...SOCIAL RELATIONSHIPS of MARKETING and ANALYTIC DIMENSIONS NICOLE GAJADHAR ALGOMA UNIVERSITY SOCIAL RELATIONSHIPS of MARKETING and ANALYTIC DIMENSIONS Summary Any business, in order to be successful must have market researchers to analyze behaviors and actions of consumers. Without market researchers it is impossible to maintain a good business to consumer rapport. Businesses to consumer relationships are misunderstood because the dimensions of characteristics such as competence are not explained thoroughly in marketing research. This article identifies the characteristics and relationships between, “communication, trust, commitment, satisfaction and conflict handling” (Malhotra, Agarwal and Du Bisi 2010, p.20) along with a hypothesis created by the authors. The research paper attempts to prove how successful relationship marketing strategy must require an understanding of social dynamics to achieve customer satisfaction. I chose this article because I like how the authors gave thorough explanation of key characteristics behind why retail industries may lack positive business rapport and knowledge on customer satisfaction. Furthermore, I like the evaluation of content for the hypothesis and its findings displayed in the integrated model shown in the article. It explains how each social characteristic is related and how it leads to positive and negative behavior of customer satisfaction and (or) dissatisfaction. Evaluation Customer Satisfaction can be achieved multiple...
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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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...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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...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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...about IoT technology, applications, and challenges. 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...
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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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...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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...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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...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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...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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...I. Organization History SAS Institute Inc. - (Statistical Analysis System) is a software company that has been leading IT analysis since 1976. The company was started on the campus of North Carolina State University when two facility members (Jim Goodnight and John Sall), used grants provided by the United States Department of Agriculture to analyze vast amounts of agricultural data. At the time there was a need for computerized statistics programs, the grants provided allowed Goodnight, Sall and a consortium of eight land-grant universities to create the programs needed. When the grant funding came to a halt in 1972, the group of statisticians agreed to chip in $5,000 apiece each year to keep the project running. The analytical software was licensed by pharmaceutical companies, insurance companies, banks, and also by the academic community that had given birth to the project ("SAS Company Stats," n.d.). With a growing customer base that already numbered close to 100 academic, government and corporate entities, it was evident that success as an independent operation was possible. Goodnight, Sall and two other facility members decided to leave NCSU and develop SAS Institute Inc. - – a private company "devoted to the maintenance and further development of statistical analysis systems." The Company’s mission is to deliver solutions that drive innovation and improve performance ("SAS Company Stats," n.d.). II. Organizational Strengths and Weaknesses A. First organizational...
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...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 Intelligence-The Next Ruler of IT Monalisa Mishra periodic basis say 3 days. The production system is...
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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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