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Data-mining tools and techniques will also allow Spikes to predict the future behavior of the consumers and to develop advertising programs and promotions accordingly.
Lucinda has been quite keen to develop customer profiles so that they are able to target the future sales campaigns in a better and cost effective way. Customer profiling is the process used by organizations to describe the characteristics of groups of customers by using relevant information from the available databases (Manifold Data Mining Inc., 2009). The drivers for their purchasing decisions and their discriminators from other customers are identified (Manifold Data Mining Inc., 2009) so that they can be used to market new products more effectively using data-mining. These customer profiles can be used to develop group specific marketing and sales plans. Customer profiles will also help Spikes to identify the most valuable customers so that their needs can be differentiated (Manifold Data Mining Inc., 2009) from the other customers. Customer profiling can also help improve one to one relationships with the customers.
Using data-mining techniques, the customer data, orders associated with that customer and the data about the shoes associated with that order can be used to develop the customer profiles for Spikes. Therefore the profiles should contain the following data; CUSTOMER_NUMBER, FIRST_NAME, LAST_NAME, CITY, AGE, ORDER_QTY, TOTAL_ORDERS, TOTAL_PAYMENTS, TOTAL_SHOES_QTY.
Most of these data fields will be derived from the databases using data-mining techniques and tools. This profiling will help Spikes define a better sales strategy, eliminate products not liked by the customers, introduce new products according to the preferences of the customers and gain higher response rates for promotional campaigns.
Once the customers of Spikes start using the E-commerce website, individual data of each consumer will start building up in the Spikes
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Therefore it is scientific that a true data mining software application or technique must be able to change data presentation criterion and also discover the previously unknown relationships amongst the data types. Data mining tools allow for possible prediction of the future trends and behaviors, hence enabling for formation of proactive, knowledge-driven decisions.
This is realized through predictive analysis data mining, which offers the users, impactful insights throughout the organization (Greene, 2012). Predictive analytics is where statistics and mathematics integrate to business and marketing to establish patterns in data and extrapolating the patterns to future business cases and issues, so as to reduce costs, improve response rates, increase the efficiency of processes and consequently boost revenue levels.
The major organizational element, in this case, is the customers. The predictive scores inform the business about the most probable action by the customer. The production of predictive scores occurs when the subject organization design a predictive model.
The case study presents the management of FMC Green River with several unique challenges. While FMC Aberdeen is a newer plant, FMC Green River is older and unionized. This analysis will make recommendations based on FMC Green River's openness to ability management, the degree of organizational commitment, and the ethical questions that changes would present.
One such area where data plays a key role is auditing. Auditing is a crucial role carried out by all companies as a test of their own procedures and products. To assist auditors, companies deploy massive databases to capture all relevant data from all departments; this can be used by auditors to assess the company's internal control.
The first is Discovery, or the practice of examining data without a pre-determined hypothesis, in order to discover patterns in it. The discovery stage may occur by classification of data on the basis of clusters, association rules among sets of data, sequential
In discovery driven method, the data is scoured to identify patterns and hidden information. Future values of unknown variables are ‘guessed’ using the predictive modelling technique. However, the forensic method focuses on extracting unusual elements
Examples of data mining software are oracle, Microsoft SQL Server 2012 and SAS.
KXEN provide an automated data mining for high productivity model building. It focuses on expanding the use of data mining within analysts, making them more
Recently, it has been revealed that the government has also been using an advance form of data mining where they are able to get the complete information about any individual. This means that government has
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