The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations. Data mining tools enable entities to establish relationships such as associations, classes, clusters and sequential patterns.
The analysis of data can occur using a variety of techniques such as genetic algorithms, rule induction, neural networks and data visualization. Information obtained through data mining has transformed business operations by aiding companies in decision-making and the prediction of crucial factors such customer’s behavior, which helps companies to gain a competitive advantage.
Benefits of data mining to businesses
The generation of predictive scores for organizational elements is crucial in the analysis of the behavior of customers. Predictive analytics enable companies to optimize marketing strategies by modeling trends in customers’ responses. This information helps businesses to allocate funds for various campaigns based on their potential of succeeding.
In addition, it minimizes wastage of time and money caused by the use of manually analyzed marketing strategies. Research shows that manual analysis of marketing methods is a cumbersome process that is error-prone due to the extensive skills required. Predictive analytics eliminate guesswork in the identification of marketing methods by providing reliable data on customers’ preferences and habits (Pyle, 2003).
Analyzing past and present trends about customers’ habits provides patterns that aid in making decisions on future undertakings of a business. A company that implements measures in response to future patterns of customers’ behavior is likely to gain a competitive advantage.
Data mining enables businesses to establish relationships among items in a transaction. The association technique facilitates identification of products that customers purchase frequently. Using the association rule, businesses can determine the way one product influences the sale of another product.
For example, an association of Bagels and Potato Chips provides insight on products that a fast foods business can sell with Bagels so that the sale of Potato Chips increases.
Market based analysis provides businesses with important information that guides the implementation of marketing campaigns by enabling them to establish hypotheses for customers’ buying patterns. Relevant marketing strategies boost sales and promote higher profits.
Web mining enables business entities to establish patterns of customers’ behavior from the web. Using data mining techniques, companies can identify customers’ interests on the web concerning textual or multimedia data (Soares, 2010).
Web usage mining facilitates the analysis of target demographics. Web content and structured mining enable companies to monitor brands and analyze the content and structure of competitors. Such undertakings create strategic advantages.
Clustering enables companies to identify distinct groups of customers and implement strategies to retain customers that are above a cluster and gain the confidence and loyalty of customers that are below the cluster.
Customer-relationship management uses clusters to segment customers based on particular variables indentified through data mining. Variables such as customer-retention probability help companies in the identification of marketing opportunities.
Reliability of data mining algorithms
The reliability of data mining algorithms depends on the nature of data under analysis. Some datasets contain information that has errors or is invalid. Research shows that algorithms have diverse responses to errors and thus compromise the results of data analysis in different manners. Assumptions such as noise-free data influence the accuracy levels of data mining algorithms.
Another factor that interferes with the accuracy of data mining algorithms is the size of data. The search space varies depending on the dimensions in a domain space.
Research shows that the relationship between the search space and dimensions in a domain space is exponential. This relationship introduces a phenomenon known as curse of dimensionality, which interferes with the reliability of data mining algorithms (Kantardzic, 2003).
Various errors arise due to factors that affect data-mining algorithms. Systematic errors are likely to arise due to assumptions on clean data. Preprocessing of data helps to minimize such errors. Other errors include training and pessimistic errors that arise due to invalid data and assumptions such as noise-free data.
Data mining infringement on privacy
Data for mining purposes raises many privacy concerns. First, data intended for profiling customers and analyzing their behavior contains a lot of personal information. The collection and storage of confidential information about individuals introduces controversies due the possibility of illegal access to the information.
Another issue concerns the dissemination of implicit information about an individual or a group of customers. Thirdly, data mining discovers valuable information that is subject to sale. This creates loopholes for the distribution of confidential information without control.
To address the issue of privacy protection in data mining, concerned bodies have established measures that promote reliable data mining results while meeting privacy requirements. OECD Guidelines on data mining extensively cover the use of personal information obtained through data mining by providing various guidelines (Aggarwal & Yu, 2008).
First, executors of data mining should clearly inform subjects on the process and the intended use of the collected data. This will ensure that people participate under their free will.
Secondly, the Forthcoming Policy regulates the use of data mining results by stipulating the purpose of data, its allowed use, and persons who should access the information. The Disclosure policy enables subjects of data mining to determine purposes for disclosure of knowledge by giving or denying consent on the anticipate use of data.
Businesses that have used predictive analysis to gain a competitive advantage
Flight 540 employed the predictive analytics strategy to boost their customer appeal and gain competitive advantage.
They used data from the internet, customer spending history, comment cards and various surveys to model their flights in such a manner that customers could easily make purchase decisions depending on the flight packages on offer. Instead of planning flights on a standardized basis, the company modified them as per groups of customers with similar preferences.
Netflix became a key player in the video rental business by using predictive results on customer preferences to model a customer-centric business approach. The company introduced products such as video rentals that did not constitute a lateness fee, and the provision of video streaming based on customers’ requests. Apart from expanding its market share, the company succeeded in reducing its promotional expenses.
Tusk supermarket was able to increase its sales considerably by using predictive analytics to establish feasible pricing strategies using data on customer survey feedback. The data facilitated estimation of price sensitivity and the determination of price ranges that would have minimal impacts on sales. This promoted customer retention for the supermarket, increase market share, and stabilized generation of revenue.
Conclusion
Data mining provides companies with a basis for analyzing customers’ behavior and responses of potential customers by enabling them to establish relationships among internal and external factors of business operation. In this regard, companies can effectively determine the role of factors such as price, competition and demographics in influencing customers’ behavior.
References
Aggarwal, C. C., & Yu, P. S. (2008). Privacy-preserving data mining models and algorithms. New York: Springer.
Kantardzic, M. (2003). Data mining: concepts, models, methods, and algorithms. Hoboken, NJ: Wiley-Interscience :.
Pyle, D. (2003). Business modeling and data mining. Amsterdam: Morgan Kaufmann Publishers.
Soares, C. (2010). Data mining for business applications. Amsterdam: IOS Press.