Note: Sample below may appear distorted but all corresponding word document files contain proper formattingExcerpt from Research Paper:
Determine the benefits of data mining to the businesses when employing:
Predictive analytics to understand the behaviour of customers
"The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011). There are basically seven steps involved in the predictive analysis, these are: spotting the business problem, exploring various data sources, extracting patterns from data, building a sample model by making use of the data and problem, Clarify data -- finding valuable factors -- generating new variables, constructing a predictive model by making use of sampling and validating and deploying the model.
Decisions can be made very quickly by the business if they make use of this method as, they will have a lot of data to help them in their decision making process. Predictive analysis has three main benefits which are: pursuing new sources of revenues, minimizing risk and identifying fraud. Some of the examples of these benefits are being able to predict the risks associated with the credit and loan organizations as well as being able to make the predictions about coupons and promotional offers. Algorithm of this kind helps the business by testing all sorts of scenarios and situations that it will actually take them years to test in the practical world. It helps in reducing the costs associated with making mistakes. Businesses are also able to achieve competitive edge by being able to study and observe the consumer behaviour.
b. Associations discovery in products sold to customers
Association analysis can be of great use when relationships need to be found out in huge amounts of data. Two things that have to be kept in mind when making use of the association analysis with respect to the market data are:
1. It can prove to be computationally very expensive to discover patterns from the large amounts of transaction data.
2. There is a possibility that most of the discovered patterns can prove to be false as they may have happened only be chance.
A very common example of association discovery is the marker-base analysis. This algorithm is made use of in the recommendation engines. These are the engines that observe the products that the customers have shown interest in or that they have already bought and based on that they recommend other products to the customers.
c. Web mining to discover business intelligence from Web customers
The process of getting structured information from semi-structured or unstructured web data sources is known as web data mining. Web data mining is used by the companies in order to collect data from various websites and assemble it in a way that analysis can be done or websites can be built which will gather and provide information from various other websites. In order to survive in this competitive market it has become very important for the businesses to make use of web mining which would help them in getting a better understanding of the market and competitors by getting information from various websites, which will ultimately help these businesses in catering to the needs of these customers in a better way.
It is very important to make use of the web mining in order to manage the web sites in an effective manner along with creating business and support services, adaptive web sites, analysis of the network traffic flow, personalization etc. Business intelligence helps the businesses in staying up-to-date with the trends of the markets, customer demands as well as giving information about new ways to generate more revenue.
d. Clustering to find related customer information
A market is subdivided into separate subsets of customers with the help of cluttering analysis and from these subsets any one of them can be chosen as a target market which can be reached with the help of specific marketing mix. This analysis basically works in such a manner that it looks for clusters of data that are like one another and then put that similar data into a segment (Oracle.com, 2008).
Today information about what sites the customers go to and in what order do they view the pages is recorded…[continue]
"Data Mining" (2013, August 28) Retrieved December 9, 2016, from http://www.paperdue.com/essay/data-mining-95301
"Data Mining" 28 August 2013. Web.9 December. 2016. <http://www.paperdue.com/essay/data-mining-95301>
"Data Mining", 28 August 2013, Accessed.9 December. 2016, http://www.paperdue.com/essay/data-mining-95301
Data Mining Businesses can receive many benefits from data mining. Which benefits they receive, however, can also depend on the way in which their data mining is undertaken. Predictive analytics are used to understand customer behavior, and businesses use the behavior of the customer in the past to attempt to determine what the customer will do in the future (Cabena, et al., 1997). While it is not an exact science, many
Data Warehousing and Data Mining Executive Overview Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline
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
Data Mining The amount of knowledge available in today's world is massive. The information technology specialist who's responsible to his or her organization for maximizing the capacity for practical usage of this knowledge, it is becoming increasingly difficult to have a total grasp of the problem. The purpose of this essay is to discuss the importance of implementing data warehousing and mining systems inside an organization. In order to do this,
The use of databases as the system of record is a common step across all data mining definitions and is critically important in creating a standardized set of query commands and data models for use. To the extent a system of record in a data mining application is stable and scalable is the extent to which a data mining application will be able to deliver the critical relationship data,
The ability to parse through the many records of transactions, customer contacts, and many other items stored electronically creates the foundation for data mining's definition. Data mining specifically is defined as the process of data selection, exploration and building models using vast data stores to uncover previously unknown patterns, insights, and observations that lead to strategies for effective differentiation and growth. Central to the development of data modeling is the creation of data and prediction models based
Data mining is very important for operational effectiveness but when / how to stop mining data before it becomes more trouble than it's worth? Over the last several years, advancements in technology have meant that an increasing number of companies are using data mining to be able to understand the demographics of their customers. This is when they will look at large amounts of information to figure out specific buying habits