How to provide better value to customers, and generate better company insights
Data is the oil of the digital age…
What next? Unfortunately I wasn’t quite able to figure out how to scrape sufficient shoe reviews to collect a comprehensive enough dataset to really be useful for a recommendation system. However with access to shoe reviews, one could develop a comprehensive recommendation system using methods such as clustering, dimensionality reduction, and non-negative matrix factorization to identify similar shoes. Paired with a customer profile and purchasing habits, one could build a recommender system to help customers leverage hundreds of reviews they certainly couldn’t leverage themself. Business insights could also gleam what common themes exist in shoes, both positive and negative, to better inform stakeholders.
If you’re interested in recommendation systems and don’t feel like skirting around robot.html files and collecting your own dataset, the Jester Dataset is a great place to start in building your own recommendation system.
Thanks to Demitri Haddad who helped me with some of the visual web design aspects
His personal website can be found here