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- All Subjects: Data Analytics
- Creators: Department of Information Systems
Description
Millennials are the group of people that make up the newer generation of the world's population and they are constantly surrounded by technology, as well as known for having different values than the previous generations. Marketers have to adapt to newer ways to appeal to millennials and secure their loyalty since millennials are always on the lookout for the next best thing and will "trade up for brands that matter, but trade down when brand value is weak", it poses a challenge for the marketing departments of companies (Fromm, J. & Parks, J.). The airline industry is one of the fastest growing sectors as "the total number of people flying on U.S. airlines will increase from 745.5 million in 2014 and grow to 1.15 billion in 2034," which shows that airlines have a wider population to market to, and will need to improve their marketing strategies to differentiate from competitors (Power). The financial sector also has a difficult time reaching out to millennials because "millennials are hesitant to take financial risks," as well as downing in college debt, while not making as much money as previous generations (Fromm, J. & Parks, J.). By looking into the marketing strategies, specifically using social media platforms, of the two industries, an understanding can be gathered of what millennials are attracted to. Along with looking at the marketing strategies of financial and airline industries, I looked at the perspectives of these industries in different countries, which is important to look at because then we can see if the values of millennials vary across different cultures. Countries chosen for research to further examine their cultural differences in terms of marketing practices are the United States and England. The main form of marketing that was used for this research were social media accounts of the companies, and seeing how they used the social networking platforms to reach and engage with their consumers, especially with those of the millennial generation. The companies chosen for further research for the airline industry from England were British Airways, EasyJet, and Virgin Atlantic, while for the U.S. Delta Airlines, Inc., Southwest Airlines, and United were chosen. The companies chosen to further examine within the finance industry from England include Barclay's, HSBC, and Lloyd's Bank, while for the U.S. the banks selected were Bank of America, JPMorgan Chase, and Wells Fargo. The companies for this study were chosen because they are among the top five in their industry, as well as all companies that I have had previous interactions with. It was meant to see what the companies at the top of the industry were doing that set them apart from their competitors in terms of social media marketing content and see if there were features they lacked that could be changed or improvements they could make. A survey was also conducted to get a better idea of the attitudes and behaviors of millennials when it comes to the airline and finance industries, as well as towards social media marketing practices.
ContributorsPathak, Krisha Hemanshu (Author) / Kumar, Ajith (Thesis director) / Arora, Hina (Committee member) / W. P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
For our collaborative thesis we explored the US electric utility market and how the Internet of Things technology movement could capture a possible advancement of the current existing grid. Our objective of this project was to successfully understand the market trends in the utility space and identify where a semiconductor manufacturing company, with a focus on IoT technology, could penetrate the market using their products. The methodology used for our research was to conduct industry interviews to formulate common trends in the utility and industrial hardware manufacturer industries. From there, we composed various strategies that The Company should explore. These strategies were backed up using qualitative reasoning and forecasted discounted cash flow and net present value analysis. We confirmed that The Company should use specific silicon microprocessors and microcontrollers that pertained to each of the four devices analytics demand. Along with a silicon strategy, our group believes that there is a strong argument for a data analytics software package by forming strategic partnerships in this space.
ContributorsLlazani, Loris (Co-author) / Ruland, Matthew (Co-author) / Medl, Jordan (Co-author) / Crowe, David (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Economics (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Information Systems (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This report details a prioritization value model that was created for the use of Arizona State University and ASU LightWorks in determining and implementing appropriate sustainability projects for removing greenhouse gas emissions. A thorough review regarding the current project selection process, and an extensive analysis into the desired state of the process was conducted for this paper. The newly developed prioritization model includes multiple attributes that rank and prioritize projects based upon the highest value as determined by criteria set forth by the university. Encompassed within this report are the steps in creating the decision model, as well as the benefits and additional uses of the model for the end user. From the analysis and model created, the end user has the ability to choose carbon neutral projects that better align with the vision of the New American University.
ContributorsAmoroso, Nicholas (Co-author) / Lee, Betty (Co-author) / Brooks, Dan (Thesis director) / Johnson, Travis (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor)
Created2015-05
Description
The purpose of this project is to provide our client with a tool to mitigate Company X's franchise-wide inventory control problem. The problem stems from the franchises' initial strategy to buy all inventory as customers brought them in without a quantitative way for buyers to evaluate the store's inventory needs. The Excel solution created by our team serves to provide that evaluation for buyers using deseasonalized linear regression to forecast inventory needs for clothing of different sizes and seasons by month. When looking at the provided sales data from 2014-2016, there was a clear seasonal trend, so the appropriate forecasting model was determined by testing 3 models: Triple Exponential Smoothing model, Deseasonalized Simple Linear Regression, and Multiple Linear Regression.The model calculates monthly optimal inventory levels (current period plus future 2 periods of inventory). All of the models were evaluated using the lowest mean absolute error (meaning best fit with the data), and the model with best fit was Deseasonalized Simple Linear Regression, which was then used to build the Excel tool. Buyers can use the Excel tool built with this forecasting model to evaluate whether or not to buy a given item of any size or season. To do this, the model uses the previous year's sales data to forecast optimal inventory level and compares it to the stores' current inventory level. If the current level is less than the optimal level, the cell housing current value will turn green (buy). If the currently level is greater than or equal to optimal level or less than optimal inventory level*1.05, current value will turn yellow (buy only if good quality). If the current level is greater than optimal level*1.05 current level will be red (don't buy). We recommend both stores implement a way of keeping track of how many clothing items held in each bin to keep more accurate inventory count. In addition, the model's utility will be of limited use until both stores' inventories are at a level where they can afford to buy. Therefore, it is in the client's best interest to liquidate stale inventor into store credit or cash In the future, the team would also like to develop a pricing model to better meet the needs of the client's two locations.
ContributorsUribes-Yanez, Diego (Co-author) / Liu, Jessica (Co-author) / Taylor, Todd (Thesis director) / Gentile, Erica (Committee member) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Predictive analytics have been used in a wide variety of settings, including healthcare,
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
ContributorsBlinkoff, Joshua Ian (Co-author) / Voeller, Michael (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain factors that increase the probability of a successful fourth down conversion in the Power 5 conferences. The logistic regression models predict the likelihood of going for fourth down with a 64% or more probability based on 2015-17 data obtained from ESPN’s college football API. Offense type though important but non-measurable was incorporated as a random effect. We found that distance to go, play type, field position, and week of the season were key leading covariates in predictability. On average, our model performed as much as 14% better than coaches in 2018.
ContributorsVoeller, Michael Jeffrey (Co-author) / Blinkoff, Josh (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The objective of this project was the creation of a web app for undergraduate CIS/BDA students which allows them to search for jobs based on criteria that are not always directly available with the average job search engine. This includes technical skills, soft skills, location and industry. This creates a more focused way for these students to search for jobs using an application that also attempts to exclude positions that are looking for very experienced employees. The activities used for this project were chosen in attempt to make as many of the processes as automatable as possible.
This was achieved by first using offline explorer, an application that can download websites, to gather job postings from Dice.com that were searched by a pre-defined list of technical skills. Next came the parsing of the downloaded postings to extract and clean the data that was required and filling a database with that cleaned data. Then the companies were matched up with their corresponding industries. This was done using their NAICS (North American Industry Classification System) codes. The descriptions were then analyzed, and a group of soft skills was chosen based on the results of Word2Vec (a group of models that assists in creating word embeddings). A master table was then created by combining all of the tables in the database. The master table was then filtered down to exclude posts that required too much experience. Lastly, the web app was created using node.js as the back-end. This web app allows the user to choose their desired criteria and navigate through the postings that meet their criteria.
This was achieved by first using offline explorer, an application that can download websites, to gather job postings from Dice.com that were searched by a pre-defined list of technical skills. Next came the parsing of the downloaded postings to extract and clean the data that was required and filling a database with that cleaned data. Then the companies were matched up with their corresponding industries. This was done using their NAICS (North American Industry Classification System) codes. The descriptions were then analyzed, and a group of soft skills was chosen based on the results of Word2Vec (a group of models that assists in creating word embeddings). A master table was then created by combining all of the tables in the database. The master table was then filtered down to exclude posts that required too much experience. Lastly, the web app was created using node.js as the back-end. This web app allows the user to choose their desired criteria and navigate through the postings that meet their criteria.
ContributorsHenry, Alfred (Author) / Darcy, David (Thesis director) / Moser, Kathleen (Committee member) / Department of Information Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event a soybean is exposed to temperature outside their accepted range is labeled as an instance of stress. Currently, there are few models that use genetic information to predict how crops may respond to stress. Using data provided by an agricultural business, a model was developed that can categorically label soybean varieties by their yield response to stress using genetic data. The model clusters varieties based on their yield production in response to stress. The clustering criteria is based on variance distribution and correlation. A logistic regression is then fitted to identify significant gene markers in varieties with minimal yield variance. Such characteristics provide a probabilistic outlook of how certain varieties will perform when planted in different regions. Given changing global climate conditions, this model demonstrates the potential of using data to efficiently develop and grow crops adjusted to climate changes.
ContributorsDean, Arlen (Co-author) / Ozcan, Ozkan (Co-author) / Travis, Daniel (Co-author) / Gel, Esma (Thesis director) / Armbruster, Dieter (Committee member) / Parry, Sam (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
ContributorsVerma, Ria (Author) / Goegan, Brian (Thesis director) / Moore, James (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The aim of this thesis is to improve the user experience within FedEx's eProcurement system, directly address feedback received from customer surveys, and to make recommendations for the Sourcing and Procurement Division within FedEx. In the first part, the overall client engagement is outlined with the specific timeline between New Venture Group and FedEx. The thesis encompasses three deliverables that were integral parts to the semester-long consulting engagement. The thesis then dives into methodology and each deliverable individually. After months of conference calls and best practice research, consulting efforts are summarized in the results. In a detailed discussion sections, the thesis forecasts opportunities for FedEx within sourcing and procurement. Here, the thesis draws on sources from various companies and research. Furthermore, overall recommendations are given to FedEx and acknowledgements are made. In conclusion, the thesis hopes to offer FedEx improvements to leverage improved functionality of eProcurement that will become available in the next upgrade of the Performance Management System.
ContributorsRuhlman, Payne (Co-author) / Pollack, Amanda (Co-author) / Peterson, Andrew (Co-author) / Taylor, Todd (Thesis director) / Choi, Thomas (Committee member) / Halvorson, Joel (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Department of Economics (Contributor) / School of International Letters and Cultures (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12