In a world with an abundance of food choices, individuals frequently struggle to make decisions about where to eat, resulting in decision paralysis and inefficiency in the dining selection process.
A very relatable meme of the struggles of deciding where to eat.
Hence, Diiinder is born. It aims to help individuals overcome decision paralysis and efficiently choose where to eat amidst a multitude of food choices through gamification, advanced algorithms and user preferences to provide personalised restaurant recommendations.
Our target audience are Millennials and Gen Z individuals, aged between 18-35. Even though food recommendation apps can be, and are popular across different age groups, I decided to target this specific age range as they tend to be more active users, and are often early adopters of technology, resulting in them being more inclined to use food recommendation apps to explore new dining options. Further, food recommendation apps primarily attract those who are comfortable with technology and smartphone usage, seeking new digital solutions for daily tasks and leveraging the convenience through these offerings.
Even though food recommendation apps attract food enthusiasts and adventurous eaters who have a passion for food and enjoy exploring diverse culinary experiences, I do not want to limit the target audience to food enthusiasts and adventurous eaters between ages 18 to 35. Despite these users being likely to be open to trying different cuisines, and sharing their food adventures with others, Diiinder aims to help users efficiently locate food choices based on preferences, and provide a platform to help decide, especially with friends.
The actual user base of food recommendation apps in Singapore can be diverse and evolving. To gain a more precise understanding of the demographics of Diiinder's users, conducting user surveys, analyzing app usage data, and monitoring user feedback would provide valuable insights.
Other Potential Targets
With the development of the application, I also aim to reach out to restaurants that are on Google Maps, but do not participate in food recommendation applications, as well as the general masses who are currently underutilising current food recommendation offerings.
There are a couple of food recommendation apps that Singaporeans use:
These are just a few examples of the competitive landscape in Singapore's food recommendation app market. To stay competitive in this market, it is essential to offer unique features such as personalized recommendations, intelligent search filters, real-time availability updates, seamless reservation systems, and social engagement functionalities.
Additionally, keeping up with the latest food trends, partnering with popular influencers or food bloggers, and fostering a strong community of users who actively contribute reviews and recommendations can help differentiate a food recommendation app and attract a larger user base.
Regular updates, improving user experience, and leveraging data analytics to gain insights into user behavior and preferences will be crucial for staying competitive in the dynamic and evolving food recommendation app market in Singapore.
Key Takeaways from Analysis