Applications of Data Mining in the areas of Marketing Communications Essay

mental hospitalIn the randomness age, technological advancements have facilitated the collection of large amounts of information on various palm to include military intelligence, scientific and business data amongst several others. Computers are able to sort taboo this data with the aid of database management systems. info shag be classified according to predefined criteria.Data mining involves the extraction of implicit and useful information from databases. Use of relational databases is more helpful in the aesthesis that it allows linkage with the structured query language (SQL) that allows for predicting, comparison and the determination of variations (Che, Han & Yu, 1996). In websites, this technology is used by businesses in crawling through web pages and collect information that enables the organization to enhance business, analyze the foodstuff trends and utilize the information obtained to their best rice beer (Web Data Mining, 2013).Applications of Data Mining In the Areas of Marketing Communications, creation Relations and Corporate CommunicationsWith the development of business intelligence, corporate management through the use of data marts and reporting software can obtain data from any region or field of interest in computer readable form in a relatively short time. It uses this data to gauge on future market expectation and consumer trends. Through its models and tools, managers are able to predict future events (Web Data Mining, 2013). It allows the analysis of past records and market to tailor and narrow target audience. It also helps in the determination of marketing methods in the end, it increases revenue on sales with fewer campaigns.Data mining can predict consumer bearing, the psychological science of the consumer, behavior while shopping, influence of business environment on consumers and consumer motivation depending on the importance of the product. The products exchange to consumers provide data on items how they ar e positioned. Data mining analyzes consumption patterns, for instance, during festive seasons to find forbidden which products sell more and the association between one product and a nonher. It is common to associate the purchase of profits with butter (Raorane & Kulkarni, 2011).Association is utilized in making decisions in cross marketing. Through web crawling information on consumer preferences are collected, their purchase records are used in making inventory decisions and analysis of ambidextrous payments (Web Data Mining, 2013).Data mining can be categorized according to the data that is collected, in businesses the appointment of high profit and low risk customers is an important task for business owners, customers can be segment with associated characteristics as loyalty and other traits. This is useful in marketing and customer relationship management (Rajagopal, 2011). trueness is how often models get their predictions right while reliability is a measure of consisten cy of the model. Validation is through to determine how models perform against real data quality and characteristics of a data mining model mustiness be evaluated before deployment. However, data mining models are considered reliable if they generate the same type of predictions and sink the same pattern of findings regardless of the test data.Reliability in data mining is also certified on the skill, knowledge and the ingenuity of the analyst. Meaningful relations between variables can be extracted from databases in complex formats that are unachievable through manual systems. However, reliability is no longer assured in data mining due to its complex heterogeneous and dynamic nature. It is necessary to incorporate preventive measures to safeguard data validity and integrity (Kavulya, Gandhi, & Narasimhan, 2008).ConclusionData mining is an effective tool in fields as medicine, marketing and crime prevention amongst many others. The use of computers has seen this lessen the time required for researches. The tools and models it utilizes are genuinely helpful in business in determining and predicting consumer trends and consumption patterns that were unknown in the past. This seeks to promote revenues with itsy-bitsy campaigns. Additionally, the use of web data mining allows businessmen to monitor consumer patterns, clusters and associations for inventory purposes. However, this technique may not be completely reliable, this depends on the skills of the user and preventive measures installed checking on reliability. Through legislation and technological interventions these issues can be alleviated.ReferencesKavulya, S., Gandhi, R. & Narasimhan, P. (2008). Gumshoe Perspective. IEEE Trans. Knowledge and Data Engineering, 8 (1), pp. 866-883.Rajagopal, S. (2011). Customer data clustering using data mining technique. International journal of Database Management Systems, 3(4), pp. 1-9.Raorane, A & Kulkarni, R.V. (2001). Data mining techniques a source for consum er behavior analysis. Retrieved November 13, 2014 from http//arxiv.org/pdf/1109.1202.pdfWeb data mining. (2013). Predictive analytics and data mining. Retrieved November 13, 2014 from http//www.web-datamining.net/analytics/