Muzz
Empowering Women to Like
Helping new female users send their first likes with confidence, turning small actions into meaningful connections.
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Muzz, the leading Muslim marriage and social app with over 13 million users globally, empowers Muslims to find meaningful connections while honouring their values and taking ownership of their marriage journey.
As Muzz expanded into new markets, we faced a key challenge: the time to first match within 24 hours of joining increased, reducing early engagement and risking churn. I was tasked with reducing the time to the first match without making any changes to the existing matching algorithm, as it was central to the app’s value and resource-heavy to modify.
I collaborated with a cross-functional squad consisting of 1 Product Director, 1 Product Manager, 4 Engineers, 1 QA Engineer, and 2 Data Scientists.
I partnered with data scientists to review behavioural patterns across genders and markets. The data revealed that the drop in engagement was driven primarily by new female users, who were three times less likely to engage compared to male users. However, quantitative data alone couldn’t explain why, highlighting the need for qualitative insights.
I collaborated with local marketing specialists to conduct users interviews. The research surfaced three key pain points: Profile compatibility concerns, Hesitation and shyness, and Cultural gender expectations.
The research revealed that psychological barriers, not app usability, were the main factors affecting engagement. I prioritised educational features to empower female users and highlight how sending likes could personalise future matches. I collaborated with cross-functional teams to ensure a scalable, culturally relevant design that addressed varying market expectations.
I led two workshops with my squad and the design team, generating over 10 potential solutions. Using the impact–effort matrix, we narrowed them down to two key concepts: an icon change for liking behaviour to accommodate cultural sensitivity and a “Learn Your Preferences” feature to educate users during onboarding.
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To ensure the design changes resonated globally, I conducted usability tests across key markets, focusing on encouraging users to express interest without feeling pressured and confused. I identified the tick as the most culturally neutral and universally understood option.
I researched competitor onboarding flows and drafted a user flow that guides users who are familiar with the new feature without overwhelming them. This design used progressive disclosure to introduce features gradually, ensuring a smoother onboarding experience and encouraging early engagement.
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While my squad and product owner quickly supported these initiatives, the changes impacted multiple app touch points. I facilitated meetings with adjacent squads, presented user research findings, and mapped the dependencies across features to drive prioritisation. This cross-team alignment ensured that the onboarding updates improved the user experience without delaying key roadmap initiatives.
The liking behaviour icon was changed from a heart to a tick, starting with female users and later rolled out globally across all genders.
The “Learn Your Preferences” feature educated users on how sending likes contributes to a more personalised experience by improving the algorithm’s understanding of their preferences during onboarding. This provided new users with guidance and helped build confidence in their interactions.
Our design empowered new female users to overcome psychological barriers and take confident steps in their marriage search journey.
With more female users taking action, the overall match rate improved, helping users find meaningful connections sooner.
The icon change made the app more inclusive by respecting cultural sensitivities. Both solutions helped users overcome hesitation and engage more confidently with the app.
This project deepened my understanding of how cultural differences shape user behaviour and decision-making. It reminded me not to shy away from exploring bold ideas, but to always question the current experience to see where meaningful improvements can be made.
Collaboration was key to this project’s success. By working with cross-functional teams, I gained new perspectives, learning from data scientists' findings and engineering constraints. These insights helped me make stronger, more informed design decisions.
Collaborate with product and data teams to adjust the feature based on regional performance insights, tailoring it to market-specific behaviours and cultural differences.
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