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The Importance of Customer Data & its Application
Introduction
In today's competitive environment, companies begin to highly rely on customer data to build up their brand recognition and customer loyalty. Organisations such as banks, insurance companies and telecommunication companies, have many customers and record their buying behaviour in routine transactions. Such organisations may store integrated summarised and documented data about their customers in a data warehouse.
Warren Macfarlane has pointed out that ¡¥in five years time there will be two types of company. Those who use the computer as a marketing tool, and those who face bankruptcy.' (A.Tapp, p7) More and more companies start to realise that ¡§the new direct marketing is based on the premise that not all customers are alike and that by gathering, maintaining, and analysing detailed information about customers and prospects, marketers can identify key market segments and optimise the process of planning, pricing, promoting, and consummating an exchange between sellers and buyers that satisfies both individual and organisational objectives¡¨. (D.Shepard, p5)
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The main purpose of this report is, therefore, to examine the importance of customer data, and how they will be exploited in the customer campaigns. In order to do so, a few companies marketing campaigns will be discussed. In part 1, customer data will be analysed in the concept of data mining. Data mining techniques will be evaluated in part . Then it will be followed by a critical examination of the customer campaign by using customer data in part .
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 analysed in order to make better decisions require dramatically improved new automated data modelling 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 modelling techniques. The latter will be discussed in the second part.
1.1 The concept of data warehousing
An organisation's Data Warehouse is its centralised store of detailed information about each of their customers, their behaviours, 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 this 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 organisation.
The reason of building up customer database can be explained by applying Pareto's Principle to business, which is known as the ¡¥80/0 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 analysed from the database.
As a result, data mining is vital for the modern marketing practice, especially in direct marketing.
1. 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, p16)
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, p16-165)
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)
Table 1
1. The benefit of using data mining
Data Mining helps marketing professionals improve their understanding of customer behaviour. 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.
æ Customised 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 customises offers based on a member's previous selections and purchases as well as demographic and lifestyle information captured through previous communications.
Thus, customising by treating different types of members differently not only helps minimise 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.¡¨
æ 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 80/0 rule, they then provide the customised service to the highest ranking customers, which have spent the most money and most frequently buying in their companies.
æ 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.
æ Foster new services and generate repeat orders
Some catalogue 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 customise 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.
In short, data mining is a powerful new technology with great potential to help companies focus on the most important information ¡V customer data- in their data warehouse. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge- driven decisions.
Part Two Data Mining Modelling Techniques
Modelling techniques are the second foundation of the application data mining. Modelling is as much an art as a science. (A.Tapp, p68) Very often in modelling, variables such as ages, income, and gender will be related to each other. Different companies might have different modelling techniques according to their own marketing objectives and strategies.
.1 The purpose of modelling techniques
Mark S. Bacon has summarised that modelling techniques will be applied based on some of these purposes, which will be shown in the following table. (M.S.Bacon, p16-p14)
Purpose Objectives and implication
Customer Profitability To find character traits that help identify the profitable customers. The process starts with a customer list, which is appended with additional data. This list is then processed through a data mining modelling technique to find hidden patterns that help identify prospects that are similar within the customers. These patterns are then used to score the consumer or business database, resulting in a database with each record showing how closely it resembles the customer.
Customer Retention One of the objectives of this kind of modelling is to determine the character traits that identify highly response customers. Besides, patterns may be detected or modelled that will predict that when a customer exhibits a certain type of behaviour, they may be at high risk of switching to the competitors.
Customer Acquisition It involves finding customers who previously were not aware of the products, or customers who in the past have bought from the company's competitors. Modelling techniques will spot these prospective customers and help the organisation to set up an acquisition marketing campaign.
Customer Segmentation This may the basic requirement for the company. Patterns may be detected which allow organisations to rationally group individual customers into large groups or segments for marketing purpose.
Table purpose of modelling techniques
Thus, companies will choose the appropriate modelling techniques to suit their marketing strategies and purposes. One of modelling techniques will be further discussed in detail.
. CHAID analysis
CHAID, for Chi-square Automatic Interaction Detector, is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. (I.Linton, p87) CHAID modelling selects a set of predictors and their interactions that optimally predict the dependent measure. The developed model is a classification tree that shows how major types formed from the independent (or predictor) variables differentially predict a criterion or dependent variable.
CHAID is a statistical procedure that is often used when market segmentation is desired. CHAID is most useful when the variable responses are categorical in nature and a relationship is sought between the predictor variables and a categorical outcome measure.
In this example, executives of the Knobyte Computer Co. are interested in knowing whether their customers can be segmented for future target mailings. The company selected 10,000 purchasers of their product at random and surveyed them to determine their demographics and opinions on various issues related to computer upgrades.
The diagram in the following page is a representative CHAID output.
This output shows that of the 10,000 purchasers 4,500 had upgraded their computer system. Next it presents that the most statistically significant attribute within these 4,500 is whether they had purchased a PC in the last six months, since the company would expect that people with older PCs would be more inclined to upgrade than a recent purchaser.
Looking at those who had purchased a PC in the last six months the most significant predictor variable within this group is who uses the PC, self or self and others. This is reasonable since a sole user is more inclined to personalize his PC setups by upgrading to fit his or her particular needs.
Figure CHAID output
[Source http//www.marketstrategies.com/it/tools/chaidh.htm]
For those who thought modem speed was the most influential characteristic in their upgrade purchase there was a significant difference between those who were on-line versus those who were not. Again this makes intuitive sense as once people get on-line they might realise the need (requirement) for a faster modem.
The Knobyte Computer Co. can now be more effective with their upgrade related mailings by concentrating on the less recent PC buyers and perhaps target them for a new upgrade package centred on faster modem speeds. This example shows how CHAID analysis can help the company answer market segmentation questions in a quick and straightforward manner.
The CHAID method has certain advantages as a way of looking for patterns in complicated datasets. First, the level of measurement for the dependent variable and predictor variables can be nominal (categorical), ordinal (ordered categories ranked from small to large), or interval (a scale). Second, the level of measurement for the predictor variables can be nominal, ordinal, or interval. Third, not all predictor variables need be measured at the same level (nominal, ordinal, interval). Fourth, missing values in predictor variables can be treated as a floating category so that partial data can be used whenever possible within the tree.
On the other hand, CHAID modelling is essentially a stepwise statistical method and that there is always a potential for too much to be seen in the data even when very conservative statistical criteria are used. In general, CHAID modelling will be very useful in identifying major data trend.
. Other techniques
There are some other modelling techniques, such as clustering analysis, regression analysis available for the companies to choose. Table 4 presents some of the modelling techniques.
Modelling techniques Description
CART Classification and Regression Trees. A decision tree technique used for classification of a dataset. Provides a set of rules that users can apply to a new dataset to predict which records will have a given outcomes. Segments a database by creating -way splits. Requires less data preparation than CHAID
Clustering The process of dividing a dataset into mutually exclusive groups such that the members of each group are as ¡§close¡¨ as possible to one another, and different groups are as ¡§far¡¨ as possible from one another, where distance is measured with respect to all available variables.
Linear regression A statistical technique used to find the best-fitting linear relationship between a dependent variable and its independent variables.
Logistic regression A linear regression that predicts the proportions of a categorical target variable, such as type of customer, in a population.
Table 4
To sum up, companies will choose the modelling based on their own marketing strategies, and their purpose of building up the database. Customer data play the major role in the modelling, it is the major source of the analysed data.
Part Three Customer Campaign
After companies have done the data mining, they may know which specific group of consumer they need to target. Then according to the special characteristic of this group of people, companies will start to develop the marketing campaign. However, companies may also develop the campaign based on their strategy ¡V
customer retention and customer acquisition.
Edward Nash pointed out that there are five basic elements within the customer campaign, shown as follow. (E.Nash, p74) Each element will interact with each other. There may be more than one element appearing in the customer campaign.
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