User Experience Testing for LetsPopIn.com

LetsPopIn.com is seeking to enhance its user experience by conducting comprehensive user testing on its platform. The goal of this project is to identify usability issues and gather feedback from real users to improve the overall functionality and user satisfaction. Students will apply their knowledge of user experience design and testing methodologies to create a structured testing plan. They will recruit participants, conduct testing sessions, and analyze the results to provide actionable insights. The project will focus on specific areas of the website, such as the registration process, event creation, and user navigation . By the end of the project, students will have a deeper understanding of user-centered design principles and the importance of iterative testing in product development.

Lead Generation and outreach strategies

LetsPopIn.com, a dynamic platform for event management, seeks to enhance its outreach and lead generation strategies to expand its user base and increase engagement. The project aims to identify effective outreach methods and develop a comprehensive lead generation plan tailored to the company's target audience. Learners will analyze current outreach efforts, research industry best practices, and propose innovative strategies to improve lead acquisition. The project will involve evaluating digital marketing channels, social media engagement, and email marketing campaigns . By applying classroom knowledge in marketing and data analysis, learners will gain practical experience in developing actionable strategies that align with LetsPopIn.com's business objectives.

AI-Driven Event Matcher

LetsPopIn.com aims to enhance user experience by implementing an AI-based event matching system for users to events and with other users at that event. The current challenge is to efficiently connect users with events that align with their interests and preferences and matching them with others present. The goal of this project is to develop a prototype algorithm that can analyze user data and event characteristics to provide personalized event recommendations. This will involve understanding user behavior, preferences, and historical data to create a model that predicts the best event matches. The project will allow learners to apply their knowledge of machine learning, data analysis, and algorithm development. The tasks will include data collection, feature engineering, model training, and evaluation. The project is designed to be completed by a team of learners specializing in data science or computer science within a single academic program.

Smart Grant Recommendation Engine

FindGrant is seeking to enhance its platform by integrating a Smart Recommendation Engine that can suggest relevant grants to users based on their profiles and past grants success data. The goal is to improve user experience by providing personalized grant suggestions, thereby increasing user engagement and satisfaction. This project involves developing an algorithm that analyzes user data, such as interests, previous grant applications, and success rates, to generate tailored recommendations. The engine should be capable of learning and adapting over time to improve its accuracy. Learners will apply their knowledge of data analysis, machine learning, and software development to create a prototype of this recommendation system. The project will focus on creating a scalable and efficient solution that can be integrated into the existing FindGrant platform. - Analyze user data to identify key factors for grant recommendations. - Develop a machine learning model to predict relevant grants for users. - Ensure the recommendation engine is scalable and efficient. - Test and validate the engine's accuracy and adaptability.