Feastopolis

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A Royal College of Art Cap Stone Project

Type

Service design

Start-up Plan

Digital Platform

Data collection policy

Duration

Team

24 weeks

1 person

Area of work

Interview Sessions

User research

Rapid Prototyping

Service Blueprint

Touch point design

Challenge:

There is a fear about data collection

Service designs need data to learn about the people sitting on the other side of the service, and make the customise experience for them. However, data collection is not easy. To a certain extent, people are afraid of data collection. The fear is not unsound but actually reasonable. There are bad examples of how data is being handled, things such as phishing, scamming, or Cambridge Analytica. A lot of terrible stuff tells us about how vulnerable we can be if we don't safeguard our data.

Index:

Research Questions 1: Why is there a fear about data collection?

Research Questions 2: What is the cause for the data collection disengagement?

Design Solution: How might we promote a "data-driven lifestyle", closely related to good information through food consumptions.

The Approach:
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Research Questions 1: Why is there a fear about data collection?

Why is there a fear about data collection? Is it because people feel that their data is not being protected? 

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Insights: Needs for Digital Me

The need for security plays a crucial part of a successful data collection policy.  The human’s need in the digital era is often neglected as most of the ideas of data governance are hard to adapt for small businesses and sometimes conflictual with big business’s interests. Ultimately, the lack of security in data collection costs money and customer’s trust.

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Digital Native Persona

The need for security plays a crucial part of a successful data collection policy.  The human’s need in the digital era is often neglected as most of the ideas of data governance are hard to adapt for small businesses and sometimes conflictual with big business’s interests. Ultimately, the lack of security in data collection costs money and customer’s trust.

Hypothesis 1: Absolute data security, Transparent and detailed data usage information, is the key to help data collection

The first hypothesis is drawn under the impression that the insecurity of how people's data circulates is the primary cause of the distrust about data. My goal was to test the idea of how a data collection model with transparency at the centre of the design would work.

Prototype 1: Testing the relation between data security and user engagement with the service

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A fully digital platform: Prototyping in a COVID Time

At the height of the second wave of COVID-19 in the UK, a need to continue the research in a digital way is obvious and urgent. The solution is an entirely digital platform distributed to the audience. It is a platform that allows people to upload their daily meals and show them how they are progressing through the data collection before a complete report of their diet pattern would appear. Community factor is added to the platform to incentify people to put in the logs.

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Experiment in Theory

In the prototyping 1 process, when any piece of information gets recorded, the impact is shown right away. In other words, if there is no impact shown to the users, the data will be locked from any use. 

In the prototyping 1 process, when any piece of information gets recorded, the impact is shown right away. In other words, if there is no impact shown to the users, the data will be locked from any use. 

Outcome

The platform did not successfully engage the majority of the prototype participants. From the 50 participants who downloaded and used the app, around 6 are actively engaged with the platform. Most of the participants are engaged because of their interests in cooking and the ties they have within the community.

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Research Questions 2: What is the cause for the data collection disengagement?

Why is there a fear about data collection? Is it because people feel that their data is not being protected? 

 

Insights

Several research buddies pointed out the similarity between quantify-self movement and the prototype 1 digital experiment. The similarity can help pinpointing the reasons for the disengagement from the experiment. 

Critics pointed out that it is hard to make sure the piece of information collected from quantified-self movement is relevant to the specific person using those tracking devices.
When talking about footsteps on the Fitbit we have collected, it is hard to tell the difference between 10000 steps or 8000 steps for the users. 

In order to make sense of the information collected from the quantified-self movement, the users must spend efforts to make the information collected relatable. Of all self-trackers who closely track themselves, the motivation to do tracking is mostly from the subject they are interested in. 

Hypothesis 2:  Enjoyable experience  with a clear vision on the purpose of the information collected

The second hypothesis is an extension of hypothesis 1. The difference is that it stresses the purpose of the data collected even more and aims to create an enjoyable experience around it. 

Prototype 2: Testing how a good experience aid the flow of data

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How it Works

The second prototype is a Secret-Santa-ish Potluck. The event can only happen thanks to the ease of the lockdown. In this potluck, everyone will have to cook for someone else in secret. In return, someone else will be cooking for you. 

Outcome

The result of the potluck is astonishing. People were very engaged in creating the dishes that other people may like. The information about the other's taste flows in the group naturally. 

In this experiment, the participants know why someone else is collecting their information. Therefore, they know that they can expect a good experience.

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Image by Rachel Park

Capturing the Phenomenon

After interviewing the participants, most of the participants said that they love the event because of how it has that core idea of food sharing. What is even better is that someone else is cooking for the person so they can expect personalised content. Entire experience is transformed to a cultural-sharing gathering with the participants actively looking into other people’s diet preferences. They all stated that they do not feel any insecurity about other people gathering their data. They are happy to allow the event being documented and published and they love to have a similar event again. 

 

My design will be the solution to scale and digitise this environment.

Essentially, the design will not be just a service but a promotion of a lifestyle with people's decisions influenced by information.

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The Problem Statement:

How might we promote a "data-driven lifestyle", closely related to good information through food consumptions.

 
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Start of the Service Funnel

Promoting a lifestyle is complicated. A funnel of service is needed to slowly help users adapt the style. The start of the funnel needs to be something easy, something you do a bit differently every day, something that brings you joy with a sense of exploration. The design turned to the concept of food and diet because of this. 

 

Food is something people have to have every day. People also love to discover new stuff while having food. You don't need to change people's behaviour massively like other quantified self movements do.

Service blueprint

A service blueprint was built in order to identify key elements/ artefacts that needs to be create. It also serves as a method to explain the service as a whole with touch point sequencing and backstage utilities.

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Restaurant
Stage 1: A sustainable business to start promoting the idea

At first, Feastopolis will be limited as it only launches to just a local restaurant providing algorithm-generated meals. This stage aims to introduce a good food experience to people, as stated previously in the service funnel. In this stage, initial funding and the revenue of the restaurant will be used to help promote and advocate the data-driven diet style for the local community Feastopolis is located.

Ordering

Though it's been said that the restaurant has no menu, it's not entirely menuless but instead a sheet for you to choose what you like and dislike. 

Customers can order food based on the ingredient they like, something from the past that they had with Feastopolis, or something completely random to enjoy the excitement of discovering new flavours.

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Control

To give the users a sense of control over what they like to eat, They can tweak small parts of the dish they have ordered to make the meal perfect for them. 

Flavour Palette

Flavour Palette, the visualised algorithm, will be accessible for users to see their diet journey in real-time. The palette will power the random dishes suggestions and give out recommendations for users which they can swipe. It is just like tinder, but here you are dating the dish. If you find something that sounds just about right. Customers can order the dish from this screen straight away too.

 

The aim is to let users understand their progress and make sense of how data circulates in the Feastopolis platform. The data collected can help you discover new food and learn about your diets and perhaps manage your wellness.

The flavour palette matches the previous hypothesis of an enjoyable experience associated with the clear vision on the purpose of the data collection.

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Seasonal Themes

There will be seasonal themes to help stock up mainstream ingredients such as burger meat or rice to make the logistics easier. This also encourages users to try out different ingredients and genres of food.

Stage 2: Bridging the vision and the business

When Feastopolis's user base starts to accommodate this data-driven diet style, Stage 2 of the service will start. The focus moves away from the restaurant and The Favour Palette will become the main product. In this stage, Feastopolis will try to focus on activities made possible thanks to the information collected from the customers. 

Platform for Food of Choices Emerging

The biggest change to this stage will be the platform opening up for other restaurants to join. This is to provide users a better experience with the flavour palette. The Feastopolis physical restaurants will still struggle to provide the wildest meal recommendation on their own. With other restaurants also contributing to the deck of dishes to be recommended, the flavour palette will be even more powerful. 
Feastopolis will take service charge from the collaborators to maintain the system. 

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Data Activities

Users at stage 2 will be able to participate in social interaction fueled by the data they have collected. If users want to cook for someone in your close family or friends, and they allow access to their Flavour Palette, the system will suggest a dish for that person. Of course, users can also have their own Secret Santa Potluck too!
A premium account will unlock more dish recommendations for other people and remove the service charge from the food order. This will engage the customers even more and retain customers.
This is to put the data users have in their own hand and allow them to play with it.

Stage 3: Promoting a Data-driven Lifestyle

When a loyal user base emerges, it's time to move on to stage 3.

Stage 3 is all about transforming Feastopolis into a data-sharing platform. The business model will revolve around data collection and administration fees as a data platform and public funding for future feature expansion

Data Diary

Besides enjoying the data activities and the dining experience, Feastopolis will open up another section to allow users to complete their self-monitoring diary while the outside collaborators help build ways to measure the impact of the data. 

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A platform for data

With the help from collaborators, the platform can make sure that there are tools to measure the meaning of the data. Even better, the existing data collected from Feastopolis can be directly linked and processed by outside collaborators.

Users will get information on how their data is changing the world, how they can make informed decisions about their everyday life and even potential monetary benefits awarded by collaborators.

 

At the same time, the outside collaborators get the data they need to innovate their service and a better way to engage and retain their customers.

Purpose of Artefact

With the help from collaborators, the platform can make sure that there are tools to measure the meaning of the data. Even better, the existing data collected from Feastopolis can be directly linked and processed by outside collaborators. Users will get information on how their data is changing the world, how they can make informed decisions about their everyday life and even potential monetary benefits awarded by collaborators.

 

At the same time, the outside collaborators get the data they need to innovate their service and a better way to engage and retain their customers.

Validation Interview

To validate the viability of the business model, interviews with restaurant owners were conducted. 

 

Besides feedback being generally positive, the restaurant owners pointed out similarity of how they interact with regular customers. The cooks of the smaller restaurants often customise the food for those customers they are familiar with. This has been a trick for the smaller businesses to retain their customer and develop their reputation in the community. They would gladly join the platform at Feastopolis’s second second stage if the service can help understand their regular customers better and acquire new customers through platform recommendation.

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How do you think about the project?

Lets talk about data, data-driven lifestyle and opportunities!

Current state of the project

The service system crafted around Flavour Palette is still under development and will be published on both iOS & Android platformin the coming November. Subscribe to the latest update of the project here.