Analyzing Industry Data

Our company advertises thousands of products online. We believe we know a lot about our consumers and target consumer base, but we need to look beyond the buyer and examine our industry. We would like to collaborate with students to identify key market trends and assist in our go-forward plans. This will involve several different steps for the students, including: Familiarizing themselves with our products, target market, and industry. Analyzing growth trends, stagnation patterns and decline analytics. Identifying areas of promising innovation and areas that are slowing down. Bonus steps in the process would also include: Recommending strategies to best position our company.

Artificial Intelligence & Machine Learning Application

Our company advertises thousands of products, and we want to leverage the latest technology to gain market advantage. Applications of this technology include recommendation algorithms, predictive analytics like lifetime values, fraud detections, and classifications. We would like to collaborate with students to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students will develop an AI / ML model related to any of the aforementioned applications. This will involve several different steps for the students, including: Conducting background research on our existing products and the dataset. Analyzing our current dataset. Researching the latest AI / ML techniques and how they could be applied to our data. Developing an AI / ML model that provides unique outcomes or insights into our data. Providing multiple solutions that can be applied to solve the same problem.

Recommendation Engine Development

Our company advertises thousands of products through a user interface. We would like to work with students to create a new recommendation system that returns products based on what a user has previously viewed or rated. The recommendations should also consider products viewed by other users who are like the given user. This will involve several different steps for the students, including: Analyzing our existing dataset of users, products, and reviews. Developing a recommendation engine software. Optimizing software runtime performance and assessing areas for improvement. Researching other variables that can improve the quality of product recommendations. Accounting for additional variables in the recommendation engine software. Testing the developed software and making improvements based on additional data.

Potential Customer Segmentation Analysis

In this project, we hope to gain insight into customer segments and patterns by analyzing market data on buying behaviors. This will help us enhance and refocus our marketing efforts by being able to identify the needs of each segment, reach them most effectively, and address any pain points they face. Students will research customer’s behavior through sales trends of various companies, analyze the data, create predictive models, and provide recommendations for how to move forward. This will involve several different steps for the students, including: Analyzing market data to define different segments based on customer needs. Choosing appropriate metrics to examine the data, and analyzing it using data analysis software to identify trends and correlations. Creating predictive models for various outcomes the market may face. Creating a report with all findings from analysis conducted, including recommendations for next steps.