The Good, Bad and Ugly of Food Tech: TinyOwl Case study

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“Out of clutter find simplicity, from discord find harmony, in the middle of difficulty lies opportunity” — Einstein

  1. Realisation

Somewhere in the middle of October food tech industry realised the gaping holes in the value proposition, it was clear that large players were losing customers to new entrants and whilst new entrants had customers they couldn’t retain them. There needed to be a shift from mere growth to how do you achieve frequency and defensibility with the customers acquired.

Also, we early on knew that price and quick delivery would self-adjust itself as we were already delivering 45mins per delivery for orders delivered by TinyOwl with unit cash positive as against the burn of our competitors and Average Ticket Size was more or less consistent for a large order number. So the long-term value built on these tenets would fade away soon.

2. Insight

At TinyOwl we had always maintained a breakout team — designers, product and data scientists which worked on building solutions to simplify the journey of the user and find more meaningful value. it became central now to our functioning.

We realised that consumers who order food online are pressed much harder for time and more unclear of their decisions on what to have than restaurant finding audience. It was the urgency and perpetual nature of the transaction which required one to make the transaction a no-brainer for the audience.

3. Simplify

To make it simple it required us to first understand the audience preferences — their anxieties and motivations towards the behaviour which led them to order what they did. Thereby establishing a leaner funnel for our consumer to enter and exit the platform with least resistance and necessary aids to help them choose better for themselves.

To understand consumer taste preferences and the journey we created a Taxonomy — a spider algorithm which indexed users past ordering patterns and journeys to allow them to a result and checkout in the fastest and most seamless way.

This is getting smarter with each passing day and allows our users to not only have the regular foods but discover what they love easily.

4. Discovery

When we deep dived into our consumer journey we realised that mere answers do not help. Why would the consumer buy what we suggested? And if they didn’t trust us they would go back to what they usually order which doesn’t deliver him any value to stick on our platform.

What was even more dangerous is that frequent repeatability in food often creates a mind block and users drop of the platform to seldom come back. We saw that with same menu restaurant chains which saw a massive drop and that would be devastating. Making users explore on our platform became fundamental to long-term survival.

5. Trust at the core

“There is a deficit in trust today. from politicians to academic institutions to religious places trust is at an all time low; but what’s important to note is that what people still trust in each other, they always have.” — David Plouffe, Campaign Manager Obama (2008 & 2012)

Trust in food is also at an all-time low. Food in India is always divided into ghar ka khana & bahar ka khana with the latter being the forbidden fruit. This largely stems from lack of transparency of whats going in the food, industry bad practices of artificial and cheap ingredients to allow for lower costs yet better margins; and most importantly the lack of trust between people who are serving with the ones which are eating.

This was the same reason for repeatability or going back to what they know- which augurs from the comfort of places and brands familiar.

To establish trust our first aim was to build relatability and make transparency and information possible yet not overload the user.

6. Validation Layers

This required us to create anchors and data to validate those anchors and funnel users choices. We learnt menu designs and how we could drive discovery yet maintain a consistent pattern which users recognised. We found that the best menus were divided into four major portions 1.Regular Eats 2.Specials 3.Seasonals and 4.Variable Rewards (small bites which allowed for impulse purchases)

Whilst the above aided with taxonomy allowed to target users on what they should order, we also were aware that one answer wouldn’t be enough. In our buying patterns, we usually shortlist a few before finalising on what to order. So under these decision anchors, we create food and mood types which allowed users to choose from a shortlist.

At the time of the transaction, we converted food from a commodity to a relatable story through reviews, ratings and information on what goes into it and why it exists on the shortlist.

Magical Fridge is solving the problem of identifying food as per taste and then develop trust, whats important is that it’s not each exclusive problem on its own but what Magical fridge is a coherent intentional journey wherein the whole is larger than the sum of its parts.

“Out of clutter find simplicity, from discord find harmony, in the middle of difficulty lies opportunity” — Einstein

This article was originally published here by Hozefa Ayyajiwala.

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