What makes data mining an important business tool?
Data mining is an important business analytical tool because it facilitates the analysis of large volumes of data to find hidden patterns and relationships that cannot be obtained using OLAP (Laudon & Laudon, 2015). These patterns and relationships can then be used to guide future decision making within the business and even determine the effect of these decisions to future values through forecasting. Information gathered from data mining reduces the overall cost of advertising and promotions by providing businesses with trends in consumer behavior that can be used to design more effective marketing strategies that are more likely than not to increase revenue for the business.
What types of information does data mining produce?
Businesses can obtain a considerable amount of information by using data mining including associations, sequences, classifications and classifications (Laudon & Laudon, 2015). Moreover, businesses can also use existing values to determine what future values will be through forecasting. Associations and sequences can be useful in determining occurrences within the data that are linked to a single event and discovering which events in the data are linked over a period of time. For instance, a buy one get one free promotion at a grocery store would not be justified if there was no association between past similar promotions and increased purchase. Classifications and clustering are useful in partitioning large data sets into meaningful groups or discovering which data sets can be grouped together (Laudon & Laudon, 2015). Through classification, businesses can examine sets of items that have been classified using a set of rules and determine the group to which an item belongs while through clustering, they can define affinity groups within a data set and partition databases into groups even if there are no predefined set of rules to do so (Laudon & Laudon, 2015).
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In what type of circumstance would you advise a company to use data mining?
I would advise businesses to utilize data mining algorithms in situations where more detailed analysis than that provided by OLAP is needed (Laudon & Laudon, 2015). Analysis of consumer behavior is possible through associations and sequences as is the analysis of consumer response to one-to-one marketing campaigns (Laudon & Laudon, 2015). Businesses that are encountering challenges in finding marketing strategies that work can use data mining to test the effect of new marketing strategies and prevent losses resulting from trial and error. For instance, if a restaurant desires to launch a “specials” promotion, data mining can give insight into what customers order most frequently and when they do so. This kind of information will enable the restaurant to tailor their choice of specials to the customers’ preferences and increase the probability that the “specials” promotion will be a big success.
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Data mining would also be a worthwhile tool to consider in situations where customer acquisition or retention has become a challenge for a business entity. For customer acquisition, data mining would facilitate the identification of the most profitable customers, visualization and prediction of their consumer behavior and the generation of effective strategies to attract such customers and encourage them to spend more. For customer retention, data mining will provide information on the most probable reasons why customers leave and which customers are likely to leave which should assist in guiding the development of marketing strategies to retain such customers (Laudon & Laudon, 2015).
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