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Data mining, a process that involves the extraction of predictive information which is hidden from very large databases (Vijayarani & Nithya,2011;Nirkhi,2010) is a very powerful and yet new technology having a great potential in helping companies to focus on the most important data in their data warehouses. The use of data mining techniques allows for the prediction of trends as well as behaviors thereby allowing various businesses to make proactive and yet highly informed knowledge-driven decisions. Data mining can therefore help businesses in answering various business questions that in the past have been considered too time consuming to analyze and solve. Companies can therefore collect as well as refine large quantities of data in order to gain a competitive advantage from the hidden predictive patterns contained within. In this paper we determine benefits of data mining to the businesses when employing:
Predictive analytics to understand the behavior of customers
Associations discovery in products sold to customers
3. Web mining to discover business intelligence from Web customers
4. Clustering to find related customer information
The paper also assesses the reliability of the data mining algorithms and then decides if they can be trusted and then predict the errors they are likely to produce. An analysis of the privacy concerns raised by the collection of personal data for mining purposes is also conducted.
Benefits of data mining to the businesses
The benefits of data mining to businesses are numerous. The high level of competition in the global marketplace has forced companies to seek out various competitive advantages aimed at reducing or eliminating inefficiencies, maximizing relationships with all the relevant stakeholders as well as optimizing internal operations. In order to help in this endeavor, companies are involved in the development as well as deployment of effective data mining technologies aimed at leveraging the data resources in order to enhance their decision making capabilities as noted by Nemati and Barko (2003).data...
Predictive analytics is the use of business intelligence technology to produce a credible predictive score for the customers and other organizational elements. A predictive model is used in assigning these scores. Predictive analytics can be successfully used by marketing companies in building models on the basis of the historical data that they have gathered over time. This data is used in new marketing campaigns like direct mail as well as online marketing campaigns. By use of such predictions, the marketers can formulate an appropriate approach to be used in the profitable selling of products to their targeted market segment.
The concept also has several benefits to the retail companies. A market basket analysis can for instance be used by a store in making the best choice of appropriate production. It can also be used by these retailers in offering discounts to various products.
Data mining can therefore be used by companies in observing their best selling points as well as giving them an opportunity of taking advantage of the acquired intelligence/information. Predictive analytics can also be used to realize higher profitability by helping the firms to pay attention to customer trending issues. This is achieved by means of associations discovery in products sold to customers.
Predictive analytics can also be used in understanding the behavior of customers. It can be use din the analysis of customer data as well as the extraction of useful information to be used in gaining of a competitive advantage. Customer service database is therefore considered as a repository of very invaluable information as well as knowledge. These…
References
Ahmed, S., A. (2004) 'Applications of Data Mining in Retail Business', IEEE Computer society international Conference on Information Technology: Coding and Computing (ITCC'04).
Berry, M.J.A. And Linoff, G. (1997) Data mining techniques for marketing, sales and customer support, USA: John Wiley and Sons.
Chen, S., Y. And Liu, X. (2005) 'Data mining from 1994 to 2004: an application -- oriented review', International journal of business intelligence and data mining, vol.1, No.1, pp.4-21
Hui, S., C. And Jha, G. (2001) 'Application of data mining techniques for improving customer services', International Journal of computer applications in Technology, Vol.14, No.1-3, pp.64-77.
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