Concept – DDI

By nereazabalo January 25th, 2018

Sprint 5 // Elaborating the final concept

The challenge for the fifth sprint of the process was to start elaborating the final concept of a cross-platform service, a new way of interaction for live streaming enabling personalization and group belonging, integrating the conclusions and ideas of the last sprints and deliver a business model and data model.

// Our users

As explained on the last post, after researching and observing different users’ profiles and behaviors, we created three different personas and designed three different solutions for each one of them.

As wrote in the last post, regarding Stephanie’s profile (foodie live streamer) and her pains and gains, we designed an easy way for the streamer to interact with her followers enabling group belonging in the audience. Making for her easier to get feedback from the watchers to improve her live streams and performance and the watcher can interact with the streamer through clicks, voice recognition, which makes the streamer work more efficiently. The solution enabled the streamer also to have smaller groups of watchers depending on the active role and engagement with the content.
In the case of Clark, we created a transparent and collaborative system for consuming and broadcasting news. In this concept, anonymous individuals can live stream their own content about news from their point of view. From the other side, Clark, as a journalist, creates his own online magazine (board) with the live streaming content of the anonymous people, with the help of a filtering algorithm.
And as a solution for Julia we developed a new way of interacting with an entertaining video content for the family in one screen/device enabling the participants to interact with the content (TV Show) through voice or face recognition and strengthening family bonding thanks to a shared profile.

Once presented the three different fields, users and solutions to our partner, we decided to focus on the first idea, the live streaming platform and interactions for streamer and watchers in a cooking context. Even though the first idea was chosen, we tried to get the most interesting features of the other two ideas and implement on the main one.

// Research

The research phase of the fifth sprint was based on analyzing live streaming platforms as well as observing different tools that could be used on the last concept proposed.

On the following graphic we visualized the different live stream platforms analyzed regarding affordability and use:

// Final concept

As a final concept we ideated a platform to enable an easier and more effective live streaming experience for live streamers and more interesting interactions for watchers.

From one side, the platform had some primary features as the possibility of changing the point of view of the live streaming video; buying objects from the live stream video by clinking on the screen through object recognition; hosting watchers in the live streaming giving them the opportunity to co-stream together with the live streamer; voice recognition in order to enable conversations and asking questions during the live stream.

From the other side, the platform offers some secondary features such as the possibility of having highlights and quick recap when the video is not live any more and the possibility of offering a different context depending on the context of the watchers while watching the live stream.

Together with the final concept we started to work in the data model behind the idea, in order to understand the kind of data that can be collected, the sources of data, and the key activities in order to get different outcomes and trends as a result.
In this data model, we started thinking on potential partnership that could be created with retail companies due to the interest they have on knowing what happens once their customers are consuming or cooking their products at home.

By nereazabalo December 11th, 2017

Sprint 4 // New interactions for Stephanie, Clark and Julia

Our challenge for the forth sprint was to create three concepts of a cross-platform service with an integration of past sprints conclusions and ideas. We are going toward the final delivery, that’s why we included a user research, an ideation of data streams in the solutions and liveness as a concept.

// Process

In this case, the time invested on research has been less than the time invested on ideation and creation.

// Research

Regarding the research part, the focus was in making a user analysis. Having a look at generations, with special focus on Millennials and GenZ people, we defined 10 detailed personas, inspired by people around us, influencers and some Instagram and Twitter accounts.

After defining the 10 personas…

… we started with the clustering process, so we choose 6 and through a dot voting tool, we came out with the final 3 personas: Stephanie, Clark and Julia.

“I don’t share my life. I share the things I eat, the places where I eat and fragments of where I am and what I do”

Stephanie is a 22 year old young Dutch single woman who lives in Amsterdam and studies Communication. She likes to have an ideal lifestyle. She is very into healthy lifestyle, she loves working out, socialize with old and new friends and feel beautiful, healthy and successful. One of her passions is cooking.Her favorite thing as a foodie is to discover new bio products to include in her recipes, she is specialized in making brunches and meals that look very healthy and nice. She has a blog for her recipes, and she uses Instagram for posting beautiful pictures (almost professional) of the dishes she makes. She is becoming more and more famous on Instagram and she is getting more and more followers. She likes it, but she is afraid of being stalked by people too much. She lives with two more girls in an apartment that they rented as students, all of them have the same interest in healthy lifestyle, so they can share the matcha tee and the chia seeds. On the week ends, she works in a fancy coffee place. She feels very happy to work there and earn some money to pay the rent and save for her master degree

“Media should in some way educate the people and not just vocalize what people are saying”

Clark is a 28 year old German male who lives in Berlin and is freshly married with a guy. Clark is a freelance photo journalist who started as a reporter for a broadcast out of his own country, that’s why he lived abroad for many years and knows many languages. He then switched to freelancing, and he writes his articles on Medium and share them on Twitter. He also takes pictures and videos and uses Tumbler and Instagram to promote himself. He works mainly with local news, be he sometimes enjoy writing about tech. Since he became a freelance, he rented and apartment with his husband in Berlin, but he keeps traveling for work. He is a media and tech enthusiast. At the same time he doesn’t trust the web a lot: there are too many fake news! He is curious, runs a lot and he is an activist for people rights and for the environment. He believes in open source and sharing economy, but thinks not everyone can understand it and use it.

“I’m trying to understand my kids’ deigital world, but sometimes is too hard. I just want some family time while watching TV”

Julia is a 46 year old proud and happy Italian housewife who lives in Naples with her family (two children and husband). She loves reading books and spends her time giving back to the community , volunteering in the parent association, baking sales and collecting (and mending) clothes to give to the people who need them. When not volunteering or reading (in the evenings), she watches prime time tv shows with her family, like “The voice”. She even likes to use the app to vote! Usually her children (Emma 12, Niccolo 10) laugh at her when she get’s emotional while watching, but she loves having scheduled time with them during the week, even her busy Husband (Matteo, 41) joins the fun!

// Ideation

After having the personas defined and a clear focus on Stephanie, Clark and Julia, we made an empathy map of each of them in order to be able to analyze them in a more emotional way defining what do their hear, do, think and say about live streaming and which are their pains and gains regarding it. The empathy map helped to understand even better Stephanie’s. Clark’s and Julia’s points of view and roles, making easier and more effective the process of designing a solution for them.

In order to start creating solution for our three personas, we used the Lotus Blossom tool as an ideas generator and we came out with three different concepts (each one of them for each one of the personas) and we try to define them with a clear challenge a concept, a why, a concrete user and value proposition, a data model and a business model.

// Building

With the idea of making the three concepts more tangible or easy to understand, in the “building phase” we created three different Storyboards that represent the solution for each of the personas.

Taking into account Stephanie’s profile and her pains and gains regarding live streaming, we designed an easy way for the streamer to interact with her followers enabling group belonging in the audience. With this concept, the streamer gets feedback from the watchers to improve their live streams and performance and the watcher can interact with the streamer through clicks, voice recognition, which makes the streamer work more efficiently. The solution enables the streamer also to have smaller groups of watchers depending on the active role and engagement with the content.

In the case of Clark, we created a transparent and collaborative system for consuming and broadcasting news. In this concept, anonymous individuals can live stream their own content about news from their point of view. From the other side, Clark, as a journalist, creates his own online magazine (board) with the live streaming content of the anonymous people, with the help of a filtering algorithm.

As a solution for Julia we developed a new way of interacting with an entertaining video content for the family in one screen/device enabling the participants to interact with the content (TV Show) through voice or face recognition and strengthening family bonding thanks to a shared profile.

The aim of creating three different solutions in the fourth sprint was to get the feedback and choice from the partners regarding the concept and have a guide regarding the following sprints and the final delivery.

By nereazabalo December 11th, 2017

Sprint 3 // On Board

For this third sprint our goal was to create a physical prototype (or a paper/ digital) of a cross-platform service with a focus on personalization and data awareness, following the assumption that people are not aware of what kind of data they are sharing and they are not happy with a personalized online services and platforms.

// Process

We started the sprint with a research focused on personalization, data trends and filter bubbles and applying the insights from the research to a live streaming context we came out with an interesting prototype as a delivery for the sprint.

// Research

We took David Weinberger’s definition for personalization from 1999 as a starting point for our research, according to him, “Personalization is the automatic tailoring of sites and messages to the individuals viewing them so that we can feel that somewhere there’s a piece of software that loves us for who we are”.

Taking this as s reference we found three different levels of personalization:

Highly personalized: addressed to a particular individual.
Moderate personalized: based on common characteristics of a population subgroup.
Non-personalized: no specific target.

As a source for the research on personalization and data awareness we took the survey we did in the first sprint in which we took insights and interesting quotes from the participants, our objetive of taking again the survey was to make a deeper research on when people feel scared and annoyed when a platform recognizes their data and makes suggestions for them and when they feel surprised and curious about it.

“I would not mind personalization if the results are truly good. However, most times the suggestions are pretty terrible”.

“Booking needs to know where I would like to go and not where I where I have been. It’s annoying to get offers in places you’ve already been”.

In order to have more qualitative answers about personalization and data awareness and get better people’s emotions, we decided to invite people to write a love or a break up letter of a time when a service gave them personal feedback or suggestion that they found useful or invasive. We got 26 interesting letters in 24 hours.

After having the references of existing definitions for personalization and after receiving the perspective of the people about personalized online services, we ideated our own definitions of personalization:

General definition // Using information from the user to offer a tailored experience on the single user.
Consumer’s perspective // The fact of services using consumer’s personal data in order to receive new relevant inputs that expand consumer’s knowledge.
Streamer’s point of view // A method to get information about the user and tools from the platform in order to reach more people, have a human and user centered approach to develop long lasting connections with the user.
For the platform // Understanding user’s needs in order to have good analytics for an agile and efficient platform and offer a value proposition for the streamer and the user.
For us // Offering an open minding tailored experience on the single user to avoid filter bubbles.

As a final part of the research of this sprint we had a look at some interesting concepts related to data awareness as the “Internet of Things”, “Machine Learning” and Samrt Cities” and to the concept of “Filter Bubbles” as a data trend, in order to get some inspiration for the ideation part of the sprint.

We looked at Eli Pariser, who was the creator of the concept of the filter bubble and who defines the concept as a “personal ecosystem of information that’s been catered by algorithms” and that makes people get information on the Internet that feeds that ideology, profile or point of view.

In order to understand the existing solutions for avoiding the filter bubbles, we found the following four cases:

escapeyourbubble.com
Online service that works mainly with Facebook and that understands which is the point of view that the user needs to understand better, in order to provide him/her with curated positive posts to help him/her understand a accept different points of view.
allsides.com
In order to avoid Fake News, allsides is a website that provides multiple angles on the same story in order to help et the full picture.
hifromtheotherside.com
It’s a website that connects people with different ideological thoughts in order to promote “healthy “discussions about politics.
readacrosstheaisle.com
It’s an app that notice the user, when he/she is a little too comfortable in his/her filter bubble and remains hims/her to go to see what other people is reading.

// Ideation

For the creative part of the sprint we combined personalization on online services with data trends connected to personalization and live streaming in news, games and sports.

In order to start ideating solution we first focused on identifying the real problem, that’s why we used the Lotus Blossom tool in order to redefine the problem generating a big amount of ideas. The main problem that we asume was that “personalization is more difficult during the live streaming” and with the Lotus Blossom we came out with eight deeper problems that came from the main one, all of them divided into more technical problems or behavior problems. From them, we choose four ideas that we wanted to implement in the solution:

Using real time data to affect the streaming and the content itself.
Have a shared indexing of content.
Make people go out form their own bubbles of information.
Personalized the platform and the content.

After having the idea of which were the ingredients that we wanted to implement in our solution, we made a brainstorming session using the “superheroes tool” answering to the following question: “How can we design a service/product to make people go out from their filter bubble of information?”. And we came out with an idea that mixes news, opening user’s mind and switching of pants of views, also called “On Board”.

// Prototype

On Board is a metaphor of an online personalized platform that helps the user consume news in an open minded way, reaching different points of views of the same issue and helping her/him to go out from the filter bubble. The platform is composed by a decision making system and a personalized board, it’s designed as an app for tablet for the filtering system, as a computer platform for personal use of the board, as interactive mirror layer for a family or group context and as a wall screen for a working environment.

By lorenzopositano November 14th, 2017

Sprint 2 // The paper prototype

// Introduction

For this second sprint our goal was to

“Create a paper or digital prototype of a new service (or update Ex-Machina or Angrybyte platforms) with a focus on group belonging. We are doing this to learn and improve a deeper sense of belonging in people’s online live”

// Process

After a deep research about group belonging we decided to work on a new chat for Twitch. We noticed these problems:

Big flow of comments, you can’t read each comment;
People vomit random comments without coherency;
Not easy to interact with other users.

The solutions:

We want to go from fitting to valued involvement;
We want to slow down the flow of comments;
We want to establish connections between watchers.

VIP deconstruction Design Method

References analysis

There are many platforms that uses chat, groups and particular features. We analyzed Facebook, WhatsApp, Twitch and Youtube and we used the design method “ViP deconstruct” (Vision in Product Design) to understand a product/service as much as possible. As an output we got a list of principles, states, developments and trends.  A series of contexts that are contained in stories.

WhatsApp features



YouTube Features

Liveness and Group Belonging

From here we wanted to correlate our group belonging hypothesis together with liveness theory, to give more strength to our concept.

Simultaneity	Immediacy
The group is created while the live streaming is running.	The interesting topics appear in the right moment, immediately, the group is created.
Authenticity	Unpredictability
The groups are organic, and are created just in the moment.	The groups depend on what is happening during the game

// Findings

The new chat will be an extension that the user and streamer of Twitch can add to the normal streaming. We have these goals for the chat:

More hierarchy of the information
Tags and visual cloud to understand the main topics (useful for the user that just landed on the video and for the streamers that understands the feelings viewers have or what they want)
Possibility to filter the comments by the tags
Possibility to create sub-chats (or rooms, groups), about that topic

The new chat will have this architecture:

Chat architecture

Comment improvement
1.1 There is this big flow of comments, as any chat with many users
1.2 To start creating some hierarchy, we added the possibility to answer to single comments
Creation of the tag-cloud
2.1 ORGANIC: the algorithm highlights the most frequent words (except for stop words)
2.2 ORGANIC: the algorithm creates a tag cloud on top of the video with the most frequent word, with different sizes depending on their frequency
2.3 MECHANIC: the user transforms a word in the comment in a tag
2.4 MECHANIC: the new tag goes in the tag cloud automatically
Interaction with the cloud
3.1 you can go on mouse over
3.2 you can see the frequency of the words
3.3 on click two buttons appear to let you open a new chat or filter the comments by that tag
Grouping features
4.1 by clicking on the filter button, you can see all the comments with that tag
4.2 you can answer to those filtered comments
4.3 by clicking on the group button, you create (or join) a new room/sub-chat that talks about that tag/topic
4.4 you can see some data from other users, common interests
4.5 you can video call among the users in that chat

Paper prototyping

For the final prototype, we used the Design Method “Paper Prototyping” to determine the aspects that will be tested, such as content, form, structure, ‘tone’, key functionality, etc.

Rotem drew all the features in a wireframe and we scanned them to collect the prototype in an Invasion mockup. You can play with the prototype here or watch the video down here.

By rotemmark September 13th, 2017

Dat Maker’s Sprint

Written research is all fun and games, but a tangible research is a whole different level.

While researching data and data driven innovation, we found our selves swamped with topics, and like the data field it self, the topics were all connected but varied. As you can probably understand, confusion may arise.

At a makes sprint in media lab, if you are not familiar with, you have 2 rooms packed with technological instruments for, well, creating and making, like laser cuts, 3d printers, and hand machinery. As you might imagine, our team got some tingling feelings in our fingers, wishes to use it all, forgetting about products for a minute.

But the process s important, in order to make not only a good use of the machines but a right use for our project as well.

We have started with brainstorming, what it meant for us is writing on post it notes what ever crossed our minds, each o us on their own, or together as a team. we found it is really important to let each other have their own time to write their ideas, that way w keep our mind open and our brain creative, which is futile at this stage of work.

After clustering it all out, we started thinking, and connecting the thoughts together, mixing them from different fields, to find interesting new ideas.

A tip from the #DATASS: keep the conversation flowing, as this stage is where the magic starts to spark!

Writing our ideas out took time but we made it! Making a plan to execute it for the next two days of the sprint and getting the materials. (thanks, Pavel!)

Our idea was making people understand the concept of data as a currency as well as for as to learn about what types of information people are willing to give and analyze it to a visual end product. It meant we had 3 part of our project:

1// Data exchange (or how we like to call it; Data Casino): where you answer our questions, and you get coins in return.

2// aMAZEing game (a pun was intended): a maze you use your coins in to get ahead in.

3// Data viz: a glass display of the answers’ data from the exchange questions.

We have divided the work load between us and ready, set, SPRINT…

Nerea and Maylis wrote the very invasive questions.

(Oh, and they were having fun doing it.)

Rotem was making the maze plans and the coins.

(Maze planning is a migraine in the making.)

Lorenzo was building the Data viz and the Data Casino

(Glue is known as the best way to get high in Amsterdam)

We really loved letting people try out our game, we also learned a lot from watching people play, and we are going to keep working to improve the game and get better information from the gamers.

Until next time,

#DATASS

DAT Rotem

CREATING TOMORROW

DDI

Data Driven Innovation project (aka #DATASS)

Team

Commissioner: Ex Machina and AngryBytes

Description

Sprint 5 // Elaborating the final concept

// Our users

// Research

// Final concept

Sprint 4 // New interactions for Stephanie, Clark and Julia

// Process

// Research

// Ideation

// Building

Sprint 3 // On Board

// Process

// Research

// Ideation

// Prototype

Sprint 2 // The paper prototype

// Introduction

// Process

References analysis

Liveness and Group Belonging

Simultaneity

Immediacy

Authenticity

Unpredictability

// Findings

Paper prototyping

Dat Maker’s Sprint

A tip from the #DATASS: keep the conversation flowing, as this stage is where the magic starts to spark!

Nerea and Maylis wrote the very invasive questions.

(Oh, and they were having fun doing it.)

Rotem was making the maze plans and the coins.

(Maze planning is a migraine in the making.)

Lorenzo was building the Data viz and the Data Casino

(Glue is known as the best way to get high in Amsterdam)