Launched Personalized Instant Tutoring Platform
Launched Personalized Instant Tutoring Platform
I spearheaded the launch of a personalized instant tutoring platform in the US market, scaling it from concept to 100K+ users in six months. Through deep market research, UX optimizations, and targeted growth strategies, the product became a trusted solution for students seeking on-demand learning support.
I spearheaded the launch of a personalized instant tutoring platform in the US market, scaling it from concept to 100K+ users in six months. Through deep market research, UX optimizations, and targeted growth strategies, the product became a trusted solution for students seeking on-demand learning support.
Challenge
Breaking into the US edtech market meant competing with well-established incumbents. Students wanted instant, personalized support, while tutors expected consistency and fairness. Early challenges included reducing onboarding churn, building trust in a new product, and ensuring effective student–tutor matches that drove repeat sessions. At the same time, scaling quickly without attracting “low-value” free users was essential to building strong unit economics and long-term retention.
Breaking into the US edtech market meant competing with well-established incumbents. Students wanted instant, personalized support, while tutors expected consistency and fairness. Early challenges included reducing onboarding churn, building trust in a new product, and ensuring effective student–tutor matches that drove repeat sessions. At the same time, scaling quickly without attracting “low-value” free users was essential to building strong unit economics and long-term retention.
Results
Within six months of launch, the platform scaled to over 100K users, achieving 120% Q-o-Q growth while maintaining strong engagement metrics. The attribute-driven matching model significantly improved tutor–student compatibility, leading to higher satisfaction and repeat session bookings. By collaborating closely with marketing, we acquired high-LTV user cohorts that boosted retention and monetization potential, rather than attracting low-value free trial users. These efforts not only enhanced activation rates by 30% but also established the platform as both instant and intelligently personalized, carving out a differentiated position in the competitive US edtech market.
Within six months of launch, the platform scaled to over 100K users, achieving 120% Q-o-Q growth while maintaining strong engagement metrics. The attribute-driven matching model significantly improved tutor–student compatibility, leading to higher satisfaction and repeat session bookings. By collaborating closely with marketing, we acquired high-LTV user cohorts that boosted retention and monetization potential, rather than attracting low-value free trial users. These efforts not only enhanced activation rates by 30% but also established the platform as both instant and intelligently personalized, carving out a differentiated position in the competitive US edtech market.
1.5M+
Users Acquired (Globally)
100k+
Users Acquired in 6M (US)
>3%
Conversion Rate
Process
Market Discovery: Researched student/tutor pain points and mapped competitor gaps in personalization and immediacy.
Product Strategy: Defined vision (“instant, personalized tutoring with intelligent matching”) and prioritized features for MVP.
Matching Model: Built an attribute-driven tutor and student scheduling and mapping engine (subject expertise, ratings, availability, engagement History) that improved compatibility and repeat usage.
UX & Onboarding: Redesigned onboarding with contextual nudges, boosting activation by 30% and reducing drop-offs.
Growth Collaboration: Partnered with marketing to run high-LTV acquisition campaigns, targeting student cohorts with higher retention and lifetime value instead of low-quality free trial users.
Agile Execution: Drove sprint planning and cross-functional alignment, rapidly shipping matching logic, UX improvements, and feedback loops.
Iteration & Feedback: Used in-app surveys, tutor ratings, and engagement data to refine both the product and the matching algorithm.
Market Discovery: Researched student/tutor pain points and mapped competitor gaps in personalization and immediacy.
Product Strategy: Defined vision (“instant, personalized tutoring with intelligent matching”) and prioritized features for MVP.
Matching Model: Built an attribute-driven tutor and student scheduling and mapping engine (subject expertise, ratings, availability, engagement History) that improved compatibility and repeat usage.
UX & Onboarding: Redesigned onboarding with contextual nudges, boosting activation by 30% and reducing drop-offs.
Growth Collaboration: Partnered with marketing to run high-LTV acquisition campaigns, targeting student cohorts with higher retention and lifetime value instead of low-quality free trial users.
Agile Execution: Drove sprint planning and cross-functional alignment, rapidly shipping matching logic, UX improvements, and feedback loops.
Iteration & Feedback: Used in-app surveys, tutor ratings, and engagement data to refine both the product and the matching algorithm.
Conclusion
This launch showed how data-driven product design and disciplined growth strategy can create traction in a crowded market. By focusing on user quality, personalization, and scalable matching systems, the platform avoided resource-draining “value suckers” and instead built a base of engaged, high-LTV users. Within six months, the product achieved 100K+ users, 30% higher activation, and 120% Q-o-Q signup growth, while laying a sustainable foundation for monetization and scale. This experience reinforced my ability to lead 0→1 launches, balance growth with unit economics, and deliver personalization at scale.
This launch showed how data-driven product design and disciplined growth strategy can create traction in a crowded market. By focusing on user quality, personalization, and scalable matching systems, the platform avoided resource-draining “value suckers” and instead built a base of engaged, high-LTV users. Within six months, the product achieved 100K+ users, 30% higher activation, and 120% Q-o-Q signup growth, while laying a sustainable foundation for monetization and scale. This experience reinforced my ability to lead 0→1 launches, balance growth with unit economics, and deliver personalization at scale.