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- All Subjects: E-Commerce
- Status: Published
Description
E-commerce has rapidly become a mainstay in today's economy, and many websites have built themselves around providing a platform for independent sellers. Sites such as Etsy, Storenvy, Redbubble, and Society6 are increasingly popular options for anyone looking to open their own online store. With this project, I attempted to examine the effects of four different marketing techniques on sales in an online store. I opened a shop on Etsy and tracked sales in connection with promotion through social media, selling products in-person at a convention, holding a holiday tie-in sale, and using price anchoring. Social media accounts were opened on Facebook, Tumblr, and Instagram to promote the shop over the course of the project period, and Etsy's web analytics were used to track which sites directed the most traffic to the shop. I attended a convention in mid-January 2016 where I sold my products and distributed business cards with a discount code to track sales resulting from being at the convention. A holiday sale was held in conjunction with Valentine's Day to look at whether holidays influenced purchases. Lastly, a significantly more expensive product was temporarily put in the shop to see whether it produced a price anchoring effect \u2014 that is, encouraged sales of the less expensive products by making them seem affordable in comparison. While the volume of sales data was too small to draw statistically significant conclusions, the project was a highly instructive experience in the process of opening a small online store. The decision-making steps outlined may be helpful to other students looking to open their own online shop.
ContributorsChen, Candice Elizabeth (Author) / Moore, James (Thesis director) / Sanford, Adriana (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In the era of big data, the impact of information technologies in improving organizational performance is growing as unstructured data is increasingly important to business intelligence. Daily data gives businesses opportunities to respond to changing markets. As a result, many companies invest lots of money in big data in order to obtain adverse outcomes. In particular, analysis of commercial websites may reveal relations of different parties in digital markets that pose great value to businesses. However, complex ecommercial sites present significant challenges for primary web analysts. While some resources and tutorials of web analysis are available for studying, some learners especially entrylevel analysts still struggle with getting satisfying results. Thus, I am interested in developing a computer program in the Python programming language for investigating the relation between sellers’ listings and their seller levels in a darknet market. To investigate the relation, I couple web data retrieval techniques with doc2vec, a machine learning algorithm. This approach does not allow me to analyze the potential relation between sellers’ listings and reputations in the context of darknet markets, but assist other users of business intelligence with similar analysis of online markets. I present several conclusions found through the analysis. Key findings suggest that no relation exists between similarities of different sellers’ listings and their seller levels in rsClub Market. This study can become a great and unique example of web analysis and create potential values for modern enterprises.
ContributorsWang, Zhen (Author) / Benjamin, Victor (Thesis director) / Santanam, Raghu (Committee member) / Barrett, The Honors College (Contributor)
Created2018-05