Download Category: DEFINITIONS Header: DATA MINING

Category:
Header:
DEFINITIONS
DATA MINING
“Data Mining is used to search for valuable information from the
mounts of data collected over time, which could be used in decision
making” (Keating 33).
Business applications:
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banking
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fraud detection
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marketing
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distribution/sales
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research
Source: Keating, Barry. “Data Mining: What is it and how is it Used?” The Journal of Business Forecasting, 27.3 (2008): 33-37.
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DEFINITIONS
DATA MART/WAREHOUSE/OLAP
Data Warehouse = repository of historical data (recorded past
activities)
Data Mart = subset of Data Warehouse, holds grouped information
for some end purpose.
OLAP = Online Analytical Processing = first step of analysis: initial
analysis of data (Keating 34).
Source: Keating, Barry. “Data Mining: What is it and how is it Used?” The Journal of Business Forecasting, 27.3 (2008): 33-37.
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OBSERVING
DATA MINING and FORECASTING
Forecasting/statistical modeling, traditionally used before, is limited
by the patterns, expectations, and connections assumed, or
obtained from the theory.
Data mining is free from preliminary assumptions, and can lead to
useful, and sometimes unexpected, results and applications, as it
builds the assumptions itself. (Keating 34-35)
Source: Keating, Barry. “Data Mining: What is it and how is it Used?” The Journal of Business Forecasting, 27.3 (2008): 33-37.
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THEORY
MARKETING MODEL
“Marketing is defined as putting the right product in the right place,
at the right price, at the right time. It is a process of planning and
executing the conception, pricing, promotion, and distribution of
ideas, goods and services to create exchanges that satisfy individual
and organizational objectives” (Khan 223).
McCarty 4 Ps: [very useful definitions for modeling]
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Product
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Price
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Place
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Promotion
Source: Khan, Rafi Ahmad. "Data Mining: Applications in Marketing." The International Journal of Science and Technoledge 2.3 (2014): 223-7. ProQuest. Web. 6
May 2016.
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THEORY
CRISP-DM 1990 Process
Process of Data Mining includes 6 steps:
1. 
Defining Project Objectives focuses on understanding and defining
objectives from business perspective
2. 
Data Exploration focuses on relevant data collection and checks data
credibility.
3. 
Data Preparation constructs final data set in a way useful for model building.
4. 
Model Building involves applying a variety of modeling techniques.
5. 
Assessment of Models evaluates the model before implementation. It has to
check for objectivity and completeness.
6. 
Implementation incorporates the models in day-to-day operations. (Khan
224-225)
Source: Khan, Rafi Ahmad. "Data Mining: Applications in Marketing." The International Journal of Science and Technoledge 2.3 (2014): 223-7. ProQuest. Web. 6
May 2016.
Category:
Header:
THEORY
DATA MINING
Techniques Data Mining consists of a huge variety of complex
algorithms, which essentially perform four functions. They are
classification, sequencing, clustering, and association. There are
some other trends, such as interactive data mining methods,
standardization of data mining language, visual data mining, and
other (Khan 225-226).
Source: Khan, Rafi Ahmad. "Data Mining: Applications in Marketing." The International Journal of Science and Technoledge 2.3 (2014): 223-7. ProQuest. Web. 6
May 2016.
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Conjunction
Data Mining for Techniques
Classification, based on machine learning, classifies data into preexisting data sets. Commonly
used methods include Logistic Regression, K-nearest Neighbors, Decision Trees, Artificial neural
networks, fuzzy sets, and etc.) In Marketing, it is widely used in four dimensions, customer
identification, customer attraction, customer retention, and customer development.
Sequencing, in other words putting the events in chronological order, helps to predict
likelihoods of future purchases, or consumer behaviors dependently on some factors.
Clustering is the process that groups the data in specific classes or categories that naturally
occur. In marketing, it can result in specifying the low risk, high risk customers, or market
segmentation of any level.
Association is the process that builds rules fro data. Direct association, for example, can answer
questions of where and how the discounts should be offered (Khan 225-226).
Source: Khan, Rafi Ahmad. "Data Mining: Applications in Marketing." The International Journal of Science and Technoledge 2.3 (2014): 223-7. ProQuest. Web. 6
May 2016.
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Practical Models
Data Mining as a Marketing Activity
There are 3 techniques of data mining that are used for marketing
purposes. They are regression, neural nets and tree models.
Logistic regression is used, when we need to answer the question
that has only two answers, Yes or No.
Tree models are used, when we build a segmentation models of
customer base.
Neural nets are used when we want to build a multivariable
model, for example including revenues, profits, margins, costs, or/
and consumer preferences. These models are complex and hard
to build (Vanesko 52)
Source: Vanecko, James J., and Andrew W. Russo. "Data Mining and Modeling as a Marketing Activity." Direct Marketing 62.5 (1999): 52-5. ProQuest. Web. 6 May
2016.
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Practical Models
Data Mining as a Marketing Activity
CART is a marketing package that builds a profile of a preferred
marketing model, based on collected data, to model the possible
solutions or outcomes. Running the program is easy. First, you need
to decide on purpose, and then dependently on the purpose use
an appropriate model. The purposes may include acquiring new
customers, cross selling products and services to existing
customers, upselling customers to the next level, and creation of
retention or customer loyalty programs (Vanesco 53).
Source: Vanecko, James J., and Andrew W. Russo. "Data Mining and Modeling as a Marketing Activity." Direct Marketing 62.5 (1999): 52-5. ProQuest. Web. 6 May
2016.
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Practical Models/ Example
Integrated Marketing Process
Check Figure 2 and 3.
Task: A company desires to increase its market share. To do so
it needs to acquire the list of 50,000 names and to coordinate
direct and database marketing to allocate time efficiently.
Solution: The first part of the task is realized with the help of
modeling and mining large data sets. The second task is a
business task process. However, as the trends change with
time, the new data is acquired and incorporated in business
models constantly (Vanesko 53-55).
Source: Vanecko, James J., and Andrew W. Russo. "Data Mining and Modeling as a Marketing Activity." Direct Marketing 62.5 (1999): 52-5. ProQuest. Web. 6 May
2016.
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Practical Models
A subscriber Centric Approach
Mobile APPLICATIONS = gold rush for “mining”.
The data provided by users in application:
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Functional category
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Price
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Average ratings
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Reviews
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Target group
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Execution Platform (Erman 139).
The application developer generally discloses this information easily.
Source: Erman, Bilgehan, Ali Inan, Ramesh Nagarajan, and Huseyin Uzunalioglu. “Mobile Applications Discovery: A Subscriber-Centric Approach”. Bell Labs
Technical Journal 15.4 (2011): 135-48. ProQuest. Web. 25 Apr. 2016.
Category:
Header:
Practical Models
A subscriber Centric Approach
Mobile APPLICATIONS = gold rush for “mining”.
The data provided by users in application:
• 
Functional category
• 
Price
• 
Average ratings
• 
Reviews
• 
Target group
• 
Execution Platform (Erman 139).
The application developer generally discloses this information easily.
Source: Erman, Bilgehan, Ali Inan, Ramesh Nagarajan, and Huseyin Uzunalioglu. “Mobile Applications Discovery: A Subscriber-Centric Approach”. Bell Labs
Technical Journal 15.4 (2011): 135-48. ProQuest. Web. 25 Apr. 2016.
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Header:
Practical Models
A subscriber Centric Approach
Project Agora, as many other programs, can collect other
information, such as location, web usage, installed media and
traffic on them, voice call detail records, and user
demographics through user mobile device, network, media
storage, temporary cash storage, and commercial parties to
use it later in data mining, implementing memory-based and
model based algorithms (Erman 134-146)
See Table 1 and Figure 3 for more details.
Source: Erman, Bilgehan, Ali Inan, Ramesh Nagarajan, and Huseyin Uzunalioglu. “Mobile Applications Discovery: A Subscriber-Centric Approach”. Bell Labs
Technical Journal 15.4 (2011): 135-48. ProQuest. Web. 25 Apr. 2016.
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Header:
Data Mining in Marketing
Popularity
Data mining industry is becoming a mainstream, as it
combines the abundance of digital data with smart software
to analyze it.
“As the technology moves beyond the Internet incubators like
Google and Facebook, it has to be applied company by
company, one industry after another”(Lohr).
“The total market for big data technology, according to the
research firm IDC, will reach $41.5 billion by 2018, more than
tripling in five years. And there are far more optimistic
forecasts”(Lohr).
Source: Lohr, Steve. “In Big Data, Shepherding Comes First.” The New York Times. 14 Dec. 2014. Web. 5 May 2016.