Applications of Data Mining in the areas of Marketing Communications
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In the information age, technological advancements have facilitated the collection of large amounts of information on various fields to include military intelligence, scientific and business data amongst several others. Computers are able to sort out this data with the aid of database management systems. Data can 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 sense 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 market trends and utilize the information obtained to their best interest (Web Data Mining, 2013).
Applications of Data Mining In the Areas of Marketing Communications, Public Relations and Corporate Communications
With 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 forecast 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 marketing 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 behavior, the psychology of the consumer, behavior while shopping, influence of business environment on consumers and consumer motivation depending on the importance of the product. The products sold to consumers provide data on items how they are positioned. Data mining analyzes consumption patterns, for instance, during festive seasons to find out which products sell more and the association between one product and another. It is common to associate the purchase of bread 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 fraudulent payments (Web Data Mining, 2013).
Data mining can be categorized according to the data that is collected, in businesses the identification of high profit and low risk customers is an important task for business owners, customers can be segmented with associated characteristics as loyalty and other traits. This is useful in marketing and customer relationship management (Rajagopal, 2011). Accuracy is how often models get their predictions right while reliability is a measure of consistency of the model. Validation is done to determine how models perform against real data; quality and characteristics of a data mining model must be evaluated before deployment. However, data mining models are considered reliable if they generate the same type of predictions and return the same pattern of findings regardless of the test data.
Reliability in data mining is also dependent 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).
Data 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 very helpful in business in determining and predicting consumer trends and consumption patterns that were unknown in the past. This seeks to promote revenues with little 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.
Kavulya, 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 consumer behavior analysis. Retrieved November 13, 2014 from: http://arxiv.org/pdf/1109.1202.pdf
Web data mining. (2013). Predictive analytics and data mining. Retrieved November 13, 2014 from: http://www.web-datamining.net/analytics/