Download The Discussion about the Relationship between Data Mining and Precision Marketing

The Discussion about the Relationship between Data Mining and
Precision Marketing
WANG Hui
School of Economics and Management, Henan Polytechnic University, P.R. China, 454000
[email protected]
Abstract The author describes a simple but comprehensive definition for data mining and precision
marketing, discusses the main difference about the two concepts, analyzes the relationship between data
mining and precision marketing from process and application. Through this paper, we can see that data
mining denotes to a technological tool for decision support while precision marketing represents a
management idea, and the former turns the idea into practice. Additionally, as a tool for implementing
precision marketing, data mining reflects more interpretation to market and customer, and the new
pattern of precision marketing has made marketing emphases from selling product to satisfying special
customer needs and wants.
Keywords Data Mining, Precision Marketing, Relationship, Discussion
1. Introduction
Nowadays, , using databases to deposit and manage data, and using machine learning to analyze data,
vast latent knowledge hidden in large data volumes has been discovery because of the need of practical
work and the development of correlative technology. The integration of such idea comes into being the
research domain which is now paid more attention by people: knowledge discovery in databases (short
for KDD), the process of finding novel and valid patterns in the data. Data mining is part of the KDD
and the process that enumerates the patterns (Fayyad, 1995). The data deposited in databases is regarded
as the headspring of knowledge and is compared to ore visually. From all over the world, based on the
original information system, many companies have begun to process the operation information deeply
through making use of data mining, so as to construct their competitive predominance and enlarge their
turnover. Such data information from various channels is combined and is managed by super computer,
neural networks, model algorithm, and other techniques of dealing with information, thus, the
companies can get hold of the decision-making information of directional marketing to special
consumption colony or unity.
Simultaneously, along with the continuous development and extensive application of global information
technology, it is possible for one-to-one marketing (i.e. precision marketing) because the correlative
information of customer (such as vital statistics, race statistics, former consumptive pattern, or
commodity fancy) is easy to be obtained. This situation brings powerful impact to management idea and
market fashion of companies, it is means that the system of precision marketing is becoming the new
trend of marketing development for modern corporations which taking the network and information
technology as its core. Data mining techniques are the important analysis methods, for instance,
customer profiling, targeted marketing, and market-basket analysis. By collecting, processing and
transacting the mass information related to customer purchase behavior, estimating special consumption
colony or unity’s interest, habit, tendency, and requirement, farther deducing the corresponding
consumption colony or unity’s next purchase behavior, many organizations can implement the
directional marketing to special consumption colony or unity.
Then, what the difference between data mining and precision marketing? How does precision marketing
associate with data mining? Which applications of data mining can be applied to precision marketing?
And which revelations can we gain from the discussion? This paper expounds these problems one by
one.
2. Definition of Data Mining and Precision Marketing
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2.1 Definition of Data Mining
The definition to data mining depends on diverse viewpoint and context. For example, data mining in
knowledge discovery is the search for patterns and /or relationships hidden in large data volumes, and it
is a significant process of confirming an effective, useful, and comprehended pattern among the massive
datasets (Fayyad, 1996); data mining is the exploration and analysis of the data in order to discover
meaningful patterns (Berry and Linoff, 1997); data mining can ease the knowledge acquisition
bottleneck in building prototype expert systems (Hong et al., 2000). A more comprehensive parlance is
that data mining represents a process of seeking to a pattern among the aggregation of fact and observed
data for decision-making support. Its object is not only databases, but also file systems or any other
congregate data. (J. J. Huang, H. P. Pan, and Y. C. Wan, 2003).
Being mainly expatiated on the meanings of data mining, such definitions sometimes are nonobjective,
but the value of data mining is its applications from the word go, especially in businesses. Utilization of
historical data to support evidence-based decision making, is leading many organizations to recognize
that the effective use of data is the key element in the future client-server enterprise information
technology (Mitchell, 1999). Our ability to analyze and understand massive datasets lags far behind our
ability to gather and store the data. The technology for accessing, updating, organizing, and managing
large volume of data has matured over the past near thirty years. So the concept of data mining is
gaining acceptance in business as a means of seeking higher profits and lower costs. It is obvious that
data mining is a profound method of data analysis. Moreover, the data is produced from pure business
operation, analyzing the data is not simply for the need of research, bud mostly to provide veritably
valuable information and obtain more profits.
From summarized viewpoints above, based on business management and administration, this paper
thinks that data mining denotes to reveal the implicit, unknown and valuable information from massive
datasets in databases. It is a kind of decision-making support technology. Through analyzing the
enterprise data high automatically, enterprises can get latent patterns in order to help policymaker to
adjust market strategy, reduce risk, and make correct decision.
2.2 Definition of Precision Marketing
As a new concept, precision marketing is changing in an unprecedented speed. It is difficult to give it a
specialized definition, currently the popular parlance is: the effect of globalization, knowledge economy,
digital and Internet are shaping a new era, marketing has to be customized and accountable. On one
hand, the competition in classified product/market becomes more and more fierce. On the other hand,
the diversification of consumer needs and wants are accelerating in a speed beyond most peoples’
imagination. To confront this trend, marketing practitioners realized the imperative to adopt a customer
orientation (Schultz et al, 1996). In order to satisfy diversified customer needs and wants, products and
services should be diversified as well, which means customization is the sole solution in today’s market
place based on modern information technology, and integrated with Internet and wireless
communication, and many companies are taking a more scientific approach to marketing. This means
applying more precision to capturing, analyzing and manipulating customer data, delivering
narrowly-defined messages designed to resonate with customers’ specific wants and needs (Zabin, 2004).
This process is called precision marketing. Actually, point to point communication is the basis of
precision marketing (Lampel et al, 1996). Compared with traditional mass marketing, precision
marketing can cut costs, grow revenues, increase profits, and create an competitive advantage.
The author agrees with that precision marketing employs advanced database system and analytical tools
to classify and segment customer needs, and market tests to ensure the appropriate positioning.
Furthermore, it can yield enormous business value, Yet Precision marketing is unnecessarily at low costs.
On the contrary, the author thinks that it gives more emphasis to the rate of investment-returns so as to
shoot the arrow at the target. In addition, Precision marketing also reflects a management idea, namely
through identifying and satisfying diversified customer needs and wants, companies focus on providing
special products and services for special clients, this pattern of “point to point” can process again and
again, thereby the long-term relationships with existing and potential customers can be build. This
implies that the emphases of marketing have been transferred from products to customers.
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For simplicity, we consider that precision marketing is a way to achieve well-measured expansion by
pinpoint positioning, direct communication, integrated marketing activities, and customized products or
services. We can see that precision marketing is the opposite of massive advertising and direct
communication is the core of precision marketing.
3. Discussing about the Relation between Data Mining and Precision Marketing
3.1 The Distinction between Data Mining and Precision Marketing
Data mining techniques can use machine-learning algorithms to analyze records in a firm’s internal
databases, and discover patterns, transactional relationships and rules which indicate competitive
opportunities. Data mining generally involves the searching for patterns in one or several particular
forms (clustering, classification, prediction, regression, link analysis, etc) (Age Eide et al, 1997) which
used in data analysis from different point of view. These approaches can be applied to many problems,
such as customer analysis, market tests, risk management, or fraud detecting. In a word, they can help
decision-makers by prompting them to consider important issues, especially those associated with data
mining tools. Whereas, in order to be able to gain a high investment-return, precision marketing is a way
to achieve well-measured expansion and implement accurate market positioning through analyzing
customer behavior by marketing quantitative analysis, marketing test systems, and mainframe databases.
In other words, precision marketing is the new rules for attracting, retaining, and leveraging profitable
customers.
Clearly, the main difference between data mining and precision marketing is that the former denotes to a
sort of technology for decision-making support, while the latter mainly reflects a management idea.
Precision marketing is not only a sort of marketing tool based on networks, but also a firm’s continuous
goal, and its way would run through all the marketing period so as to establish a more purposeful
marketing plan.
3.2 The Connection of Process between Data Mining and Precision Marketing
In despite of different concepts in essence, data mining and precision marketing are tied together in an
inalienability manner, precision marketing is a philosophical expression, and data mining is the
technological tool turning the philosophy into practice. Data mining techniques hold the balance in
precision marketing. Moreover, the steps of precision marketing are similar to the process of data
mining.
Generally, a product marketing period can be divided into the following approaches: target customer
cluster positioning, marketing research, marketing extending, and communication feedback collecting.
Precision marketing also can be divided into such approaches, and even more exactly and measurably.
Firstly the aim must be definite, secondly collecting marketing information to investigate, thirdly
establishing ingenious marketing extending strategy, and then exerting a subtle influence on target
customer cluster.
When an organization embarks on a data mining project, it usually goes through a number of stages: (1)
orientation and problem definition, in which management becomes aware of problems, opportunities,
and issues associated with the project and determines its readiness, it should begin from defining the
issue, identifying the problem that would be settled, this is most conformed to the first approach of
precision marketing; (2) data preparation, in which decisions are made as to sourcing, involving data
selection and pretreatment, this resembles marketing research; (3) execution, in which the organization
identifies the analysis modifications, algorithms and relevant applications, this corresponds to the third
step of precision marketing. Just like what the marketing plan is provided in precision marketing, the
third stage of data mining consists of what the algorithm is adopted, what the model is based on, and
what information and knowledge are need to be mined from massive datasets.
In fact, the marketing plan of precision marketing depends on data mining techniques, for example, the
plan should be implemented from customer mining based on databases firstly, including target data
mining, data optimization, and individualized communication manners with target customer; next,
diffusion, and logistics are all based on the applications of data mining techniques to position target
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customers, design exact extending plan and logistics solution. The project of customer increment is
much more in virtue of data mining to deploy the analyses of customer value and retention.
3.3 The Applications of Data Mining in Precision Marketing
It is the core of creating an overall competitive advantage as well as precision marketing concept for
companies to detect potential market, enhance existing customer value, and detain valuable customers.
Actually, data mining is the significant technological tool turning the concept into practice.
One of the features about precision marketing is to build individual and point to point communication
system by positioning target customers exactly. Handling data mining techniques can be process the
following customer managements:
(1) Customer value analysis about operation, which is a customer-centric framework that enables
companies to better identify and select the best opportunities for growth. Customers are not created
equal, nor do they equally value the benefits they receive. A comprehensive perspective of determining
the value of the relationship includes both customer equity and the customer’s perception of relationship
value. Through analyzing the customers’ contribution to the operation of companies and accounting the
rate of customer value for enterprises, we can use data mining techniques (such as clustering, or
classification), to subdivide customers so as to put the diversified service in practice.
(2) Customer value analysis about product, denoting to analyze customers’ contribution to product sale,
in which the method of data mining is similar with customer value analysis about operation. It not only
makes for the product management to provide different service for customers, but also offers
comparatively exact target customers.
(3) Customer retention, which maintains customer retention and satisfaction. It is a tactically-driven
approach based on customer behavior, and the core activity going on behind the scenes in relationship
marketing, loyalty marketing, database marketing, and permission marketing. The theoretic foundation
of customer retention marketing is that past and current customer behavior is the best predictor of future
customer behavior. Adopting clustering (or classification) and link analysis, we can divide customers
into five kinds: highly valuable and steady customers, highly valuable but unstable customers, little
valuable but steady customers, little valuable and unstable customers, and no-good customers.
Identifying the customers with great valuable and profit potential, decision makers can properly allocate
resources, effectively improve services, and clench the most valuable customers.
(4) Customer satisfaction analysis, which is always our aim to improve the quality of performance and
products. Understanding and reacting to the need of the customer are critical elements of effective
quality management. Many organizations are incorporating customer data as part of individual
performance appraisal and merit pay systems. Through using a data mining technique called conjoint
analysis, the data obtained from the questionnaires are used to build a collective utility function
representing customer preferences. This utility function permits to measure the satisfaction of the
customers and to determine the most critical features relevant for the appreciation of the considered
products or services.
(5) Customer credit analysis, which helps us analyze and rate the credit references your potential
customers. Customer credit analysis is very important to companies because customers with different
credit level would be taken different plan on account sale. Utilization of data mining can lead
organizations to obtain customers’ detailed credit level and detect credit abuse and fraud. Additionally,
utilizing data mining tools in precision marketing can carry out market-based analysis, memory-based
reasoning (i.e. making predictions about unknown instances by using know instances as a model),
perform sales and trend analysis, and so on.
Although data mining is the primary method to carry out precise customer positioning, customer mining,
and customer retention, but we should especially pay attention to the two problems. Above all, definition
to the issue must be quite clear in respect that the issue determines the direction of data mining
approaches. For instance, in the data mining application process of customer loss analysis management,
the issues ought to include the loss customers’ characteristic, the loss probability of existing customers,
and the loss reasons, etc. Next is to select appropriate data mining techniques and tools. Since the
mining technique and tool would have a great effect on the exact customer position, we should choose
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carefully and deliberately.
4. Conclusion
Through this paper, we can get the following revelations:
Firstly, data mining is a decision-making support technology, while precision marketing represents a
marketing idea which is also a sort of management concept. They are entirely different in essence but
are tied together in an inalienability manner. Data mining is a primary approach to carry out precision
marketing.
Secondly, data mining is innovative tool of precision marketing. Along with the fierce competition in
classified market, the diversification of consumer needs and wants are accelerating in a speed beyond
most peoples’ imagination, hence all companies would pay more attention to how to identify dispersive
target customers by using the innovative tool (i.e. data mining). As a technological tool to implement the
precision marketing concept, data mining reflects more interpretation to market and customer.
Thirdly, data mining is also a critical technological tool in customer management involving customer
positioning, customer mining, and individual service. Data mining can help organizations select the right
prospects on whom to focus, offer the right additional products to existing customers, and identify good
customers who may be about to leave. The result is improved revenue because of a greatly improved
ability to respond to each individual contact in the best way.
Finally, the new pattern of precision marketing makes the marketing emphases from selling product to
satisfying special customers’ needs and wants. Accordingly, marketing has to be customized and
accountable, and the essential means to achieve these standards should be widely utilized in the
information era. Companies should take full advantage of information resources in virtue of data mining
techniques to analyze customer characteristic, seek to the operational rules between company and
market, try for higher investment-return rate, and enhance benefits continuously.
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The Author can be contacted from Email: [email protected]
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