Data Mining And Its Application In Marketing And Business
ful format, such as agraph or table, for display and visualization
Methods of Analysis Artificial neural networks: Non-linear predictivemodels that resemble biological neural networks instructure Genetic algorithms: Optimization techniques thatuse genetic combination, mutation, and naturalselection based on the concepts of natural evolution Rule induction: The extraction of useful if-thenrules from data based on statistical significance
Methods of Analysis (Cont.) Decision trees: Tree-shaped structures thatrepresent sets of decisions. These decisionsgenerate rules for the classification of a dataset. Classification and Regression Trees (CART),Chi-Square Automatic Interaction Detection(CHAID) CART segments a dataset by creating 2-waysplits while CHAID segments using chi squaretests to create multi-way splits.
Methods of Analysis (Cont.) Nearest neighbor method: A technique thatclassifies each record in a dataset based on acombination of the classes of the k-record(s) mostsimilar to it in a historical dataset (where k 1) Data visualization: The visual interpretation ofcomplex relationships in multidimensional data. Use graphics tools to illustrate data relationships
How Data Mining Applied Data mining is accomplished through modeling Modeling is the act of building a model thatapplies to one situation and then applying it toanother situation where you don’t have a model Use these new models to predict patterns
Data Mining and Marketing Advances in the data mining field have hadprofound effects on the marketing of companies Companies use this data to tailor their coupons,advertisements and sales to consumers This marketing tactic is more effective, efficientand can save the company money
Target Case Study Target uses data mining to tailor the coupons theysend in hopes to attract consumers at times in theirlives where they are vulnerable to changing their storeloyalties The period where consumers are most vulnerable iswhen parents are expecting a child Research has found that when a couple is expecting,they often break their habits and form new ones This gives stores like Target the opportunity to lureconsumers into their stores and get them hooked forlife
Target (Cont.) Target uses data that it collects while you are in thestore/on their website along with personalinformation that they buy from other companies“For decades, Target has collected vast amounts of dataon every person who regularly walks into one of itsstores. Whenever possible, Target assigns each shoppera unique code — known internally as the Guest IDnumber — that keeps tabs on everything theybuy” (Duhigg) This data is then analyzed to better understandconsumers’ shopping and personal habits
Target (Cont.) Analyst, Andrew Pole, started work on a“pregnancy prediction model” by combingthrough Target’s baby shower registry andtaking note of how shopping habits of pregnantwomen changed throughout their pregnancy Using this info, he created a list of about 25items that signal that a woman is pregnant This model was able to predict not only ifsomeone is pregnant but also estimate due date
Amazon Case Study Amazon is using the data they have collected toimprove the customer-service This includes, name, address & basic personal info as wellas consumer preferences and the specific issue theconsumer is trying to fix Use synchronized data to transfer all the data aboutan individual collected from various departments toprovide the customer service representative withthe information they need to have an effectivehuman conversation
Amazon (Cont.) It makes interactions with consumers more efficient Customer service employees have access to the infoneeded when interacting with customers The employees know enough about you to makeyour interaction seem personal but not too muchthat it seems creepy Good to know name, address and the topic of the callbut don’t need to suggest an item that your data hasshown they may like
Starbucks Case Study Starbucks uses data to determine the bestlocations for their stores Multiple Starbucks locations are able to do sowell in such close proximity due to data miningand modeling Use location-based data, street traffic analysisand demographic information to determinewhere their locations will have the most success
Starbucks (Cont.) Starbucks uses a company called Esri and theirdata platform, ArcGIS online, to monitor sales,demographics and proximity to potentialconsumers’ homes, work and other excursions This company takes Starbucks’ massive amountof data, analyzes it and places it in easy-tounderstand platform for Starbucks employees
The Future of Data Mining Predictive analytics: “one-click data mining”,achieved by a easier and more efficient datamining process Allow advanced analytics to be applied acrosssubjects The most revolutionary will be in medicineResearchers can use predictive analytics to findfactors associated with a disease or predict whatpatient might respond best to an experimentaltreatment.
Future Trends Distributed Data Mining: mining data that islocated in various different locations Uses a combination of localized data analysiswith a global data model Hypertext/Hypermedia Data Mining: miningdata which includes text, hyperlinks, text markups, and other forms of hypermedia info Techniques: classification, clustering, semi-structured learning & social network analysis
Future Trends Multimedia Data Mining: multimedia data(including images, video, audio and animation)need to be represented differently thantraditional data Audio data mining (mining music) Spatial/Geographical Data Mining: analyzinginfo about natural resources, images fromsatellittes, or topographical data Most of data is image oriented, a lot of it is fromdifferent locations
Concerns about Data MiningPrivacy: As data mining becomes more widely used,more info is collected about every individual Useful applications of this knowledge vs.potentially dangerous misuse Possible easy access of data and ill intentions Potential for identify theft and more!
Other Concerns User Interface Issues: do visualization tools make theuncovered knowledge interesting & understandable? Performance Issues: many analysis tools andstatistical methods were designed for smaller sets ofdata. As the data size increases, how do they scale? Trade-off: Do benefits of data collection and datamining outweigh the potential risk?
Distributed Data Mining: mining data that is located in various different locations Uses a combination of localized data analysis with a global data model Hypertext/Hypermedia Data Mining: mining data which includes text, hype
DATA MINING What is data mining? [Fayyad 1996]: "Data mining is the application of specific algorithms for extracting patterns from data". [Han&Kamber 2006]: "data mining refers to extracting or mining knowledge from large amounts of data". [Zaki and Meira 2014]: "Data mining comprises the core algorithms that enable one to gain fundamental in
Preface to the First Edition xv 1 DATA-MINING CONCEPTS 1 1.1 Introduction 1 1.2 Data-Mining Roots 4 1.3 Data-Mining Process 6 1.4 Large Data Sets 9 1.5 Data Warehouses for Data Mining 14 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 1.7 Organization of This Book 21 1.8 Review Questions and Problems 23
Data Mining and its Techniques, Classification of Data Mining Objective of MRD, MRDM approaches, Applications of MRDM Keywords Data Mining, Multi-Relational Data mining, Inductive logic programming, Selection graph, Tuple ID propagation 1. INTRODUCTION The main objective of the data mining techniques is to extract .
October 20, 2009 Data Mining: Concepts and Techniques 7 Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization October 20, 2009 Data Mining: Concepts and Techniques 8 Why Not Traditional Data Analysis? Tremendous amount of data
enable mining to leave behind only clean water, rehabilitated landscapes, and healthy ecosystems. Its objective is to improve the mining sector's environmental performance, promote innovation in mining, and position Canada's mining sector as the global leader in green mining technologies and practices. Source: Green Mining Initiative (2013).
Data Mining CS102 Data Mining Looking for patterns in data Similar to unsupervised machine learning Popularity predates popularity of machine learning "Data mining" often associated with specific data types and patterns We will focus on "market-basket" data Widely applicable (despite the name) And two types of data mining patterns
Data Mining The field of data mining addresses the question of how to best use historical data to discover general regularities and improve future decisions (Mitchell, 1999). Data Mining Data mining is the extraction of implicit, previously unknown, and potentially useful information - structural patterns - from data (Witten et al., 2017).
2. IDC, “Emerging Tech and Modern IT: The Key to Unlocking your Data Capital,” 2018 – Document #US44402518 3. Based on ESG Research Insight Paper commissioned by Dell EMC and Intel, “How Organizations Unlock Their Data Capital with Artificial Intelligence” November 2019. Results based on a survey of 750 global IT decision makers.