The Benefits of Data Mining

• Determine the benefits of data mining to the businesses when employing

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Predictive analytics to understand the behavior of customers Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model, which has, in turn been trained over your data, learning from the experience of your organization. Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn.

Each customer's predictive score informs actions to be taken with that customer. Associations discovery in products sold to customers The way in which companies interact with their customers has changed dramatically over the past few years. A customer's continuing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made has been shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate what a customer desires. For an example in the old days, the storekeepers would simply keep track of all of their customers in their heads, and would know what to do when a customer walked into the store. Today’ store associates face a much more complex situation, more customers, more products, more competitors, and less time to react means that understanding your customers is now much harder to do.

A number of forces are working together to increase the complexity of customer relationships, such as compressed marketing cycles, increased marketing costs, and a stream of new product offers. There are many kinds of models, such as linear formulas and business rules. And, for each kind of model, there are all the weights or rules or other mechanics that determine precisely how the predictors are combined. In fact, there are so many choices, it is literally impossible for a person to try them all and find the best one.

Predictive analytics is data mining technology that uses the company’s customer data to automatically build a predictive model specialized for the business. This process learns from the organization's collective experience by leveraging the existing logs of customer purchases, behavior and demographics. The wisdom gained is encoded as the predictive model itself. Predictive modeling software has computer science at its core, undertaking a mixture of number crunching, trial, and error.

• Web mining to discover business intelligence from Web customers

The fast business growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to prefer from a number of alternatives, the business community has realized the essential of intelligent marketing strategies and relationship management. Web servers record and accumulate data about user relations whenever requirements for resources are received. Analyzing the Web access logs can help understand the user behavior and the web structure.

From the business and applications point of view, knowledge obtained from the web usage patterns could be directly applied to efficiently manage activities correlated to e-business, e-services and e-education. Accurate web usage information could help to attract new customers, retain current customers, improve cross marketing/sales, effectiveness of promotional campaigns, tracking leaving customers etc. The usage information can be exploited to improve the performance of Web servers by developing proper perfecting and caching strategies so as to decrease the server response time. User profiles could be built by combining users? navigation paths with other data features, such as page viewing time, hyperlink structure, and page content”, according to Sonal Tiwari.

• Clustering to find related customer information

Clustering is a typical unsupervised learning technique for grouping similar data points. A clustering algorithm assigns a large number of data points to a smaller number of groups such that data points in the same group share the same properties while, in different groups, they are dissimilar. Clustering has many applications, including part family formation for group technology, image segmentation, information retrieval, web pages grouping, market segmentation, and scientific and engineering analysis. Many clustering methods have been proposed and they can be broadly classified into four categories such as partitioning methods, hierarchical methods, density-based methods and grid-based methods. Customer clustering is the most important data mining methodologies used in marketing and customer relationship management (CRM).

Customer clustering would use customer-purchase transaction data to track buying behavior and create strategic business initiatives. Companies want to keep high-profit, high-value, and low-risk customers. This cluster typically represents the 10 to 20 percent of customers who create 50 to 80 percent of a company's profits. A company would not want to lose these customers, and the strategic initiative for the segment is obviously retention. A low-profit, high-value, and low-risk customer segment is also an attractive one, and the obvious goal here would be to increase profitability for this segment.

Cross-selling (selling new products) and up-selling (selling more of what customers currently buy) to this segment are the marketing initiatives of choice. Assess the reliability of the data mining algorithms. Decide if they can be trusted and predict the errors they are likely to produce. Most methods for validating a data-mining model do not answer business questions directly, but provide the metrics that can be used to guide a business or development decision. There is no comprehensive rule that can tell you when a model is good enough, or when you have enough data.

Accuracy is a measure of how well the model correlates an outcome with the attributes in the data that has been provided. There are various measures of accuracy, but all measures of accuracy are dependent on the data that is used. In reality, values might be missing or approximate, or the data might have been changed by multiple processes. Particularly in the phase of exploration and development, you might decide to accept a certain amount of error in the data, especially if the data is fairly uniform in its characteristics.

For example, a model that predicts sales for a particular store based on past sales can be strongly correlated and very accurate, even if that store consistently used the wrong accounting method. Therefore, measurements of accuracy must be balanced by assessments of reliability. Reliability assesses the way that a data-mining model performs on different data sets. A data-mining model is reliable if it generates the same type of predictions or finds the same general kinds of patterns egardless of the test data that is supplied. For example, the model that you would use to generate for the store that used the wrong accounting method would not generalize well to other stores, and therefore would not be reliable. Analyze privacy concerns raised by the collection of personal data for mining purposes.

• Choose and describe three (3) concerns raised by consumers.

Recent surveys on privacy show a great concern about the use of personal data for purposes other than the one for which data has been collected. The handling of misinformation can cause serious and long-term damage, so individuals should be able challenge the correctness of data about themselves, such as personal records. The last concern is granulated access to personal information, such as personal information about someone’s health when applying for a job.

• Decide if each of these concerns is valid and explain your decision for each.

These concerns are valid, the first concerned mentioned caused an extreme case to occurred in 1989, collecting over $16 million USD by selling the driver-license data from 19. million Californian residents, the Department of Motor Vehicles in California revised its data selling policy after Robert Brado used their services to obtain the address of actress Rebecca Schaeffer and later killed her in her apartment. While it is very unlikely that KDDM tools will reveal directly precise confidential data, the exploratory Knowledge Discovery and Data Mining (KDDM), tools may correlate or disclose confidential, sensitive facts about individuals resulting in a significant reduction of possibilities. The second concern is valid due to incident happening in Washington; Cablevision fired an employee James Russell Wiggings, on the basis of information obtained from Equifax, Atlanta, about Wiggings' conviction for cocaine possession; the information was actually about James Ray Wiggings, and the case ended up in court. This illustrates a serious issue in defining property of the data containing personal records. The third issue is For example, employers are obliged to perform a background check when hiring a worker but it is widely accepted that information about diet and exercise habits should not affect hiring decisions.

• Describe how each concern is being allayed.

KDDM revitalizes some issues and possess new threats to privacy. Some of these can be directly attributed to the fact that this powerful technique may enable the correlation of separate data sets in other to significantly reduce the possible values of private information. Other can be more attributed to the interpretation, application and actions taken from the inferences obtain with the tools. While this raises concerns, there is a body of knowledge in the field of statistical databases that could potentially be extended and adapted to develop new techniques to balance the rights to privacy and the needs for knowledge and analysis of large volumes of information. Some of these new privacy protection methods are emerging as the application of KDD tools moves to more controversial datasets.

• Provide at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and evaluate the effectiveness of each business’s strategy.

The first advantage analysis helps when it comes to validity of a product by making a distinction between the positioning of a product and its ability to satisfy customer requirements. Another important attributes include ease of use, innovation, how well the product integrates with other technologies that customers need. The second advantage is the technology provides to customers. Even if a product is well designed, it must be able to help businesses achieve their business goals. Goals range from gaining insight about customers in order to be more competitive, to using the technology to increase revenue. A key attribute that is measured in this dimension is how well the product supports companies in meeting their objectives. The third advantage is the strength of the company’s strategy. It is not enough to simply have a good vision; a company must also have a well-designed road map that can support this vision. Vision attributes also include more tactical aspects of the company’s strategy such as a technology platform that can scale, well-articulated messaging, and positioning. A key component of this dimension is clarity: it must be clear what business problem the company is solving for which customer.

References

1. Alexander, D. (2012). Data Mining. Retrieved from: http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/#8
2. Josh, K. (2012). Analysis of Data Mining Algorithms. Retrieved from: http://www-users.cs.umn.edu/~desikan/research/dataminingoverview.html
3. Exforsys. (2006). Execution for System: Connection between Data Mining and Customer Interaction. Retrieved from: http://www.exforsys.com/tutorials/data-mining/the-connection-between-data-mining-and-customer-interaction.html
4. Frand, J. (1996). Data Mining: What is Data Mining? Retrieved from: http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm
5. Pupo, E. (2010). HIMSS News: Privacy and Security Concerns in Data Mining. Retrieved from: http://www.himss.org/ASP/ContentRedirector.asp?type=HIMSSNewsItem&ContentId=73526
6. Stein, J. (2011). Data Mining: How Companies Now Know Everything About You. Retrieved from: http://www.time.com/time/magazine/article/0,9171,2058205,00.html#ixzz25MwYNhuh

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