Chapter #22. From Zero Budget to Big Ideas: Finding the Perfect AI Mobile App Niche. Interview.
Andrew Shemet
?? Founder & CEO UGC.NINJA | Your Reliable UGC-Ads Partner | Grow your performance with UGC-like ads
Hi everyone! ?? My name is Andrew Shemet. I'm the founder and CEO of UGC Ninja. We have been creating cutting-edge UGC-style creatives in various languages for over 3 years.
We have over 448+ happy clients worldwide, a monthly production capacity of 3000+ UGC creatives, and native researchers in 60+ countries managing our database of 25,000+ creators globally. So yeah, I know what I'm talking about when it comes to UGC-style ads! ??
Follow my newsletter for pro tips, our team's shared experiences, and things you can 1000% relate to while producing UGC-like ads.
DM me Andrew Shemet if you want to talk about your creative strategy.
Synopsis of Chapter
In this newsletter edition, I interviewed? Dmitry Trofimets co-founder of m-m.ventures, a strategic research studio that helps tech businesses identify and test new markets in just three months.
Launching new products and entering markets is inherently risky, but Dmitry and his team have proven that many of these risks—and significant costs—can be avoided through rigorous preparation.
Their approach has driven success for clients like Palta, Arrival, and a series of early-stage startups, with an impressive 90% of clients changing their niches after engaging with m-m.ventures.
In this interview, Dmitry walks us through his evidence-based method for discovering opportunities in the competitive world of AI-powered mobile apps. We’ll explore how his team uses creative data collection techniques, including scraping app stores and analyzing Reddit discussions, to spot trends and unmet user needs.
Let's go!
Today I’m beyond thrilled to sit down with Dmitry Trofimets to uncover how to identify lucrative niches, analyze competition, and align opportunities with emerging AI trends.
?? How did you approach mapping the market at a high level without a budget?
Dmitry: It's crucial to start by noting that we were not trying to establish the uniformed truth, as none of the methods we used were sufficient enough to do so methodologically. Instead we focused on evidence-based scouting, looking for data to support or reject a market niche against other possible niches.
Most dataset needed to spot opportunities are available at a cheap price. We went to list the fastest-growing market segments in the mobile AI health industry and then narrow it down based on key criteria including competitiveness, the extent to which the problem is solved, and AI adoption.?
??What tools or methods were most effective in this initial phase?
Dmitry:?
1. Mobile and health tech industry reports — to find what markets are currently growing. We were aiming at niches that are at least 30% CAGR (yearly growth rate).
2. Google Play / AppStore scraped data to access competition, installs, average ratings.?
3. Reddit and Amazon data to track the key pain points and AI adoption.?
??Can you walk us through the process of scraping data from Google Play and the App Store? What key metrics did you look for when analyzing the data?
Dmitry: We used a trial version of a no-code scraping platform Apify to retrieve search results for specific niches we identified on the first step — this helped bypass the issue of category mismatch. Category mapping in app stores does not reflect most industries, so by focusing on specific predefined search terms we controlled the niche definition.?
To analyze competition and spot opportunities we focused on several metrics:
??How did you identify niches with both low user satisfaction and high projected revenue potential?
Dmitry: We defined low user satisfaction niches as those with lowest ratings and most complaints according to the Reddit dataset – this way we could factor both quantitative as well as qualitative data.?
??Why did you turn to Reddit to identify the popularity of AI use cases?
Dmitry: No pain is completely unsolved. Customers are always experimenting with new solutions and Reddit is a perfect source of early adopters in the US and Western markets for any given niche. By sifting through Reddit you can see the trends in action: what are people trying right now and how does it work? Are they already trying to build their own AI solution for any problem and, if yes, how do they interact with it??
??How did you sift through Reddit discussions to find actionable insights?
Dmitry: To compare niches against each other we collected a similar sample size of relevant subreddit posts for each niche, manually labeled each post and assessed several shares within each sample looking for significant differences.
Shares we assessed included mentions of AI (to see how accustomed are niche customers to AI as of the moment) and customer complaints for current solutions (to further elaborate on a rating assessment to identify least user satisfaction niches).?
??Were there any surprising trends or user needs that stood out during this research?
Dmitry: We based our further research upon this revelation: some B2C mobile niches are much more prone to AI disruption because users are already experimenting or considering turning to Chat GPT and other free alternatives to solve the underlying pain.
??Once you overlaid the data from different sources, how did you determine which niche was the most promising?
Dmitry: ?We ranked niches against each other based on its size, competitiveness, user satisfaction and AI patterns.
We prioritized the largest niches with established AI patterns, yet least competition and user satisfaction.
??What criteria were non-negotiable in making your final decision?
Dmitry: We were not considering over-crowded niches (those would require huge marketing budgets), niches with too little competition (those are too costly to disrupt as a first player) and niches without any AI pattern (we wanted to ride on the AI trend, not form it).
??How did you validate the niche choice before moving forward?
Dmitry: We've conducted a series of interviews with experts, presented options to the client and made sure to choose a niche that is not only prominent, but aligns with the client's long-term plans and passion. After all, building a business around something you don't care about is less likely to be a winning strategy long-term.?
??How has your experience with this process influenced your approach to other projects?
Dmitry: The biggest methodology insights turned out to be how easy it is to scrape data from public sources without any coding. We now proactively apply this toolset for new projects with public data sources.?
??How do you see the landscape of AI mobile apps evolving, and what role do you hope to play in it?
Dmitry: Any AI app incumbent will likely face a similar challenge: AI can be relatively easily implemented by any of your non-AI competitors that are addressing the same set of pain points. What Chat GPT has also taught us is the importance of interaction experience – while everyone can potentially implement AI to some extent, businesses with AI tech at its core often win over users long-term – they simply have a better product in the long-run. As such, we dive deep in our research to uncover hidden insights behind a studied niche even before an MVP is coded? – this way our clients get to really know their customers before building the best possible product tailored to them on a deeper level.??
Key Takeaways:
How do you approach niche identification and market analysis for new projects? Let’s hear your thoughts in the comments!
Or drop me a DM Andrew Shemet , and let’s discuss how data-driven market analysis can help you discover your next big opportunity!