Unstructured Data Analysis to Improve Digital Eligibility of E-Commerce Listings

Abstract:
This project develops a solution using unstructured data methodologies to help improve product
digital eligibility for a national grocer. The motivation for this problem is that only 33% of the
grocers' active products are eligible to be purchased on their website due to inaccuracies in the
product listing. A lack of digital eligibility restricts them from also selling those products in their
stores. The generation of website product descriptions is currently handled manually by vendors,
and multiple products have descriptions which are either missing or not a good representation of
the product. This lowers the digital eligibility and in turn the number of products that could be listed
on the website, which leads to potential losses in sales and company performance. We develop
an algorithm that first correctly identifies poor product descriptions, then secondly generates a
product description based on the available product image(s). We provide empirical results of
various approaches we investigated for this problem, including the popular ChatGPT, and
estimate how our solution ensures a quick turnaround time to make more products available on
their digital platform with fewer errors. This engagement led to an improved automation that will
reduce the effort invested by vendors who manually write the product descriptions.

Student Team:
Sharan Shirodkar (https://www.linkedin.com/in/sharanshirodkar7/)
Suneet Abraham (https://www.linkedin.com/in/suneet-abraham/)
Akshay Deshmukh (https://www.linkedin.com/in/akshayddeshmukh/)
Anish Jasti (https://www.linkedin.com/in/anishjasti3101/)
Nikhitha Siddi (https://www.linkedin.com/in/nikhitha-siddi-319901183/)

Mentor: Matthew Lanham (https://www.linkedin.com/in/mattlanham/)
MS BAIM Program: https://purdue.university/msbaim-homepage

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