DATA DRIVEN INNOVATION BROADCAST
UX
LIVE STREAMING
INTERACTION
GAMING COMMERCIAL
DATA BUSINESS MODEL
VIDEO
Introduction
Goal
This project, in collaboration with the companies ExMachina and Angry Bytes, focuses and investigates on new commercial opportunities using data collected from second screen apps, live streaming videos and a live stream of data in general. During the five months at the MediaLAB Amsterdam we could research and design real prototypes using the most useful Design Methods together with the SCREAM method.
The aim of the project is to develop one or more ‘live’ data based media concepts that can strengthen or even replace video. We had to discover which new interactions we can create during live programming (in a stadium or streaming) that creates new (commercially interesting) data on the viewers. #DATASS team chose to resize the research to live streaming for influencers and the interactions with their followers. We focus on the unique person and not in a marketing persona.
Process The research started with an identification of actors involved in the project through a Stakeholders Map and a general research about products and services that use data as part of their business model. In order to understand the user’s point of view of these kind of products and services we launched a form, answered by 100 users. We visualized the answers and used them as a guiding point in the rest of the research. The introductive part was completed by an analysis of new trends and tools regarding live streaming.
Liveness
stephanie
“It is not simply the simultaneity of event, transmission and viewing. Liveness is here regarded as a historically defined construct that hinges on the potential connection, through media, to events that matter to us as they unfold.” (Hammelburg)
22yo From Amsterdam Communication student Cooks as a hobby
“Dear Spotify, I love you for exploring new music „
“ Dear EasyJet, I hate your suggestions of holidaytrips. Especially after I already booked a trip. „
58,2%
33,6%
17,3%
16,4%
25,5%
TV fan
App and tech fan
Games fan
Sports fan
None
Personalization Personalization is achieved through costumer data and predictive technology. Measuring the effect of personalization is still a difficult task. This is because it’s unclear if the results are based on actual or perceived personalization.
Sense of belonging “The experience of personal involvement in a system or environment so that persons feel themselves to be an integral part of that system of environment” (Hagerty et al. 173)
ROFITS FROM D ATA RE P MO EX MACHINA
ANGRY BYTE DATASS
PERSONAS
STREAMER
MERCIAL PARTNER COM
SHIP NSOR SPO
LIVE STREAMING
T
DATA
PERSONALIZATION
S D
CH AR SE RE
DATA
ENTERTAIN MEN T
CONTEN
SURVEY
ACTIVE WATCHER
Companies have access to data about user's characteristics, behaviors, allowing them to constantly monitor, follow, judge, sort, rate, and rank people as they see fit. It's the future but also risky.
LIVENESS
LOVE LETTER
M ET H O
RM
DATA
PL ATFO FOLLOW ERS
Data awareness
VIP DECONSTR.
PASSIVE WATCHER
SENSE OF BELONGING
WATCHERS
After this first general research, we found four different topics that we thought could make the difference in the last prototype we wanted to deliver: group belonging, personalization, liveness and data awareness.
LOTUS BLOSSOM
Thanks to this research we found out that we had to focus on creating a better chat experience in online live streaming platforms, in order to make the watcher have a more qualitative interaction between them and make their dialogues more constructive. People want personalized services tailored on their needs. On the one hand they are not very aware of the usage of their personal data, on the other hand there is the risk to fall in a "filter bubble". Morover, personalization is often wrong, that's why through love/ breakup letters we could understand the feelings regarding these services.
DATA AWARENESS
OOPS is a live streaming platform focused on cooking that creates new interactions between the streamer and the viewer. It offers the possibility to connect with brands and create a new shopping experience.
VOICE RECOGNITION
CONTEXT FEATURES 2 1 3
MULTIPLE P.O.V.
HIGHLIGHTS
CO-HOSTING
CLICK-N-BUY
Our solution wants to make people get out of their filter bubbles and create a group belonging and personalized live streaming interaction. After defining the personas, with their feelings and roles regarding live streaming and several solutions for them, we filled the value proposition that we could offer through our solution. The final scenario is our solution for the streamer Stephanie: OOPS.
Use of data in existing products and services
Identification of stakeholders Trends Survey Twitch ext.
Ideation of a new chat experience
Sense of belonging
Communities creation process Chat references
Voice recognition
Multiple P.O.V.
Co-hosting
Click-n-buy
Highlights
Context
The watchers can interact with the live streamer through voice recognition, answer to the questions and polls proposed by the streamer, ask doubts or change the point of view of the camera.
Both the streamer and the watchers have the option of changing the point of view of the live stream. Thanks to the multiple points of view the watcher has a personalized live stream experience, but still realistic and live.
The platform includes a co-hosting feature that enables direct conversation between the streamer and the watcher, share the live streaming screen with her/ him being for a moment the main character of the video.
This feature enables a new online shopping experience though live streaming that gives to the watcher the possibility to buy the products shown in the screen through a simple click on the screen.
In order to create a better video experience for the watchers joining the video not live the platform creates automatically highlights of the main events happening in the video. These highlights can be created by the streamer as well.
The platform captures the context in which the watcher is joining the live streaming. In order to enable group belonging and create the possibility to have a sharing experience during the live stream.The context feature helps the watchers creating group profiles.
Netiquette
Personalization
Conclusions
Trends in personalization User feelings with love/breakup letters Filter bubble concept Open the mind Ideation of live stream content aggregator for news
Personas
User identification
Different scenarios Three value propositions
Made by
In collaboration with
For more info
S
A big thanks to The two companies that helped and followed us through these five months: - ExMachina from Amsterdam - Angry Bytes from Hilversum Our two coaches: - Wouter for the expertise - Evelien for the guidance and trust.
Prototype
AS
Intermediate step
AT
Main research
The platform is applicable to other areas other than cooking, such as online courses, entertaining, or tutorials. With these new features designed in OOPS the user can get a more involved and engaging entertaining or educational experience and new data is generated. Thanks to this, commercial partners working with this platform can collect this new data to be able to offer a better experience to their customers as well.
#D
OOPS
As a solution, we build a new platform for live streaming that enables new interactions between streamers and viewers. We named our platform OOPS, based on the unpredictability of live streaming. We tried to come up with new features that work together to create a more personalized experience for the user, while at the same time creating a bigger sense of belonging and a commercial interest for brands.
January 2018
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Lorenzo Positano lorenzo.siena@hotmail.it
Maylis Mulderij maylismulderij@hotmail.com
Nerea Zabalo nereazabalooyarbide@gmail.com
Rotem Mark rotem.e.mark@gmail.com