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What's the Problem?
For three years, every semester, I used to stress about class planning. Figuring out which classes to take seemed like an impossible, convoluted task. Through research, I discovered that this was not an isolated problem, but one that every university student faced. So I decided to automate this process and by building a machine learning algorithm that makes it easier for students to select and register for classes.
What is our Minimum Viable Product(MVP)?
I built a prototype in my Design and Management of Software class, and now with a proof of concept, I recruited engineers to build a market-ready version for the university. To start, I led our marketing team through rigorous user research, primarily focusing on user interviews with students and university stakeholders, such as registrars and department chairs. The results of the research became the blueprint for our minimal viable product (MVP), so we started to build.
What is our Business Model going to be?
We created a business model where students could save time by using our service and universities could save money on class registration. This model suggested classes to each student based on their preferences and then gathered this data for universities to decide on class offerings. All of this happened at no cost to the end-users!
What do we build?
With initial research complete, we leveraged our user data to define product specifications. Because 90% of our target audience found personalization a valuable product feature, we decided to use the power of machine learning to create tailored recommendations for each student. As more students used our product, the stronger the recommendation they would get on which classes to choose.
What does the Product Roadmap look like?
I translated all of these decisions into user specifications and a product roadmap. The challenge here was to manage both the engineering and marketing processes simultaneously, so I created concurrent roadmaps for both software development as well as product launch marketing. Working in an agile environment led to a more scalable solution, for example, the engineers built a rigorous backend that was completely serverless even though this wasn’t a part of the initial product specs.
The Final Product:
Outcomes:
After leading the team through various product cycles, we were able to create an amazing product. It is now
- Students create 7,000+ course plans every month
- In the first month, 97% of the Santa Clara University student body used our solution.
- 84% of users say this problem has “vastly improved” their class registration experience
I created this startup with no clue on what to build, but solving a user-need led to a larger-than-life impact on thousands of students’ careers.
