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Food ordering made simpler & accessible


TinyOwl App

UX Designer
Research, UX, UI, Design System
Android, iOS

The problem

TinyOwl was an early-stage startup started in 2013 when food delivery was still a new challenge. By 2015, a lot of players had entered the market but nobody had cracked the hyperlocal real-time delivery model yet.

Add to it the complexity of choices and diversity of Indian food, and the design problem staring right back at us was - How do we design a food ordering app that helps users make a quick choice of what to order?

User Research

We did Field Studies to observe people at their moments of truth - food courts, canteens, social gatherings and fine dining restaurants to understand how people decide what to eat and how do they order.

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The common patterns and behaviours observed in the above study were - 

  • People don't like to spend too much time in deciding what to order 

  • People want their food to be ready and delivered as fast as possible 

  • People cared about food hygiene 

  • Most people like to experiment and try different cuisines once in a while; sometimes out  of social biases 

  • People have budgeting constraints -  those who regularly eat outside

User Personas

I organised a design session with the team of 8 other designers to discuss the above insights, to chalk out each of ours food ordering behaviours and preferences - based on these we prepared the following personas 

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Designing - Prototyping - Testing

I sat down with a Product Manager in our team to chalk out the roadmap based on the above insights - our main focus was to simplify users' decision-making process. We finalised on introducing -

  • Recommendations of restaurants or specific food based on users' platform behaviour, trending in their area and an occasional nudge to try something new or experimental.

  • Dishes were a curated collection of meal packs across cuisines picked specifically for Tinyowl by restaurant partners and in-house chefs.

  • Ratings & Reviews were a way of helping users decide based on social validation



I collaborated with 2 engineers to create an algorithm which indexed users past ordering patterns, food choices and searches - and tried to map it out across different cuisines, ingredients and cross-likes or dislikes. We experimented with this to allow our users to discover and explore better.

It was pretty rudimentary but effective as our conversions increased by 12%. That was a whopping jump in the number of users who were earlier dropping out because they couldn't decide on what to order.

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Dishes was a two-fold solution - allowing users to choose faster and curating healthier options. Unlike recommendations, Dishes was not about personalisation but about the right curation.

We offered not only restaurant food but also, homemade food prepared by our partner chefs and a lot of homemaker women who saw this as an opportunity to express their creativity.

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Ratings and Reviews

We recognised our post-order experience was not upto the mark - we needed to keep our users apprised on the order status at all times and also, give them a quick way to contact our customer support for any help.

And finally, get some relevant feedback from them about their experience, food quality & taste so that other users could use that information to make better decisions.

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Final Designs

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Interactive and playful feedback and rating system. Feedback collection increased by 30% after we rolled it out. (2).gif


OwlStrap was TinyOwl's Design System that we created to maintain consistency and faster implementation across the team. I created detailed guidelines for the fellow designers and collaborated with 2 front end engineers to get it developed.

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