Data mining is a computer-based classification and summarization of a large set of data into grouped information based on the correlated data. The data mining tools present the information in a manner that is to interpret and predict. Data mining process entails the use of large relational database to identify the correlation that exists in a given data.
Data used in this process is drawn from different sources that are related to the outcome of the target phenomenon (Chattamvelli 18). Experts use data mining software to predict the occurrence of various situations and events (Miner 35). This paper analysis how data mining is used commercially to earn money, optimize performance and improve service delivery.
Business organizations use data mining technology to improve business management. These organizations use customized data mining applications to generate business trends and patterns from their vast relational database. The applications have specialized inbuilt software algorithm that performs correlation detection.
The principal role of the applications is to sift the data to identify correlations. Eventually, the software presents the correlations as statistical trends and patterns. Various business departments use data mining for management as follows (Chattamvelli 27).
Marketing departments use data mining to perform market analysis. Market analysis is the process of monitoring changes in marketing pattern and trends. Therefore, marketers use data mining applications to investigate the possible causes of changes in marketing trends (Miner 53). Research asserts that common changes in market analysisss include customer complaints, sales decline, and loss of customer’s loyalty.
In addition, marketing departments use market analysis to monitor new products launched in the market. Specialized data mining applications display customer buying trends, post purchasing behavior, customer reviews, and demand trends among others. Marketers use the prevailing market trends to identify the best promotional strategy that will outdo the competitors (Rahman 68).
Marketing departments also use data mining technology to facilitate direct and interactive marketing. Direct marketing is the process of developing a comprehensive business mailing list. The mailing list contains the addresses of the all the business stakeholders (Han & Kamber 56).
Business experts assert that direct marketing is one of the most efficient ways of winning customer and supplier’s loyalty. On the other hand, interactive marketing is the process of optimizing the business web accessibility. The process ensures the business website provides the user with satisfactory information. Successful interactive marketing leads to improved customer loyalty and online purchases (Soares & Ghani 58).
Customer care departments use customer data mining applications to improve service delivery on customer relations. The main function of these applications is to automate customer handling process to minimize response time. Some advanced data mining tools have automated inbuilt emailing feature that automatically respond to general customer concerns (Miner 65). Marketing departments also use this feature to promote direct marketing.
Furthermore, the departments use specialized data mining applications that automate answering of the frequently asked questions (FAQ). FAQ tool provides customers with a quick online guide of solving various problems without visiting the customer care. This practice improves service delivery leading to improved customer loyalty (Soares & Ghani 67).
Customer care department also uses customized data mining applications to manage business promotions. Promotional data mining applications automate the process of selecting winners in uplift modeling promotions (Rahman 45). Furthermore, the departments use data clustering applications to automate customer segmentation analysis. Market segments are distinct groups of customers who buy similar goods or services.
Market segmentation process helps the business in identification of the most profitable customers group for promotions and marketing. In addition, customer care departments heavily rely on data mining software for catalogue marketing. The departments manage huge database that host profiles of all other departments in the organization. Therefore, the department uses product profiles at their disposal to produce product catalogues for sales promotion (Han & Kamber 77).
Commercial companies widely used data mining analysis in the human resource (HR) department. HR departments use customized applications to monitoring and optimize employee’s performance. The application provides information on employees working history.
This information is essential in planning for recruitment, promotion, training, and rewarding (Miner 84). Nevertheless, HR department use Strategic Management application to generated key performance indicators (KPI). KPIs analysis provides business management with a performance progress report. The report provides a summary of the possibility of achieving the organization’s goals. Therefore, production and sales managers use KPIs to correct performance decline hence improving the profit margin (Rahman 73).
Entrepreneurs use data mining technology to make money. For instance, software development experts design and sell data mining software to companies, businesses, and institutions. Research reveals that hundreds of entrepreneurs have become millionaires from the sale of data mining software. In addition, the entrepreneurs does not require established companies to sale their software.
Other entrepreneurs make money from data mining by operating data mining agency. These professionals use the available data mining software to open a data mining consulting agency. Later they approach institutions, businesses and companies to help them run their businesses. The agency charges consultation fee at a favorable rate depending on the amount of work and urgency (Rahman 80).
Business people can also use data mining technology to save or make money. If the business owners are experienced in data mining, they can use this knowledge to analyze market trends. From the analysis, they can predict market threats like decline of price or loss of market for their goods and services.
The business can use this opportunity to reduce production or supply of services hence avoiding losses. On the other hand, the business people can predict a possible rise of demand of their goods or services. In that case, the business increases supply of its goods and services to meet the expected demand. Research asserts that businesses using this approach are highly profitable since they always avoid losses and maximize profits (Soares & Ghani 126).
Web and Search Engine Optimization (SEO) designers use data mining to make money online. Using reliable data mining software, the designers identify the most researched keyword for their website in different locations. The designers use this keyword to optimize their website content.
Such websites receive many organic visitors, which is profitable for the businesses. Research shows that most successful online selling websites use this approach to generate traffic. The method is also very profitable for SEO experts who are paid on a commission basis. When their website ranks high in the search engines, they are likely to get more clicks or signups hence earning more commission (Rahman 142).
In conclusion, commercial organizations widely use data mining as follows. Sales department of commercial companies use data mining to carry out customer churning. Customer churning involves using the buying behavior to predict whether the company is losing its customers to its rivals. Management uses the customer churning results to regulate production hence averting losses. Marketing department uses data mining technique to facilitate market analysis, segmentation, maximizing profits and minimizing expenditure.
Human resource department uses data mining technology to oversee staff monitoring, development and termination. Finally, entrepreneurs have made a lot of money using data mining software. In summary, use of data mining is very instrumental for business organization management and investment (Soares & Ghani 158).
Works Cited
Chattamvelli, Rajan. Data mining algorithms. Oxford: Alpha Science International, 2011. Print.
Han, Jiawei, and Micheline Kamber. Data mining: concepts and techniques. 3rd ed. Amsterdam: Morgan Kaufmann, 2012. Print.
Miner, Gary. Practical text mining and statistical analysis for non-structured text data applications. Waltham, MA: Academic Press, 2012. Print.
Rahman, Hakikur. Ethical data mining applications for socio-economic development. Hershey: Information Science Reference, 2013. Print.
Soares, Carlos, and Rayid Ghani. Data mining for business applications. Amsterdam: IOS Press, 2010. Print.