Product recommendation on scraped usage data
About the Project
The primary goal of this project is to analyze patterns in product adoption to effectively engage with existing and/or potential customers. Our primary dataset consists of two large datasets regarding many companies’ qualities across the United States over a span of 20 years, ranging from 2001 to the present.
The general goal of the project is to understand whether we can provide relevant recommendations to companies based on the information that they make publicly available
Two of the main features we used are ‘signal score,’ which is a 1-3 score of how recently the product was observed, and ‘intensity’ which is a measure of how much a product is observed over time.
Student Team
- Atherv Gole
- Cristian Razo
- Eric Cha
- Natasha Leodjaja
- Qimin Tao
Mentors
- Rob Fox, Sponsor
- Lauren Wong, Sponsor
- Leron Reznikov, TA
Presentation
About HG Insights
HG is the ‘Holy Grail’ of data-driven insights and we help business innovators Go-To-Market with confidence, capture market share, and scale with velocity.
Refined from over 20 billion unique sources, our insights empower you to accurately size your markets, efficiently allocate your resources, prioritize accounts, target the right prospects, increase revenue, and act with confidence.
Read more about HG Insights here.
