Which Of The Following Is Not One Of The Four Main Applications Of Data Mining For Marketers?
Data Mining
Information mining is a powerful new technology with nifty potential to help companies focus on the most important data in the data they have nerveless about the beliefs of their customers and potential customers. Information technology discovers information inside the data that queries and reports can't effectively reveal. This newspaper explores many aspects of data mining in the following areas:
Data Rich, Information Poor
The corporeality of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-past-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to near 2 one thousand thousand books!) For instance, every day, Wal-Mart uploads 20 million point-of-auction transactions to an A&T massively parallel system with 483 processors running a centralized database. Raw data by itself, however, does not provide much data. In today's fiercely competitive business environs, companies need to rapidly plow these terabytes of raw data into pregnant insights into their customers and markets to guide their marketing, investment, and direction strategies.
Data Warehouses
The drib in price of data storage has given companies willing to make the investment a tremendous resource: Data about their customers and potential customers stored in "Data Warehouses." Information warehouses are becoming part of the applied science. Data warehouses are used to consolidate data located in disparate databases. A data warehouse stores large quantities of data by specific categories so it can exist more easily retrieved, interpreted, and sorted by users. Warehouses enable executives and managers to work with vast stores of transactional or other data to respond faster to markets and make more than informed business decisions. It has been predicted that every business will accept a data warehouse within ten years. Merely merely storing information in a data warehouse does a visitor little good. Companies will want to larn more about that data to ameliorate noesis of customers and markets. The company benefits when meaningful trends and patterns are extracted from the data.
What is Data Mining?
Information mining, or cognition discovery, is the computer-assisted process of digging through and analyzing enormous sets of data then extracting the meaning of the information. Data mining tools predict behaviors and hereafter trends, allowing businesses to brand proactive, cognition-driven decisions. Information mining tools can answer concern questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive data that experts may miss because it lies outside their expectations.
Data mining derives its name from the similarities between searching for valuable data in a large database and mining a mount for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides.
What Can Data Mining Do?
Although data mining is still in its infancy, companies in a broad range of industries - including retail, finance, heath care, manufacturing transportation, and aerospace - are already using data mining tools and techniques to take advantage of historical data. By using pattern recognition technologies and statistical and mathematical techniques to sift through warehoused information, data mining helps analysts recognize pregnant facts, relationships, trends, patterns, exceptions and anomalies that might otherwise become unnoticed.
For businesses, data mining is used to discover patterns and relationships in the data in guild to help make better business organization decisions. Data mining can assist spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Specific uses of information mining include:
- Market segmentation - Identify the mutual characteristics of customers who buy the same products from your company.
- Customer churn - Predict which customers are likely to leave your company and go to a competitor.
- Fraud detection - Identify which transactions are about likely to exist fraudulent.
- Direct marketing - Identify which prospects should be included in a mailing list to obtain the highest response rate.
- Interactive marketing - Predict what each individual accessing a Web site is most likely interested in seeing.
- Market place handbasket analysis - Understand what products or services are commonly purchased together; eastward.g., beer and diapers.
- Tendency analysis - Reveal the difference between a typical client this month and last.
Data mining technology tin generate new concern opportunities by:
Automated prediction of trends and behaviors: Data mining automates the process of finding predictive information in a large database. Questions that traditionally required extensive hands-on analysis can now exist directly answered from the data. A typical case of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets about likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
Automated discovery of previously unknown patterns: Data mining tools sweep through databases and identify previously hidden patterns. An example of blueprint discovery is the analysis of retail sales data to identify seemingly unrelated products that are frequently purchased together. Other pattern discovery issues include detecting fraudulent credit menu transactions and identifying anomalous data that could represent information entry keying errors.
Using massively parallel computers, companies dig through volumes of data to discover patterns virtually their customers and products. For example, grocery chains have plant that when men go to a supermarket to buy diapers, they sometimes walk out with a half dozen-pack of beer equally well. Using that data, information technology's possible to lay out a store so that these items are closer.
AT&T, A.C. Nielson, and American Express are among the growing ranks of companies implementing information mining techniques for sales and marketing. These systems are crunching through terabytes of point-of-sale data to aid analysts in understanding consumer behavior and promotional strategies. Why? To gain a competitive advantage and increase profitability!
Similarly, financial analysts are plowing through vast sets of fiscal records, data feeds, and other information sources in order to make investment decisions. Health-care organizations are examining medical records to understand trends of the past so they can reduce costs in the future.
The Evolution of Data Mining
Information mining is a natural development of the increased utilize of computerized databases to store data and provide answers to business analysts.
| Evolutionary Pace | Business organisation Question | Enabling Technology |
| Information Drove (1960s) | "What was my total revenue in the last five years?" | computers, tapes, disks |
| Data Access (1980s) | "What were unit sales in New England last March?" | faster and cheaper computers with more storage, relational databases |
| Data Warehousing and Decision Support | "What were unit of measurement sales in New England final March? Drill downward to Boston." | faster and cheaper computers with more than storage, On-line belittling processing (OLAP), multidimensional databases, information warehouses |
| Data Mining | "What'southward likely to happen to Boston unit sales next month? Why?" | faster and cheaper computers with more storage, advanced estimator algorithms |
Traditional query and study tools have been used to describe and extract what is in a database. The user forms a hypothesis about a relationship and verifies it or discounts it with a series of queries against the data. For example, an annotator might hypothesize that people with depression income and high debt are bad credit risks and query the database to verify or disprove this assumption. Data mining tin can be used to generate an hypothesis. For instance, an annotator might utilise a neural cyberspace to find a pattern that analysts did not recall to try - for instance, that people over 30 years erstwhile with low incomes and loftier debt but who own their ain homes and take children are adept credit risks.
How Data Mining Works
How is information mining able to tell y'all important things that you lot didn't know or what is going to happen next? That technique that is used to perform these feats is called modeling. Modeling is simply the act of building a model (a set of examples or a mathematical relationship) based on data from situations where the answer is known and then applying the model to other situations where the answers aren't known. Modeling techniques have been effectually for centuries, of form, just it is only recently that data storage and communication capabilities required to collect and store huge amounts of information, and the computational power to automate modeling techniques to work directly on the data, accept been available.
Every bit a unproblematic example of building a model, consider the manager of marketing for a telecommunication company. He would like to focus his marketing and sales efforts on segments of the population most likely to become big users of long distance services. He knows a lot about his customers, but it is impossible to discern the mutual characteristics of his best customers because there are so many variables. From his existing database of customers, which contains data such as age, sex, credit history, income, naught code, occupation, etc., he can use information mining tools, such as neural networks, to identify the characteristics of those customers who make lots of long distance calls. For instance, he might learn that his best customers are unmarried females between the age of 34 and 42 who make in backlog of $60,000 per yr. This, then, is his model for high value customers, and he would budget his marketing efforts to appropriately.
Information Mining Technologies
The analytical techniques used in data mining are often well-known mathematical algorithms and techniques. What is new is the application of those techniques to full general business problems made possible by the increased availability of information and inexpensive storage and processing power. Likewise, the use of graphical interfaces has led to tools becoming available that concern experts can easily utilise.
Some of the tools used for data mining are:
Artificial neural networks - Non-linear predictive models that larn through preparation and resemble biological neural networks in construction.
Decision copse - Tree-shaped structures that correspond sets of decisions. These decisions generate rules for the classification of a dataset.
Rule induction - The extraction of useful if-so rules from data based on statistical significance.
Genetic algorithms - Optimization techniques based on the concepts of genetic combination, mutation, and natural choice.
Nearest neighbor - A classification technique that classifies each record based on the records most similar to information technology in an historical database.
Real-World Examples
Details about who calls whom, how long they are on the telephone, and whether a line is used for fax as well as vox can be invaluable in targeting sales of services and equipment to specific customers. Simply these tidbits are buried in masses of numbers in the database. By delving into its extensive customer-call database to manage its communications network, a regional telephone company identified new types of unmet customer needs. Using its data mining arrangement, it discovered how to pinpoint prospects for additional services by measuring daily household usage for selected periods. For example, households that make many lengthy calls between three p.m. and 6 p.chiliad. are likely to include teenagers who are prime candidates for their own phones and lines. When the visitor used target marketing that emphasized convenience and value for adults - "Is the telephone always tied upwardly?" - hidden demand surfaced. Extensive telephone use betwixt 9 a.m. and 5 p.m. characterized by patterns related to voice, fax, and modem usage suggests a customer has business activity. Target marketing offering those customers "business communications capabilities for small budgets" resulted in sales of additional lines, functions, and equipment.
The power to accurately gauge customer response to changes in business rules is a powerful competitive advantage. A bank searching for new ways to increase revenues from its credit card operations tested a nonintuitive possibility: Would credit card usage and interest earned increment significantly if the depository financial institution halved its minimum required payment? With hundreds of gigabytes of data representing two years of average credit card balances, payment amounts, payment timeliness, credit limit usage, and other fundamental parameters, the banking concern used a powerful information mining arrangement to model the impact of the proposed policy change on specific customer categories, such as customers consistently about or at their credit limits who make timely minimum or small payments. The banking company discovered that cutting minimum payment requirements for small, targeted customer categories could increase boilerplate balances and extend indebtedness periods, generating more than $25 million in boosted interest earned,
Merck-Medco Managed Care is a mail-social club concern which sells drugs to the state'southward largest health care providers: Blue Cross and Bluish Shield country organizations, big HMOs, U.Due south. corporations, land governments, etc. Merck-Medco is mining its 1 terabyte data warehouse to uncover hidden links betwixt illnesses and known drug treatments, and spot trends that help pinpoint which drugs are the well-nigh constructive for what types of patients. The results are more constructive treatments that are likewise less costly. Merck-Medco's data mining project has helped customers salvage an boilerplate of 10-15% on prescription costs.
The Hereafter of Data Mining
In the brusk-term, the results of data mining will be in profitable, if mundane, business concern related areas. Micro-marketing campaigns will explore new niches. Advertising will target potential customers with new precision.
In the medium term, information mining may be as common and piece of cake to use as e-postal service. We may use these tools to find the best airfare to New York, root out a phone number of a long-lost classmate, or notice the best prices on backyard mowers.
The long-term prospects are truly exciting. Imagine intelligent agents turned loose on medical research data or on sub-atomic particle data. Computers may reveal new treatments for diseases or new insights into the nature of the universe. There are potential dangers, though, as discussed below.
Privacy Concerns
What if every phone call y'all make, every credit carte purchase you make, every flight you have, every visit to the doctor you make, every warranty card you send in, every employment awarding you fill up out, every school record you have, your credit record, every spider web page you visit ... was all collected together? A lot would be known about y'all! This is an all-too-real possibility. Much of this kind of information is already stored in a database. Remember that phone interview y'all gave to a marketing company final week? Your replies went into a database. Call up that loan application y'all filled out? In a database. Too much data about as well many people for everyone to make sense of? Not with data mining tools running on massively parallel processing computers! Would you lot experience comfortable nearly someone (or lots of someones) having access to all this information virtually y'all? And remember, all this data does not have to reside in i physical location; as the net grows, data of this type becomes more than available to more than people.
Bank check out:
http://www.kron.com/nc4/contact4/stories/computer_privacy.html
http://www.privacyrights.org
http://www.cfp.org
Explore Farther on the Internet
Introduction to Data Mining
http://www-pcc.qub.ac.united kingdom/tec/courses/datamining/stu_notes/dm_book_1.html
Data about information mining inquiry, applications, and tools:
http://info.gte.com/kdd/
http://www.kdnuggets.com
http://www.ultragem.com/
http://www.cs.bham.ac.uk/~anp/TheDataMine.html
http://www.think.com/html/data_min/data_min.htm
http://direct.boulder.ibm.com/bi/
http://www.software.ibm.com/information/
http://coral.postech.ac.kr/~swkim/software.html
http://www.cs.uah.edu/~infotech/mineproj.html
http://info.gte.com/~kdd/alphabetize.html
http://info.gte.com/~kdd/siftware.html
http://iris.cs.uml.edu:8080/
http://www.datamining.com/datamine/welcome.htm
Data Sets to test data mining algorithms:
http://www.scs.unr.edu/~cbmr/research/information.html
Data mining journal (Read Usama M. Fayyad's editorial.):
http://world wide web.research.microsoft.com/enquiry/datamine/
Interesting awarding of information mining:
http://world wide web.nba.com/allstar97/asgame/beyond.html
Information mining papers:
http://www.satafe.edu/~kurt/index.shtml
http://www.cs.bham.air-conditioning.great britain/~anp/papers.html
http://coral.postech.ac.kr/~swkim/old_papers.html
Data mining conferences:
http://www-aig.jpl.nasa.gov/kdd97
http://www.cs.bahm.air-conditioning.uk/~anp/conferences/html
Conference on very large databases:
http://www.vldb.com/homepage.htm
Sites for datamining vendors and products:
American Heuristics (Profiler)
http://www.heuristics.com
Angoss software (Knowledge Seeker)
http://www.angoss.com
Attar Software (XpertRule Profiler)
http://www.attar.com
Business Objects (BusinessMiner)
http://www.businessobjects.com
DataMind (DataMind Professional)
http://world wide web.datamind.com
HNC Software (DataMarksman, Falcon)
http://world wide web.hncs.com
HyperParallel (Discovery)
http://world wide web.hyperparallel.com
Data Discovery Inc. (Information Discovery Organization)
http://www.datamining.com
Integral Solutions (Clementine)
http://www.isl.co.united kingdom of great britain and northern ireland/alphabetize.html
IBM (Intelligent Information Miner)
http://world wide web.ibm.com/Stories/1997/04/data1.html
Clear-cut Technologies (Interactive Data Visualization)
http://www.lucent.com
NCR (Noesis Discovery Benchmark)
http://www.ncr.com
NeoVista Sloutions (Decision Series)
http://world wide web.neovista.com
Nestor (Prism)
http://www.nestor.com
Airplane pilot Software (Pilot Discovery Server)
http://www.pilotsw.com
Seagate Software Systems (Holos 5.0)
http://www.holossys.com
SPSS (SPSS)
http://world wide web.spss.com
Thinking Machines (Darwin)
http://www.think.com
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Which Of The Following Is Not One Of The Four Main Applications Of Data Mining For Marketers?,
Source: https://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/
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