...[pic] Retail Loss Prevention: Doing more with Analytics February 2009 Abstract T he retail industry is in the middle of an unprecedented economic crisis. All retailers are trying to figure out how to cut costs, retain customers, conserve cash and more importantly stay in business. Recently, the National Retail Federation (NRF) polled readers of its SmartBrief asking them what was on top of their mind. Loss Prevention (LP) came in second only to the overall economy! It is no surprise given that every dollar saved from retail shrink is a dollar added directly to the bottom-line. Looking back in history, we have seen tough times like these are conducive for higher shrink numbers. This is mainly due to retailers cutting down loss prevention staffing and store personnel, slowdown in technology investments, and increase in theft owing from people who cannot handle the economic pressure. LP organizations are at different stages of evolution when we look at their capability to harness the power of analytics – From basic reporting on shrink to understanding the key drivers with high correlation to shrink and managing by exception with the help of predictive models. There is a need to utilize available data assets effectively by building capabilities to report, analyze and predict shrink accurately. This article reviews the trends in retail shrink, its sources and how analytical techniques can help attack shrink in a cost effective manner. Retail Shrink Trends ...
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...O C T O B E R 2 0 11 m c k i n s e y g l o b a l i n s t i t u t e Are you ready for the era of ‘big data’? Brad Brown, Michael Chui, and James Manyika Radical customization, constant experimentation, and novel business models will be new hallmarks of competition as companies capture and analyze huge volumes of data. Here’s what you should know. The top marketing executive at a sizable US retailer recently found herself perplexed by the sales reports she was getting. A major competitor was steadily gaining market share across a range of profitable segments. Despite a counterpunch that combined online promotions with merchandizing improvements, her company kept losing ground. When the executive convened a group of senior leaders to dig into the competitor’s practices, they found that the challenge ran deeper than they had imagined. The competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization— from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business...
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...The New Frontier: Data Analytics Phylicia Marie Phillips Professor Progress Mtshali, Ph. D. Information Systems Decision-Making April 17, 2016 In the past, analytics was reserved for back-room debates by data geeks producing monthly reports on how things are going. Today, analytics make a difference in how a company does business, day to day, and even minute by minute; more specifically how Walmart does business. As many know, Walmart is an American based multinational retail corporation that operates a chain of hypermarkets, grocery stores and discount stores. With over eleven thousand stores and clubs in 27 countries, information technology and data analytics play a major role in Walmart’s survival and helps maintain its competitive advantage. Data Analytics Overview The business intelligence and analytic technologies and applications currently adopted in industry can be considered as BI&A 1.0, where data are mostly structured, collected by companies through various legacy systems, and often stored in commercial relational database management systems (Bottles and Begoli, 2014). The analytical techniques most commonly used in these systems, popularized in the 1990s, are mainly grounded in statistical methods developed in the 1970s and data mining techniques developed in the 1980s (Chiang, 2012). The digitalization of information has created more data and the development of cloud computing, and faster and faster computers has made the increased data more accessible...
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...of my company, I am not familiar with the names or metrics used to evaluate important data, but I do know from experience and part of my job function, reports and data gathered are used to make judgments and decisions about new products and constant improvements for existing services we currently provide. Surveys are completed by our travel partners and guests, and even employees. We compile reports and present them to management electronically. Our research, experience, and use of different applications, along with our IT departments, helps management and executives determine which direction to move forward. Feedback from our travel partners and guests are both direct and indirect. Upon learning about programs such as Google Analytics, the importance of webpage layout, the amount of time spent on our site, as well, as how often individuals contact our chat system and sales automation for assistance with our product gives insight to “how we are doing” as a company. Constant looping and revisiting certain pages...
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...volume, which is the quantity of data. The second is Variety, which the type of Data. The third is velocity, which is the speed of the data is gathered. The fourth one Variability, which is inconsistency of data can hamper processes to manage it. The final one is Veracity, which is the quality of data captured can vary. These data sets are growing rapidly mainly because they are gathered at a fairly cheap. The world's technological per-capita are doubling every 40 months. Business intelligence with data with high information density to look for trends. Big Data also increased information management specialist. Some of the largest companies like IBM and Microsoft spent over 15 billion dollars on software firms which specialize in data analytics. Governments use big data because it's efficient in terms of productivity and innovation. While gathering big data is a big benefit there are also some issues that need to brought up. Some policies involving privacy, security, and liability. Companies would need to put the right people to manage and use this data efficiently. Accessibility is also crucial because most likely there will need to be third parties included and incentives put in place to enable...
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...Date: 04-19-2015 The New Frontier: Data Analytics (Course title: Info System Decision Making) Professor: Clifton Howell Student: Deep Ajabani Data analysis is the process of finding the right data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to have the biggest possible impact. Analytics have been used in business since the management exercises were put into place by Frederick Winslow Taylor in the late 19th century. Henry Ford measured the time of each component in his newly established assembly line. But analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics have changed and formed with the development of enterprise resource planning (ERP) systems, data warehouses, and a large number of other software tools and processes. In later years the business analytics have exploded with the introduction to computers. This change has brought analytics to a whole new level and has made the possibilities endless. As far as analytics has come in history, and what the current field of analytics is today many people would never think that analytics started in the early 1900s with Mr. Ford. We are going to have a look on Big Data Analytics. Let’s have a look on advantages of big data analytics. It helps marketing companies build models based on historical data to predict...
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...Definition IoT is one of the fastest growing technologies in computing. It is an environment where people, animals, or objects are presented with unique identifiers and the ability to transfer data over a network (Rouse, 2014). It has emerged from combining wireless technologies, micro-electromechanical systems, and the internet (Rouse, 2014). See Figure 1. These wireless technologies are equipped with, or connected to a smart device allowing data collection and communication through the internet (Caron, Bosua, Maynard, & Ahmad, 2016). Figure 1. IoT Ecosystem (Medici, 2015) Benefits * Tracking behavior for real-time marketing (Borne, 2014). * Enhanced situational awareness (Borne, 2014). * Sensor-driven decision analytics (Borne, 2014). * Process optimization (Borne, 2014). * Optimized resource consumption (Borne, 2014). * Instantaneous control and response in complex autonomous systems (Borne, 2014). * Increase operational efficiency, power new business models, and improve quality of life (Harrell, 2015). * Provide an accurate analysis of customer data (Medici, 2015). Some Applications of IoF Business intelligence (BI). “The BI application ensures the analysis and measurement of the consumer’s thoughts, behaviors, relationships, buying attitudes, choices, and many more parameters that form the backbone of effective strategy building, business operations management, customer relationship management, and other business operations” ...
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...companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses. This course prepares students to understand structured data and business analytics and become leaders in these areas in business organizations. This course teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing students to apply business analytics algorithms and methodologies to real-world business datasets from finance, marketing, and operations. The use of real-world examples and cases places business analytics techniques in context and teaches students how to avoid the common pitfalls, emphasizing the importance of applying proper business analytics techniques. In addition to cases, this course features hands-on experiences with data collection using Python programs and analytics software such as SAS Enterprise Guide. Throughout the semester, each team works to frame a variety of business issues as an analytics problem, analyze data provided by the company, and generate applicable business insights as a secondary objective, while also learning essential business analytics techniques. Students benefit...
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...Disruptive Innovation: A new era of Crowdsourced Data Analytics! Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. General Management General Management MBA Core, 2nd Year MBA Core, 2nd Year Ayush Malhotra NMIMS,Mumbai Ayush Malhotra ...
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...Spring 2016 | Elective | FIN | FIN7503 | EQUITIES | Spring 2016 | Elective | FIN | FIN7504 | RISK MANAGEMENT | Spring 2016 | Elective | FIN | FIN7511 | CORP FIN I:RASNG CAP | Spring 2016 | Elective | FIN | FIN7513 | FIXED INCOME | Spring 2016 | Elective | FIN | FIN7516 | CORP FIN II:EVAL OPP | Spring 2016 | Elective | FIN | FIN7517 | FIN & VAL SUSTNBLTY | Spring 2016 | Elective | FIN | FIN7518 | Managing Portfolios | Spring 2016 | Elective | FIN | FIN7565 | RE INV FUNDAMENTALS | Spring 2016 | Elective | FIN | FIN7572 | BABSON COLLEGE FUND | Spring 2016 | Elective | FIN | FIN7573 | INVESTMENT BANKING | Spring 2016 | Elective | FIN | FIN7578 | RE DEVELOPMENT | Spring 2016 | Elective | MATH | QTM7571 | Bus Intel, Analytics, Visualization | Spring 2016 | Elective | MATH | QTM9515...
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...become an analytic competitor. Questions as to what are the sources of Decision Making to an analytic competitor will also be discussed. A discussion on how influential quantitative modeling is and its utility in business decision making will be summarized. Finally, a Christian view that presents an ethical perspective on quantitative modeling and decision making will be presented. In an ever increasing global environment, maintaining a competitive advantage can be sustained through quantitative modeling, which can make a company a viable analytic competitor. How Can a Company Become and Be an Analytics Competitor Competitors make it increasingly more difficult to maintain a strategic competitive advantage when exclusive technologies, products and services can be duplicated (Davenport, Cohen & Jacobson, 2005). Organizations are now framing their strategies to accomplish optimization of “key business processes”: serving optimal customers, optimize supply chains, and understand and create optimal financial performance (Davenport, et al., 2005, p. 1). Optimization strategies demand that organizations now gather extensive data and perform extensive analysis that will guide executives in the decision-making process. The data and the analysis must be specific to the issues. In other words, they must relate in order to be useful in decision-making for the firm. In order to benefit from this competitive advantage companies also have to possess the analytic capabilities...
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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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...biggest retailer in the world and handles more than one million customer transactions every hour and generates more than 2.5 petabytes of data storage (Venkatraman & Brooks, 2012). To put this into perspective, this data is equivalent to 167 times the number of books in America’s Library of Congress (Venkatraman & Brooks, 2012). So how can Wal-Mart use this massive amount of data and what useful information can this data provide? This paper will provide a brief overview of the importance of Business Intelligence (BI) and how the largest retailer in world, Walmart, is using it. BI platforms help management to truly understand its customer base and deliver individualized products and services (Brannon, 2010). When BI tools and analytics are used effectively, managers and decision makers can yield an all-encompassing view of the company, its position in the market, and its potential and perspectives (Albescu, and Pugna 2014). BI is best explained as a systematic process not found in a magazine, online or in a knowledge database. An organization that doesn’t have a viable BI capacity is making decisions without key information in this competitive market (Thomas, 2001). Walmart has more customer connections than any retailer in the world, from online activity to in-store purchases, and even social mentions (300,000 social mentions per week) (SAS Institute Inc.). Due to the abundance of information requiring analysis, Walmart created Walmart Labs after the company took...
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...1.Choose one data mining tools and briefly explain on background of the product. 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...
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...many reasons when gathering information. Businesses that use it are finance, retail and banks for the purpose of finding information on a company or individual. Most business use data mining to predict sales, credit card fraud and to find out what makes the patient ill. HR departments use data mining to predict the value of the employee. Robert (2006)” The eventual goal is to project how much workers will produce over their careers”(para6). This tactic helps companies predict employees who will stay longer in the company as time goes by. The information is then stored into their database to help in the hiring process. “ 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...
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