Predictive Analytics to Understand the Behavior of Customers (Research Paper Sample)

Data Mining
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Abstract
Generally, data mining can be considered as the technology that is used by most business enterprises to collect all the data that is needed from enough sources that are related to the behavior of their customers together with that of their potential customers (ucla.edu.). This information is collected and analyzed so as to give the decision makers in the enterprise a good guide towards making sound decisions that are related to their customers and potential customers. In an organization, this information then becomes very crucial in the day to day running of the enterprise in matters that concern sale of goods and services.
The benefits of data mining to the businesses
Predictive analytics to understand the behavior of customers
It is through data mining that the organizations are able to come up with charts and tables that are used by the decision makers in the enterprises which help them to make informed decisions that are based on past events. The decision makers are able to collect all the information and data that is related to the behavior of the customers in the past. Using this information, they come up with charts and tables that they will then use to predict future trends in the behaviors of not only their own customers, but of potential customers as well (Hendricks).
They will then use these tables and charts to predict future customer behavior from the previous trends. For example, in some business such as those in the tourism sector, most people have realized tends where business has been good during the month of December and low in the month of February. In fact, several industries have used data mining to come up with projections of customer behaviors in the future.
Associations’ discovery in products sold to customers
Through data mining, the enterprises are able to collect all the data that is related to the behaviors of the customers in different regions, which they then analyze to determine the products that the customers prefer most (Hendricks). In fact, the organizations are able to analyze this data and come up with the preference of customers from different segments that enable them to supply the most preferred products to each segment.
Web mining to discover business intelligence from Web customers
Over the last years, e-commerce has rapidly gained acceptance among a lot of people. Since competition has also become very intense, most customers have gotten a lot of options to choose from. For this reason, most enterprises need to become more intelligent in marketing their products. For this reason, an organization needs to utilize data mining so as to come up with a successful marketing plan. Through data mining, the enterprises will get very important information on the behavior of most customers – how they react to different marketing strategies.
Clustering to find related customer information
Through data mining, the enterprises are able to come up with the segments. They are able to come up with clusters or segments of customers who have related behaviors (ucla.edu.). Using data collected from several sources, the enterprises are able to put together information of different customers which they will use to segment them. Market segmentation is very important in marketing an enterprise’s products since the enterprise will be in a good position to market their products differently using different techniques on people of these different segments.
Reliability of the data mining algorithms
The data mining algorisms enable the people doing data mining to conduct the whole process successfully. Through the algorism, the people conducting this exercise are able to collect all the data and analyze the same effectively and efficiently. Through the several steps that are followed, they will avoid overlooking or under looking some few aspects in the process. These algorithms are set of steps that are usually followed, which means that most people will be able to conduct data mining regardless of their experience in the field.
This means that the algorithms are usually very reliable when it comes to data mining. Through these algorithms, the people conduction data mining are able to follow these steps to come up with data that is not only effective, but also reliable. However, errors are part of the while process. One of the errors that are associated with the algorithms is lack of test for any reasonable results in the case under study. This means that the people who are doing data mining are forced to follow these set procedures to conduct the whole process. Some of the aspects that are supposed to be looked into, as per the algorithm, could not be relevant to the actual data mining that is being conducted. This is because these steps and procedures are set up using problems that are on paper, which means that they could not be relevant practically.
Another error that is very common when conducting data mining is over generalizing most of the data that is collected. When using the algorithms, most of the people find themselves coming up with exaggerated generalized data and information about the behaviors of these people. For example, in an attempt to come up with clusters, most people will look at the smallest hints that would put two people in the same cluster – which could give rise to a situation where the people put in the same cluster could not actually have similar characteristics as earlier thought.
Privacy concerns raised by the collection of personal data for mining purposes
One major privacy issue in data mining is the security of the data and information about people. The people whose information and data have been collected for the purpose of data mining have little or no control on the accessibility of the same. For this reason, this data could be accessed by people who the initial sources could not have liked to access. This is major privacy information since most people would like to keep some of their information and data private. Also, the enterprise that has this information could...