Matching Items (27)

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Attendance Elasticity of Win Percentage in the NBA: An Exploration of the Effects of Team Performance on Home Game Attendance

Description

In the wide world of sports, not all fan bases are created equally—especially in the NBA. Differences in factors like tradition, history, team performance amongst teams make each fan base distinctly unique. This paper will analyze how team performance effects

In the wide world of sports, not all fan bases are created equally—especially in the NBA. Differences in factors like tradition, history, team performance amongst teams make each fan base distinctly unique. This paper will analyze how team performance effects one component of fan behavior: home game attendance. Using win-loss data and home game attendance data for each NBA team from 2001 to 2017, I will construct statistical models to estimate how great of an impact team performance has on each team’s home game attendance. I expect each team’s fan base to respond differently to changes in their team’s win-loss record. This paper will also attempt to quantify other facts that impact attendance at NBA games, including year-to-year changes in team salary expenditures, regional income, and the number of star players playing for the team. Finally, this paper will explore the factors that affect home game attendance for specific games within a given season—things like weather, strength of opponent, and win streaks. Ultimately, the goal of this paper will be to provide NBA business analysts with resources to more precisely anticipate their team’s home game attendance. The ability to understand what motivates the behavior of a fan base is invaluable in creating a marketing strategy that drives fans to the arena. This paper will help to identify teams that are most susceptible to significant fluctuations in attendance and outline alternative strategies to positioning their product offering effectively to fans.

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2018-05

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Monetization of Autonomous Vehicle Data

Description

Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use

Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: 1. Understand if there is monetization potential in autonomous vehicle data 2. Create a financial model of what detailing the viability of AV data monetization 3. Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data.

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2018-05

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The Monetization of Autonomous Vehicle Data

Description

Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use

Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: Understand if there is monetization potential in autonomous vehicle data Create a financial model of what detailing the viability of AV data monetization Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data. First, in order to brainstorm how this data could be monetized, we generated potential use cases, defined probable customers of these use cases, and how the data could generate value to customers as a means to understand what the "price" of autonomous vehicle data might be. While we came up with an extensive list of potential data monetization use cases, we evaluated our list of use cases against six criteria to narrow our focus into the following five: Government, Insurance Companies, Mapping, Marketing purposes, and Freight. Based on our research, we decided to move forward with the insurance industry as a proof of concept for autonomous vehicle data monetization. Based on our modeling, we concluded there is a significant market for autonomous vehicle data monetization moving forward. Data accessibility is a key driver in how profitable a particular company and their competitors can be in this space. In order to effectively monetize this data, it would first be important to understand the method by which a company obtains access to the data in the first place. Ultimately, based on our analysis, Company X has positioned itself well to take advantage of the new trends in autonomous vehicle technology. With more strategic investments and innovation, Company X can be a key benefactor of this unprecedented space in the near future.

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2018-05

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The Data Arms Race: Reimagining Data Transparency, Ethics and Regulations

Description

Data has quickly become a cornerstone of society. Across our daily lives, industry, policy, and more, we are experiencing what can only be called a “data revolution” igniting ferociously. While data is gaining more and more importance, consumers do not

Data has quickly become a cornerstone of society. Across our daily lives, industry, policy, and more, we are experiencing what can only be called a “data revolution” igniting ferociously. While data is gaining more and more importance, consumers do not fully understand the extent of its use and subsequent capitalization by companies. This paper explores the current climate relating to data security and data privacy. It aims to start a conversation regarding the culture around the sharing and collection of data. We explore aspects of data privacy in four tiers: the current cultural and social perception of data privacy, its relevance in our daily lives, its importance in society’s dialogue. Next, we look at current policy and legislature in place today, focusing primarily on Europe’s established GDPR and the incoming California Consumer Privacy Act, to see what measures are already in place and what measures need to be adopted to mold more of a culture of transparency. Next, we analyze current data privacy regulations and power of regulators like the FTC and SEC to see what tools they have at their disposal to ensure accountability in the tech industry when it comes to how our data is used. Lastly, we look at the potential act of treating and viewing data as an asset, and the implications of doing so in the scope of possible valuation and depreciation techniques. The goal of this paper is to outline initial steps to better understand and regulate data privacy and collection practices. Our goal is to bring this issue to the forefront of conversation in society, so that we may start the first step in the metaphorical marathon of data privacy, with the goal of establishing better data privacy controls and become a more data-conscious society.

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2019-05

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Utilizing Machine Learning Methods to Model Cryptocurrency

Description

Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using

Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.

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Date Created
2018-05

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Investigating the Relationship between Neighborhood Socioeconomic Status and Proximity to Public Services

Description

With growing levels of income inequality in the United States, it remains as important as ever to ensure indispensable public services are readily available to all members of society. This paper investigates four forms of public services (schools, libraries, fire

With growing levels of income inequality in the United States, it remains as important as ever to ensure indispensable public services are readily available to all members of society. This paper investigates four forms of public services (schools, libraries, fire stations, and police stations), first by researching the background of these services and their relation to poverty, and then by conducting geospatial and regression analysis. The author uses Esri's ArcGIS Pro software to quantify the proximity to public services from urban American neighborhoods (census tracts in the cities of Phoenix and Chicago). Afterwards, the measures indicating proximity are compared to the socioeconomic statuses of neighborhoods using regression analysis. The results indicate that pure proximity to these four services is not necessarily correlated to socioeconomic status. While the paper does uncover some correlations, such as a relationship between school quality and socioeconomic status, the majority of the findings negate the author's hypothesis and show that, in Phoenix and Chicago, there is not much discrepancy between neighborhoods and the extent to which they are able to access vital government-funded services.

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2018-05

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Social Media Sentiment as a Comparative Business Metric - Using Logical Appeals Among Businesses to Understand Consumer Reaction and Engagement with Various Brands

Description

With the discovery of “Big Data” and the positive impacts properly using data can have on any and every business, it is no wonder that there has been an explosion of companies choosing to implement many possible uses of data.

With the discovery of “Big Data” and the positive impacts properly using data can have on any and every business, it is no wonder that there has been an explosion of companies choosing to implement many possible uses of data. Consumers and any people who may not fully understand the process of collecting, analyzing, and visualizing data may be more easily swayed towards believing something that might not necessarily be true or represented accurately. Often it may feel like every hot topic issue has groups on both sides of the issues using seemingly objective data to prove why their side is correct. Seeing two contradictory sides with seemingly factual data can leave many people confused and unsure what the correct course of action is. With this in mind, I realized that there was a chance the businesses could be creating similar misrepresentations of data to sway customers that the company’s product or service is absolutely a necessity in their lives. After all, the world of marketing and understanding consumer preference is a wildly changing and constant moving target that companies have to navigate. Using data surrounding their products and services to create a desire in consumers to buy and use their offerings seems like a surefire way to successfully target market segments.
As I researched and conducted initial analysis for this project, I quickly ran into a few roadblocks that lead to me needing to pivot off of certain ideas and adapt my initial plans to fit what was actually being done in the current marketing environment. In reality, most businesses are not up for taking the risk of explicitly giving real metrics of their products and services to customers. Due to this, my thesis evolved into finding other ways that companies would use logical appeals to represent their products and comparatively analyze how these companies choose to represent themselves on a social media platform.

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2020-05

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Personalizing the In-Store Shopping Experience

Description

In this paper, I have designed a business model for a new type of fashion retail
store. This store will perfect the personal styling experience by utilizing customer and
apparel data to make individualized apparel recommendations. The format of this

In this paper, I have designed a business model for a new type of fashion retail
store. This store will perfect the personal styling experience by utilizing customer and
apparel data to make individualized apparel recommendations. The format of this store
will heavily reduce the amount of search time for customers by only showing clothing
pieces that each person is likely to purchase, based on predictive analytics. In order to
plan this business model and determine whether a company of this style could be
successful, this paper includes research on the current environment of the fashion
industry, the company’s potential target market segmentation, and tactics for developing
the best customer offering.

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Date Created
2020-05

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Building Management System Integration: Energy Data Analytics

Description

This paper describes the research done to quantify the relationship between external air temperature and energy consumption and internal air temperature and energy consumption. The study was conducted on a LEED Gold certified building, College Avenue Commons, located on Arizona

This paper describes the research done to quantify the relationship between external air temperature and energy consumption and internal air temperature and energy consumption. The study was conducted on a LEED Gold certified building, College Avenue Commons, located on Arizona State University's Tempe campus. It includes information on the background of previous studies in the area, some that agree with the research hypotheses and some that take a different path. Real-time data was collected hourly for energy consumption and external air temperature. Intermittent internal air temperature was collected by undergraduate researcher, Charles Banke. Regression analysis was used to prove two research hypotheses. The authors found no correlation between external air temperature and energy consumption, nor did they find a relationship between internal air temperature and energy consumption. This paper also includes recommendations for future work to improve the study.

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2018-05

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Sagebrush Coffee: Applying Data Analytics to Customer Purchases

Description

Sagebrush Coffee is a small business in Chandler, Arizona that purchases green beans, roasts them in small batches for quality, and ships fresh, gourmet roasted coffee beans across the nation. Deciding which coffee beans to buy and roast is one

Sagebrush Coffee is a small business in Chandler, Arizona that purchases green beans, roasts them in small batches for quality, and ships fresh, gourmet roasted coffee beans across the nation. Deciding which coffee beans to buy and roast is one of the most crucial business decisions Sagebrush and other gourmet coffee roasters face. Further complicating this decision is the fact that coffee is a crop, and like all crops, has a specific growing season and the exact same product cannot usually be ordered from year to year, even if it proves to be successful. The goal of this research is to use data analytics and visualization to help Sagebrush make better purchasing decisions by identifying consumer purchasing trends and providing a recommendation for their portfolio mix. In the end, I found that Latin American coffees are popular with both returning and first-time customers, but a specific country of origin does not appear to be associated with the top coffee producing countries. Additionally, December is a critical month for Sagebrush and Sagebrush should make sure to target the states with the most sales: California, Pennsylvania, and New York. Arizona has growth potential as it is not one of the top three locations, despite the presence of a physical store. Also included in the following report is a portfolio recommendation suggesting how many of each product based on region, processing type, and roast level to carry in inventory.

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2018-05