...the benefits for in regards to data mining: predictive analytics to understand the behavior of customers, associations discovery in products sold to customers, web mining to discover business intelligence from web customers, and clustering to find related customer information. To understand the behavior of customers by the use predictive analytics we must first understand what predictive analytics is. “Predictive analytics is the process of dealing with a variety of data and applying various mathematical formulas to discover the best decision for a given situation” (ArticleSnatch, 2011). This gives any business a competitive edge and helps to remove the guess work out of the decision making process therefore helping to find the right solution in a shorter amount of time. In order to find the solution faster there are a seven simple steps that must be worked thru first: what is the problem for the company, searching for multiple data resources, take the patterns that are observed from that data, creating a model that contains the problem and the data, categorize the data and find important factors and in turn creating new variables, build a predictive model by using examples, and authorize this model and put it into action. When these steps are followed through with then it makes it easy for businesses to make speedy decisions using the immense amounts of data that they now have. There are multiple benefits of predictive analytics such as: minimizing risk, a decline in...
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...There are several ways that Data-Mining can be used. A useful way is through customer attrition, which includes companies. Companies should have a complete retention strategy, due to gathering new customers is costly. Being able to retain customers during a downfall, when that is happening people are looking for a lower cost alternative. Knowing why a customer is leaving is important. There are customer churn analysis and apprehending methods, and trend analysis. Along with the customer churn profiling. Developing Customer Retention Strategies. Customer Retention is important because getting a new customer is expensive rather than keeping the existing one. Retention is meaningful to most companies because the cost of having new customers is much more than the cost of keeping good relationships with their current customers. How to contain customers during a downfall? During a downfall is the time when people look for a lower cost alternative. In these circumstances, down-selling can be key to retaining customers. When economy overcomes, the customers are likely to move up to the original services they had subscribed too. Down falling is better than losing customers. However that can have negative effects on sales, this should be carried out carefully on customers who are at risk of churning to lower cost alternatives, methods can be used to identify customers at risk of churning. Knowing why a customer leaves. The most important thing in customer retention is to know the customer...
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...Assignment 1: DDS, BI, Business Analytics, and Predictive Analytics LaShonda Spell Prof. S. Mirajkar CIS 356 Operating a successful business today involves utilizing the correct tools to make the best decisions for that business. The main tools that are used for making critical business decisions are DSS, DDS, BI, Business Analytics and Predictive Analytics systems. The concepts/ systems mentioned assist management in the major decision-making processes by providing crucial operational data in comprehensible formats for monitoring/ reviewing and analyzing. Making the best decisions regarding business operations determine the success or failure of the company and ensures that all business strategies are implemented and effective. In this essay, there is a brief overview of the similarities/ differences, methodologies/ technologies and evaluation of the capabilities of DDS, BI, Business Analytics and Predictive Analytics systems. Similarities and Differences among DDS, BI, Business Analytics, and Predictive Analytics regarding business scope/origins /histories/ methodologies/ technologies DDS (Data Distribution Service) are data communications based on the standards managed by the OMG (Object Management Group). The standards set by the OMG of DDS describe different latency levels of data communications for distributed applications (Twin Oaks Computing, Inc., 2011). DDS standard support data defining applications, dynamic publishing/ subscribing discovery and QoS policy...
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...The Five Types Of Analytics Michael Corcoran Sr Vice President &CMO 1 Session Agenda Why business analytics? Review the different types of analytics & common misconceptions Review the delivery methods for the operational users Propose holistic approach to expand enterprise analytics Value of integration and data quality to analytics Discussion 2 Analytic Quiz What do beer and business analytics have in common? In 1900 W.S. Gossett, an analyst at Guinness invented a distribution to analyze production processes and employment problems. Guinness decided to keep it a trade secret. He published it under the pseudonym Student. Hence the name of this popular analytic method became Student’s t-test. Analytic Quiz Who made a killing by applying it on a massive scale? 4 Four Types of Analytics Information, Analysis And Decisions: The Basics Diagnostic Analytics Why Did it Happen? Descriptive Analytics What is Happening? Analysis Predictive Analytics What is Likely To Happen? Prescriptive Analytics What Should I Do About It? Information Analytic Excellence Leads to Better Decisions 5 Information Builders’ View The Five Types of Analytics Prescriptive Predictive Skills Levels Discovery Diagnostic Descriptive Value 6 The Five Types of Analytics It is not about more data! It is about deeper look! 100% 13% Core Analytics Advanced Analytics The Five Types of Analytics Descriptive: What Happened? 8 The Five Types of Analytics Descriptive: What Happened? Too...
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...across the world. Welcome to the era of objective thinking powered by technology that has given a new dimension to business and management. With the passage of time more and more companies are coming forward to adopt, improvise and leverage on technology and Business Intelligence has proved to be the flag bearer in this upcoming trend. Business Intelligence, in layman terms, is data converted to information and available in ready to use format that can be further analyzed, modified and transformed as per the changing demand. The industries today are mostly into some or other form of nascent technology that speaks of raw form of information. Basically, these systems are into huge data repository that provides real time information or basics analytic tools that can provide historical analysis. But the future has a lot more to offer. Imagine an automotive plant with fluctuating marketing demand , supply chain constraint and increasing production costs. In such a scenario, we can only expect something beyond human intelligence to give smart solution that approximately optimizes every aspect. Now let us think of a system that is integrated with the production system and marketing technical system. This system has a historical account of all orders over the past one year and based on that can predict the average demand for various products that actually updates at the end of day. Going ahead, the internal production system updates the daily demand from the marketing system at a Business...
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...ive Analytics World for Manufacturing – June 17 & 18, 2014 Venue: McCormick Place Speaker proposal deadline: November 1, 2013 Speakers will be notified by: December 31, 2013 If you would like to receive PAW call-for-speakers announcements and notifications as they arise for future PAW events, please provide your email address here: Sign up for call-for-speaker notifications Email Address: All speakers: Please read this call for speakers in its entirety before proceeding to the speaker proposal form (below). Software vendors: If you are a software vendor, read this restriction on speaking. Join Predictive Analytics World (PAW) for Manufacturing to share how predictive analytics delivers a business impact for your organization. PAW Manufacturing is the only conference of its kind, with sessions and content reaching: Across business applications - For what purpose is predictive analytics deployed? Across vendors of solutions and software - How is predictive analytics deployed? Dedicated to the Manufacturing Industry Please read more about the scope, objective and target audience of this conference on the about, FAQ and attendee demograpics pages. Present Your Case Studies PAW Manufacturing provides speakers the opportunity to present predictive analytics case studies, deployment successes and lessons learned. At this event, potential consumers of predictive analytics witness proof demonstrating it's more than just a bunch of great ideas - predictive...
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... Angoss is a global leader in delivering powerful predictive analytics to help businesses find valuable insight and intelligence, while providing a clear and detailed proposal to increase the risk, marketing and sales performance Knowledge STUDIO is a data mining and predictive analysis suite developed for all phases of the development cycle model and use - profile, exploration, modeling, implementation, scoring, and validation, monitoring and building scorecards - all in high-performance visual environment. It is used by marketing, sales and risk analysts to provide business users and analysts specialist with powerful data mining solutions, scalability and complete data mining. Most of the world's leading financial services, insurance, telecommunications, retail, high technology, and healthcare organizations use Angoss predictive analytics to increase revenue, increase sales productivity and improve marketing effectiveness, while also reducing risk and cost. 2. Discuss on data preparation features provided by the product. Known for its industry, Decision Tree patent and a graphical user interface wizard driven which, Knowledge STUDIO is a modeling and predictive analysis workbench for advanced high-performance business analysts and quantitative analysts who offer a robust set of capabilities for the development and utilization of the mining model data for a variety of applications and use cases. Advanced Predictive Modeling Knowledgestudio offers thorough, progressed...
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...Student Name | Nayve, Stephanie Louise G. | Date Submitted | Jan 14, 2016 | Instructor’s Name | Ms. Jennifer Ventus | Assignment # | 1 | Trends and Challenges in Business Analytics | TRENDS | EXPLANATION | EXAMPLES | Real-time Fraud | A strategy in business analytics that helps detect fraud and avoids the chances of theft. | Detecting credit card fraud | Web Display Advertising | A strategy that advertises products/services through the use of Web. | Managing marketing/advertising campaigns on a hourly basis | Call Center Optimization | It is a stategy which aims efficiency meaning more output with less employees. | Optimizing staff | Social media & Social Networking Analysis | It is the process of receiving feedbacks from customers through social media sites which could help in the business and data analysis in the near future. | Mining structured data from blogs; feedbacks from customers | Intelligent Traffic Management | It is managing the deliveries and external transactions of the business in order to meet customer satisfaction. | Traffic flow, rerouting, predicting weather conditions | Smart Power Grids | Using advanced technology to increase productivity and lessen cost. | Monitoring of power consumption | Sustainability | Making sure that carbon footprint does not increase and to think of ways to lessen it. | Measuring and reducing carbon footprint | Bioinformatics | Building new applications | In order to increase productivity and efficiency in checking...
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...Robert(2006)”Companies will be able to carry out cost-benefit studies on recruiting, training, and employee retention (along with its counterpart, layoffs)”.Base on this information companies are tired of playing the guessing game but data mining gives them a more accurate look. All the data gathered such as videos email, social media helps the HR understand the person and gives the business clues. Data Mining gives HR the ability to understand a person and search for the best job candidates through social media like Facebook and twitter analyzing conversations. Stupakevich(2011)” One can perhaps get referrals from whoever a person calls, what they talk about, and who they refer to in the conversations in Facebook or Twitter”(para6). Predictive Analytics Predictive Analytics help measure the behavior of a customer depending on what they respond to or like based on age, income and demographics Some...
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...CIS 500 Complete ClasCIS 500 Complete Class Assignments and Term Paper Click link Below To Download Entire Class: http://strtutorials.com/CIS-500-Complete-Class-Assignments-and-Term-Paper-CIS5006.htm CIS 500 Complete Class Assignments and Term Paper CIS 500 Assignment 1 Predictive Policing CIS 500 Assignment 2: 4G Wireless Networks CIS 500 Assignment 3 Mobile Computing and Social Networking CIS 500 Assignment 4 Data Mining CIS 500 Term Paper Mobile Computing and Social Networks CIS 500 Assignment 1 Predictive Policing Click link Below To Download: http://strtutorials.com/CIS-500-Assignment-1-Predictive-Policing-CIS5001.htm In 1994, the New York City Police Department adopted a law enforcement crime fighting strategy known as COMPSTAT (COMPuter STATistics). COMPSTAT uses Geographic Information Systems (GIS) to map the locations of where crimes occur, identify “hotspots”, and map problem areas. COMPSTAT has amassed a wealth of historical crime data. Mathematicians have designed and developed algorithms that run against the historical data to predict future crimes for police departments. This is known as predictive policing. Predictive policing has led to a drop in burglaries, automobile thefts, and other crimes in some cities. Write a four to five (45) page paper in which you Compare and contrast the application of information technology (IT) to optimize police departments’ performance to reduce crime versus random patrols of the streets...
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...Assignment 4: Data Mining CIS 500 Professor: Dr. Edwin Otto Strayer University August 30, 2013 “Data mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban, 2011). Predictive analytics serves as a benefit of data mining because it’s a process that uses machine learning to analyze data and make predictions. This can be beneficial to a business because it can be helpful in understanding the behavior of customers. A good example of this would be a business using predictive analytics to decide what level of pricing should be used in correlation with sales information. A business could look at historical data for products, sales, and customers to determine the price for a given product and customer at the right time. Amazon is a heavy user of predictive pricing (Mehra, 2013). This technique is also used in Supply Chain Management because it helps you to understand consumer demand to manage the overall process. This includes delivery, returns, forecasting, sourcing, planning, and order fulfillment. The advantage is if a retailer can predict revenue from a specific product in a reasonable amount of time it will result in better inventory management, use of space, cash flow, and the elimination of out of stock items. Association discovery in products sold to customers is used to...
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...mining to the business when employing predictive analytics to the understanding of the behavior of customers. Predictive analytics is area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior. (Bland-Thomas, Karen 2013) It gathers information from a variety of different methods such as: statistics, modeling, machine learning and data mining which is made of current and past information that is used to form future predictions of marketing campaigns and the profit of an organization. Predictive analytics has a four step process to collecting information: 1. Establishing objective: Establish what information that you what to gather, develop a thesis with experts and the data that is required. 2. Collecting good and high quality information: Establish a prediction from consumer’s social media opinions such as: emails, tweets, Facebook posts etc. 3. Understanding consumer’s behavior and intent: Understanding consumer’s behavior and their intent by predicting with organizational wisdom. 4. Predict action: Predicting a consumer’s next purchase at the correct offer and time. Using this method offers many advantages for organizations that realize the value within the enterprise data. Strategically, it provides a measurable establishment for identifying, objectively evaluating and strongly pursing new opportunities within the market. Predictive Analytics identifies the target market, how to reach...
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...Question: Give examples of how OLAP works and its Predictive Analytics INTRODUCTION On-line analytical Processing (OLAP) refers to a computer-based processing with a capability of manipulating and analyzing large volumes of data from multiple perspectives (different points of view). It is one technique you can use to transform data into information. The original system of the OLAP is also known as the multi-dimensional cube or hyper cube. For example, a user can request that data be analyzed to display a spread-sheet showing all of a company's products sold in Ghana in the month of January, compare revenue figures with those for the same products in March, and then see a comparison of other product sales in Ghana in the same time period. Historical Background The first fully functional on-line analytical system was introduced in 1970 by Express, and later in 1995, the Oracle acquired the release for the resource of information. The formal launching for acquisition of OLAP was held in 2007. Oracle also released its own system called Essbase using the OLAP theoretical background and functionality. In 1998, Microsoft stepped in for upgrading and advancement in the OLAP technology. Microsoft worked on the mainstream idea and developed highly advanced online analytical system that is deployed in many large organizations today. Types of OLAP There are 3 types of the on-line analytical systems each with different properties according to the level of use. Multi-dimensional...
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...BUSINESS ANALYTICS Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision making. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making. Examples of BA uses include: * Exploring data to find new patterns and relationships (data mining) * Explaining why a certain result occurred (statistical analysis, quantitative analysis) * Experimenting to test previous decisions (A/B testing, multivariate testing) * Forecasting future results (predictive modeling, predictive analytics) * FUNCTIONS OF A BUSINESS ANALYST At a high level, a Business Analyst acts as a liaison between business people who have business problems and technology people who know how to create automated solutions. A Business Analyst serves the mission-critical function of understanding specific business needs, determining and documenting accurate requirements from business units and presenting those requirements in a manner that is agreeable, measurable and flexible enough to meet project...
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...| Article type: | Research paper | DOI: | 10.1108/09590550510629437 (Permanent URL) | Publisher: | Emerald Group Publishing Limited | Abstract: | Purpose – To empirically analyse the relationship between the shelf space assigned to brands and several factors related to store management. Design/methodology/approach – The data come from a study of 40 product categories in a sample of superstores in Spain. The variables are: shelf space occupied by private labels, private labels market share, number of promotions, assortment (number of brands and number of varieties) and prices gap between private labels and national brands. A neural network analysis is then applied to the data. Methodologically, this method is shown to have better predictive power than a multiple regression. Findings – There is a direct relationship between the space occupied by store brands and the market share, and an indirect relationship between the space and the price differential gap, the number of national brands and the range of choice. Research limitations/implications – The main limitation of this research is the time spent on collecting the data. Another limitation is that some variables have not been included in the...
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