How would you approach estimating the potential market size for a new mobile application targeted at millennials?

During my final year in college, I participated in a project where we had to estimate the market for a new campus app designed to help students find study groups. We began by defining our target audience as college students aged 18-24, focusing on our campus of about 20,000 students. We then researched the percentage of students who use mobile apps for academic purposes, which was around 70%. By applying this percentage to our total student population, we estimated that around 14,000 students might be potential users. Additionally, we considered the growth of similar apps and trends in educational technology. This structured approach helped us present a solid case for our app's necessity and potential engagement.

While volunteering for a local non-profit, I was tasked with estimating attendance for a fundraising event aimed at millennials. We had previously hosted similar events, so I analyzed past attendance numbers, which averaged about 300 attendees. I also surveyed our social media followers, and found that about 40% expressed interest in attending. Using these insights, I estimated that we could expect around 120 attendees from social media alone, plus additional walk-ins. My estimate helped the team plan better for logistics, ensuring we had enough resources and engagement strategies in place.

In my first role at a startup, I was involved in a project where we needed to estimate the market size for a new fitness app targeting young adults. We started by identifying the general population of millennials in our target regions. By using demographic data from government resources, we estimated there were about 50 million millennials in the U.S. We then narrowed it down by considering the percentage of millennials interested in fitness, which we found to be around 30% based on industry reports. This gave us a potential market size of 15 million users. This experience taught me the importance of combining different data sources and assumptions to arrive at a well-rounded estimate.