Data Mining: Information Systems For Decision Making

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Assignment 4:Data Mining
Information Systems for Decision Making
December 3, 2014

A business likes to create products that consumers like and/or need. In order to do this they need information, data, on all aspects of their consumers from names and contact information to a consumer’s transaction history. Collecting data utilizing sales, surveys, research, online resources and competitions, businesses are able to turn findings into useful information that can lead to new products or consumers. While this practice of collecting data offers huge advantages for businesses, the privacy and searching of data has become a growing and ongoing issue. In today’s competitive market, data mining, examining large databases in order to generate new information, has become one of the top tools used by businesses to gain an edge on competitors.
Data mining can provide an array of benefits to businesses in many different ways. Predictive analytics, for example, is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in the future, but forecasts what might happen in the future with an acceptable level of reliability, including what-if scenarios and risk assessment (Beal, 2014). Proven extremely useful, companies such as Target have even made claims stating they can analyze a consumer’s information to the point where they can detect trends that may indicate a woman becoming pregnant based on the items she purchases.
Target assigns every consumer a Guest ID number, tied to their credit card, name, or email address that becomes a bucket that stores a history of everything they’ve bought and any demographic information Target has collected from them or bought from other sources. Using that, analysts looked at historical buying data for all women who had signed up for Target’s baby registries in the past. Analysts ran test after test, analyzing the data, and before long some useful patterns emerged. For example, lots of people buy lotion, patterns detected that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester (Hill, 2012).
Another detection noted that sometime in the first twenty weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date. Data mining and predictive analytics made it possible for analysts to compile a list of twenty-five products that, when analyzed together, allowed Target to assign each shopper a “pregnancy prediction” score. More important, Target could also estimate a due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy. Using predictive analytics, in March a woman twenty-three years of age buying cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug would be determined to have an eighty-seven percent chance of pregnancy and a delivery date sometime in late August (Hill, 2012).
Paired with information derived from data mining, association discovery has allowed businesses to find rules about items that appear together in an event such as a purchase transaction. For example, in the association rule “If people buy a hammer then they buy nails,” the antecedent is “buy a hammer” and the consequent is “buy nails (2005).” Applied successfully, association discovery helps keep like items in stock to guarantee maximum sales potential in retail stores.
Due to the increasing popularity of e-commerce, web mining has provided a way for business to pull data from consumers’ habits on the
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