Mobi

From User Insights to Social Influence

Mobi is an innovative movie ticket booking platform designed to enhance the user experience by addressing common decision-making challenges. Through in-depth user research, we discovered that many users experience decision paralysis when selecting a movie, often abandoning the booking process altogether. However, we also found that our target audience has a strong desire for self-expression and enjoys showcasing their personality and interests.

Leveraging these insights, we designed a quiz-based recommendation system that transforms movie selection into an interactive and social experience. By allowing users to express themselves through personality-driven recommendations and encouraging social sharing, Mobi turns movie selection into a fun, engaging process that influences peer behavior and increases ticket sales.

Empathy & User research

Understanding the Pain Points

To better understand user behavior, we mapped out the movie-watching experience using an empathy map and user journey map. These tools helped identify key pain points:

Decision paralysis

Users often feel overwhelmed by too many choices, making it difficult to finalize a selection.

Preference uncertainty

While users may have two or three movies in mind, they struggle to decide which one to watch.

Lack of motivation to purchase

Without strong personal motivation, users may abandon the purchase process altogether.

Further observations revealed a common behavioral trait among our target users

The Desire for Self-Expression & Social Influence

They enjoy self-expression and love sharing their personality and interests on social media.

This insight became the driving force behind Mobi’s interactive and social engagement features. We recognized an opportunity to turn movie selection into a form of self-expression, leveraging social interaction to drive ticket sales.

Design ideation

From Insights to Design: The Interactive Quiz System

Matching users with the right movie

To address decision paralysis and preference uncertainty, Mobi introduces a quiz-based recommendation system inspired by self-identity and social influence theories.

Users answer a series of personality-driven questions, which then match them with a character from a currently trending movie. This connection to a recognizable character enhances self-identification, making the recommendation feel more personal and meaningful.

Matching users with the right movie

To address decision paralysis and preference uncertainty, Mobi introduces a quiz-based recommendation system inspired by self-identity and social influence theories.

Users answer a series of personality-driven questions, which then match them with a character from a currently trending movie. This connection to a recognizable character enhances self-identification, making the recommendation feel more personal and meaningful.

Encouraging Social Engagement & Peer Influence

Once matched with a movie character, users are encouraged to share their results on social media, such as Instagram. This design taps into their self-expression needs, allowing them to showcase their personality while also influencing their social circle.

  • Social Proof & Peer Influence : Seeing friends share results increases curiosity and discussion, making others more likely to take the quiz and consider watching the recommended movie.

  • Group Movie Plans : Friends with similar results may be inspired to watch the movie together, fostering group discussions and increasing overall ticket sale

Encouraging Social Engagement & Peer Influence

Once matched with a movie character, users are encouraged to share their results on social media, such as Instagram. This design taps into their self-expression needs, allowing them to showcase their personality while also influencing their social circle.

  • Social Proof & Peer Influence : Seeing friends share results increases curiosity and discussion, making others more likely to take the quiz and consider watching the recommended movie.

  • Group Movie Plans : Friends with similar results may be inspired to watch the movie together, fostering group discussions and increasing overall ticket sale

Seamless Ticket Booking: From Sharing to Conversion

To ensure a smooth transition from social engagement to ticket booking, the quiz results page includes an intuitive swipe-up gesture, allowing users to instantly access movie details.

  • Minimizes Hesitation : Users can quickly view showtimes and book tickets without leaving the experience.

  • Reduces Steps Between Discovery & Purchase : By integrating the booking process directly into the results page, Mobi lowers friction and increases conversion rates.

Design system

The visual identity of cinemas

To create a cinematic experience, Mobi adopts a black and golden-yellow color scheme, reminiscent of traditional theaters and movie posters. The visual identity aligns with the platform’s goal of making movie selection feel exciting, dynamic, and engaging.

Future Enhancements

Future Mobi redefines the traditional ticket-booking process by transforming it into a personalized, psychological, and social experience. By leveraging user research, behavioral insights, and interactive design, the platform not only simplifies decision-making but also creates excitement and engagement around movie selection. Through social sharing and peer influence, MOBE effectively increases ticket sales while offering users a fun and meaningful way to choose their next movie.

Mobi redefines the traditional ticket-booking process by transforming it into a personalized, psychological, and social experience. By leveraging user research, behavioral insights, and interactive design, the platform not only simplifies decision-making but also creates excitement and engagement around movie selection. Through social sharing and peer influence, Mobi effectively increases ticket sales while offering users a fun and meaningful way to choose their next movie.

Optimizing Social Sharing Features

Exploring additional social mechanics such as a “challenge your friends” feature to boost engagement.

Improving Recommendation Accuracy

Enhancing the psychological model by incorporating user feedback and behavioral data.

Type

Final project

Advisor

Chun-Ting Wu

Designer

Hsin-Lun Hsieh, Jing-Ya Shang Guan, Hsin-Mei Wu,Te-Chuan Li, Yu-Yun Wu

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