Matching Items (3)

134302-Thumbnail Image.png

Sneaker Life: The Critical Analysis of Marketing Strategies Administered by Nike and Under Armour

Description

My thesis will revolve around the ideology and sociology of the sneaker brand and in particular, basketball sneakers. The mega sneaker superpower Nike and underdog Under Armour have shoes they

My thesis will revolve around the ideology and sociology of the sneaker brand and in particular, basketball sneakers. The mega sneaker superpower Nike and underdog Under Armour have shoes they want to sell and consumers they want to buy them. I will discuss how the advertisements are used and implanted by both Nike and Under Armour. The two points of reference from each company will be LeBron James, Nike, and Stephen Curry, Under Armour. Both basketball players have signature shoes and undoubted the NBA's most relevant players this past season. The two players just so happened to face off against each other int eh NBA finals, which enhanced the marketing potential for both companies. Thus, the advertisements for these and their shoes would have been at its peak trying to sway consumers to either side. Nike and Under Armour both ploy attempts in creating marketing material to attract their consumer base. The thesis will look at why sneakers have become a social trend and high commodity. I will look at how pop culture and psychological diseases play a role in the consumers' choice to purchase either shoe. The work as a whole will attempt to bring forth some revitalizing information on today's sneaker culture. Research was limited, however with the information to conduct this thesis, the thesis should spark interest in new research related fields. Thus, bring forth a new renaissance in today's culture: Sneaker Life.

Contributors

Agent

Created

Date Created
  • 2017-05

134252-Thumbnail Image.png

The Sneaker Life: A Critical Analysis of Nike and Under Armour Marketing Strategies

Description

My thesis will revolve around the ideology and sociology of the sneaker brand and it particular, basketball sneakers. The mega sneaker superpower Nike and the under dog of Under Armour

My thesis will revolve around the ideology and sociology of the sneaker brand and it particular, basketball sneakers. The mega sneaker superpower Nike and the under dog of Under Armour have shoes they want to sells and consumers they want to buy them. I will discuss how the advertisement are used and implanted but both Nike and Under Armour. The two points of references from each company will be LeBron James, Nike, and Stephen Curry, Under Armour. Both basketball players have signature shoes and are undoublty the NBAs most relevant players this past season. The two players just so happened to face off against each other in the NBA finals, which enhanced the marketing potential for both companies. Thus, the advertisements for these and their shoes would have been its peak trying sway consumers to either side. Nike and Under Armour both ploy attempts in creating marketing material to attract their consumer base. The Thesis will look at why sneakers have become a social trend and high commodity. I will look at how pop culture and psychological diseases play a roll in the consumers' choice to purchase either shoe. The work as a whole will attempt to bring forth some revitalizing information on today's sneaker culture. Research was limited, however with the information to conduct this thesis, the thesis should spark interest in a new research related field. Thus, bringing forth a new renaissance in today's culture; the Sneaker Life.

Contributors

Agent

Created

Date Created
  • 2017-05

132957-Thumbnail Image.png

Predicting Sneaker Resale Prices using Machine Learning

Description

This thesis dives into the world of machine learning by attempting to create an application that will accurately predict whether or not a sneaker will resell at a profit. To

This thesis dives into the world of machine learning by attempting to create an application that will accurately predict whether or not a sneaker will resell at a profit. To begin this study, I first researched different machine learning algorithms to determine which would be best for this project. After ultimately deciding on using an artificial neural network, I then moved on to collecting data, using StockX and Twitter. StockX is a platform where individuals can post and resell shoes, while also providing statistics and analytics about each pair of shoes. I used StockX to retrieve data about the actual shoe, which involved retrieving data for the network feature variables: gender, brand, and retail price. Additionally, I also retrieved the data for the average deadstock price for each shoe, which describes what the mean price of new, unworn shoes are selling for on StockX. This data was used with the retail price data to determine whether or not a shoe has been, on average, selling for a profit. I used Twitter’s API to retrieve links to different shoes on StockX along with retrieving the number of favorites and retweets each of those links had. These metrics were used to account for ‘hype’ of the shoe, with shoes traditionally being more profitable the larger the hype surrounding them. After preprocessing the data, I trained the model using a randomized 80% of the data. On average, the model had about a 65-70% accuracy range when tested with the remaining 20% of the data. Once the model was optimized, I saved it and uploaded it to a web application that took in user input for the five feature variables, tested the datapoint using the model, and outputted the confidence in whether or not the shoe would generate a profit.
From a technical perspective, I used Python for the whole project, while also using HTML/CSS for the front-end of the application. As for key packages, I used Keras, an open source neural network library to build the model; data preprocessing was done using sklearn’s various subpackages. All charts and graphs were done using data visualization libraries matplotlib and seaborn. These charts provided insight as to what the final dataset looked like. They showed how the brand distribution is relatively close to what it should be, while the gender distribution was heavily skewed. Future work on this project would involve expanding the dataset, automating the entirety of the data retrieval process, and finally deploying the project on the cloud for users everywhere to use the application.

Contributors

Agent

Created

Date Created
  • 2019-05