Matching Items (18)
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- All Subjects: Data Analytics
- Creators: Department of Information Systems
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
- Status: Published
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
The rising age of the Baby Boomer generation has made a significant impact on the workforce, leaving leadership gaps that Generation X is unable to fill. This leaves an opportunity for the Millennial generation to step up and use their strengths and skills to become stronger leaders of the business and sales industry.
To bridge the gap between the growing sales industry there is the ability to properly train Millennials so they are successful and stay within their roles longer. By attacking this problem from a university level by strengthening sales programs as well as having employers understand and respond to needs of the Millennial generation, this will create an overall successful Millennial salesperson that will stay with their employer long term.
Strengths and weaknesses of this generation are also important to understand. Millennials are known to be tech-savvy, open-minded, collaborative, and connected, resourceful networkers. They also carry weaknesses and stereotypes of being lazy, lacking communication skills, impatient, entitled, and demanding of feedback and work flexibility. From an employer, they expect a large salary as well as a good culture, manager feedback, a mentor, work-life integration, an employer with a social responsibility mindset, and a sense of purpose.
An analysis of 12 sales programs at various universities across the country helped to understand what is being taught and offered to students as well as commonalities and differences that make a strong sales program. Commonalities among these programs include, about 250+ students, high job placement, sales labs, hosting and competing in sales competitions, and a desire to expand and grow their programs. Unique aspects of various programs were partnerships with the sales industry, hosting fundraisers, student ambassadors for the sales program, CRM courses, and internships and competition requirements.
Primary research was conducted to understand various sales development programs from companies in the sales industry. The 12 companies that participated in this research were from Arizona State University’s Sales Advisory Board. These companies completed a survey that provided detailed information of their onboarding and training process as well as their opinions of Millennial employees.
From this research, recommendations were formed for employers,
• creating a collaborative and innovative culture
• A mentorship program
• work flexibility
• continuous learning
• sense of purpose
As for Arizona State’s Sales Program, recommendations include,
• a mentorship program between Sales Scholars and the Sales Advisory Board
• creating a sales lab
• implementing CRM curriculum in classes
• continued support from the Board and alumni of the sales program
To bridge the gap between the growing sales industry there is the ability to properly train Millennials so they are successful and stay within their roles longer. By attacking this problem from a university level by strengthening sales programs as well as having employers understand and respond to needs of the Millennial generation, this will create an overall successful Millennial salesperson that will stay with their employer long term.
Strengths and weaknesses of this generation are also important to understand. Millennials are known to be tech-savvy, open-minded, collaborative, and connected, resourceful networkers. They also carry weaknesses and stereotypes of being lazy, lacking communication skills, impatient, entitled, and demanding of feedback and work flexibility. From an employer, they expect a large salary as well as a good culture, manager feedback, a mentor, work-life integration, an employer with a social responsibility mindset, and a sense of purpose.
An analysis of 12 sales programs at various universities across the country helped to understand what is being taught and offered to students as well as commonalities and differences that make a strong sales program. Commonalities among these programs include, about 250+ students, high job placement, sales labs, hosting and competing in sales competitions, and a desire to expand and grow their programs. Unique aspects of various programs were partnerships with the sales industry, hosting fundraisers, student ambassadors for the sales program, CRM courses, and internships and competition requirements.
Primary research was conducted to understand various sales development programs from companies in the sales industry. The 12 companies that participated in this research were from Arizona State University’s Sales Advisory Board. These companies completed a survey that provided detailed information of their onboarding and training process as well as their opinions of Millennial employees.
From this research, recommendations were formed for employers,
• creating a collaborative and innovative culture
• A mentorship program
• work flexibility
• continuous learning
• sense of purpose
As for Arizona State’s Sales Program, recommendations include,
• a mentorship program between Sales Scholars and the Sales Advisory Board
• creating a sales lab
• implementing CRM curriculum in classes
• continued support from the Board and alumni of the sales program
ContributorsQuinn, Jacklyn Michelle (Author) / Montoya, Detra (Thesis director) / Dietrich, John (Committee member) / Department of Marketing (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
The millennial generation is quickly solidifying its place as the dominate generation within the workforce. As millennials transition through workplace hierarchy it is essential organizations understand how to properly develop incoming talent. This is especially important within sales as the opportunity cost for hiring and developing new sales professionals is much higher compared to other professions. Downward trends in millennial retention rates is also a strong contributing factor to the importance of understanding the millennial generation. This paper aims to identify key concepts and elements employers should incorporate into their sales training programs in order to better develop millennials entering sales roles. Through an analysis of each generation and sales training a clear framework will be identified to achieve this goal. Analyzing millennials unique strengths and weaknesses will provide the basis for the key areas employers need to focus on when designing their sales development programs. The framework identified is easily adaptable within any organizations as the concepts discussed can be universally applied.
ContributorsStensland, Zachary William (Author) / Montoya, Detra (Thesis director) / Schlacter, John (Committee member) / Department of Marketing (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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
Twitter is one of the most powerful communication tools ever created. There are over 1.3 billion registered Twitter users (Smith, 2016). 100 million daily people actively use Twitter every day. 6,000 tweets are tweeted every second. Communication has never been so abundant, public, and chronicled. Not only is there a gigantic population to market to, but also a wealth of information about that population to record and draw insights from. However, many companies' Twitter accounts fail to generate popular posts on a regular basis. The content that they produce is ineffective and uninteresting. In my opinion, these companies are failing to take advantage of a huge opportunity. I decided to dive into the Twitter accounts of some of my favorite companies to see what they were doing wrong and how they could improve. My thesis investigates 18 different company Twitter accounts from four different industries: Athletic Apparel, Technology, Online Entertainment, and Car Manufacturing. I pulled 200 tweets from each company and cleaned and organized the data into an Excel spreadsheet. I investigated how certain variables impacted tweet popularity across the four industries. First, I looked at tweet format to determine whether posts, retweets, or replies were the best format. Then, I analyzed how different elements of a tweet's content could impact the tweet's popularity. Specifically, I looked at the effects of including links, hashtags, and questions into the tweet. Next, I tried to determine the optimal tweet length for each industry. And finally, I compared each industry's tweet sentiment preferences. I then summarized my findings into a series of recommendations for companies to improve their tweet popularity.
ContributorsFrame, Christopher James (Author) / Clark, Joseph (Thesis director) / Jenkins, Anthony (Committee member) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
League of Legends is a Multiplayer Online Battle Arena (MOBA) game. MOBA games are generally formatted where two teams of five, each player controlling a character (champion), will try to take each other's base as quickly as possible. Currently, with about 70 million, League of Legends is number one in the digital entertainment industry with $1.63 billion dollars of revenue in year 2015. This research analysis scopes in on the niche of the "Jungler" role between different tiers of player in League of Legends. I uncovered differences in player strategy that may explain the achievement of high rank using data aggregation through Riot Games' API, data slicing with time-sensitive data, random sampling, clustering by tiers, graphical techniques to display the cluster, distribution analysis and finally, a comprehensive factor analysis on the data's implications.
ContributorsPoon, Alex (Author) / Clark, Joseph (Thesis director) / Simon, Alan (Committee member) / Department of Information Systems (Contributor) / Department of Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05