attitudes toward a company’s products or services are similar to others within the same segment but different from those in the other segments. Data mining can achieve this by applying clustering techniques.
For example, some telesales companies segment their customer based on the frequency of purchase and the amount of purchase. Aware of the 80/20 rule, they then provide the customized service to the highest ranking customers, which have spent the most money and most frequently buying in their companies.
- Drive new programs and fuel new revenue sources
American Express, for example, created a program that used a bill insert promotion to let card members know that buying a new car has never been easier because they could use their American Express cards to charge their down payment. The members were asked to indicate which vehicles they would like to know more about so American Express could arrange for information and literature to be sent from the manufacturer. (Source from www.amex.com)
Aside from demonstrating how the card can produce qualifies leads for automotive manufacturers, this effort also enabled American Express to use the information to identify the characteristics of card members who were interested in certain types of cars. Using data mining, they were able to create profiles of who responded for each type of car and then segment their entire file accordingly.
As a result, American Express can now develop cooperative marketing programs with key manufacturers to help them target promotions to the card members who will most likely respond and to provide special incentives to charge the down payment for their new purchase on the American Express card.
- Foster new services and generate repeat orders
Some catalog companies now assign customers a unique customer ID number to record each transaction. Not only can they use their promotion history and information about products purchased to customize cross-selling opportunities, but also they can use previous purchases as basis for offering customers a new service.
The following table will show some application of data mining.
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