Data Driven Innovation: Interaction and data in livestreaming Maylis Mulderij University of Amsterdam
Lorenzo Positano Politecnico di Milano
maylismulderij@hotmail.com
Lorenzo.siena@hotmail.it
Rotem Mark Nerea Zabalo Entrepreneurial leadership and Art director Rotememark@gmail.com innovation nereazabalooyarbide@gmail.com
Abstract The growing attention for live content makes it an interesting field and leads to new opportunities for the collection of data. The problem we had to solve is: How can we create new interactions with new data opportunities in livestreaming in different fields? To answer this question, we started looking at three different aspects of live content: liveness, sense of belonging and personalization. After this we came up with our prototype called OOPS, a platform for educating through livestreaming, momentarily based on cooking classes. Understanding data as the most important resource, the company should be able to know which is the data generated through their products or services, which is the source of the data generated and which are the key activities in order to transform the data into valuable inputs both for the end users, for the company itself and for commercial partners they could have.
Author Keywords Livestreams; liveness; personalization; data; group belonging; interactivity. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. Copyright is held by the owner/author(s).
ACM Classification Keywords H.5 Human-computer interaction
Introduction The first live television broadcast in the Netherlands took place on October 2nd 1951.Today, live is still seen as the ontological characteristic of the television, and also the radio. But with the changing media landscape, also new media seem to be more and more interested Facebook and YouTube, make watching and creating live content more accessible and easy for users.
Figure 1. The Golden Circle
The growing attention for live content makes it an interesting field and leads to new opportunities for the collection of data. The problem we had to solve is: How can we create new interactions with new data opportunities in livestreaming in different fields? To answer this question, we started looking at three different aspects of live content: liveness, sense of belonging and personalization. After this we came up with our prototype called OOPS, a platform for educating through livestreaming, momentarily based on cooking classes. With OOPS, we tried to create new interactive possibilities in livestreaming between the streamer and the user. With these new possibilities and features for interaction we would like to propose a more efficient way for companies to collect and use data as part of their business model. This research and prototype is entirely based on our ‘why’. Creating a ‘why’ is part of the Golden Circletheory, created by Simon Sinek [figure 1]. He argues that by creating a ‘why’ for your product, you create a more valuable relationship with your costumers [11]. Our ‘why’ is the following: It’s you. This explains our motivation for creating this prototype. We believe that we, as human beings, want to feel special and in this uniqueness, belong to a group with a similar interests and lifestyle. We want to connect with the others
through our stories. With our focus on sense of belonging and personalization, we tried to find out what it exactly is that makes people feel special and unique.
The uniqueness of live Liveness is a concept with a history. The increase in popularity of television and the radio, new technologies made it possible to record content, so not only live but also non-live content could be shown to the public. The live aspect of television and radio was no longer about showing what’s happening at the moment, but about the possibility that the flow of life could be interrupted by a live event any moment. This phenomenal is often referred to as liveness [8]. Jane Feuer sees four notions in live content. The first one is the notion of simultaneity: it’s happening while you are watching. The second one is the notion of immediacy: instant information while the event unfolds. The third one is the notion of authenticity: it is real (un-edited). The fourth one is the notion of unpredictability: anything can happen [6]. Because the new technologies also made it possible to put a certain delay in the live content on television, to avoid the uncertainty. Live is not really live anymore, and content can be recorded and watched again. In this way ‘live’ television is missing out of the four notions, seen by Feuer [6]. The authenticity is gone, because television is edited and can be re-watched. There’s also less unpredictability. Live streams bring us back to the original meaning of live. You have to be there in the moment to experience it, therefore they are unique and unpredictable.
Sense of belonging In 1991, Daniel Dayan and Elihu Katz introduced the so-called media events [3]. These events are always live and interrupt the daily flow of live. An example of a media event can be a coronation, or a big sport event like the Olympics. One of the key aspects of these events is the ability to connect people with each other, to create a group belonging between the people who are watching. Group belonging is strongly connected to liveness. Affinity, one of the key aspects of liveness, is the possibility for people to connect and participate during a live event [4]. For a better understanding of the sense of belonging we took a look at the definition of sense of belonging of Bonnie M.K Hagerty et al. They did research on the sense of belonging in a psychiatric nursing practice. Sense of belonging can be described as 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” [5]. In this description, a system can be a relationship or an organization, and an environment can be natural or cultural [4]. Within this description of sense of belonging there are two dimensions to be found. The first one is the fitting sense of belonging. This dimension takes place when one feels his characteristics work well and fit the system or environment. This could be in geographical terms, gender or age group. The second dimension is called the valued involvement. This dimension takes place when one feels being needed, accepted and valued. One needs to feel acknowledged by the group, for what he brings to the table and who they are [4]. There are several factors that can affect the sense of belonging. The amount of energy one is willing to put in
the involvement is for example an important element. Other factors like the potentiality for something meaningful or the potentiality for shared characteristics are also elements that can affect the experience [5]. The digital world is playing a bigger role in creating a sense of belonging. New social media platforms give users a place to express themselves, to share memories and emotions [7]. Even when those users are not in the same place or don’t even know each other. This phenomenal has a strong connection to what Couldry calls group liveness. With this form of liveness, he describes the possibility to be constantly in touch with each other. Through this we can, although we are not at the same place in real life, be in a shared place [10]. This is why, according to Couldry, liveness won’t disappear as a feature of new media, as this is exactly the role new media play in the current media landscape [10]. With our research on sense of belonging we found out that the way we connect with each other changed because of new technologies. We can interact with each other when and where we want. People have to feel needed and accepted to belong.
Personalization In 1999, David Weinberger, a technologist and coauthor of The Cluetrain Manifesto, wrote: “Personalization: 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.”
Figure 2. The Break up/Love Letter
The word personalization is something we increasingly encounter when using the internet, but it is also a concept that has many different meanings. There’s often a misunderstanding between customization and personalization, the difference lies in the involvement of the user. Personalization is achieved through costumer data and predictive technology while customization is when a user manually makes changes to achieve his preferred experience [2].
We gained our first insights through a survey. We asked people how they feel when incurring in new personalized experiences. From this survey, we got some new insights on personalization. The first thing that grabbed our attention was the answer that personalization is something that is expected nowadays on certain platforms and apps. Other respondents gave the answer that they like personalization, as long as the results are truly good.
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. It is possible for an individual to perceive a non-personalized message as personalized, or to perceive a personalized message as non-personalized. This can lead to a wrong outcome on the one hand. On the other hand, if a message contains no incorrect information (no matter whether it is actually personalized or not), it has a potential to be interpreted as personalized [2].
‘Booking needs to know where I would like to go and not where I have been. It’s annoying to get offers in places you’ve already been.’
Actual personalization occurs when a message sender intentionally modifies a generic message based on some previously collected data about a message recipient and delivers such a message to him or her, whereas perceived personalization is dependent on whether that particular message recipient perceives the message fitting into his or her preferences. Thus, the message sender is in control of actual personalization and the message recipient is in control of perceived personalization. This makes measuring the effect of personalization a difficult task. That’s why we used two research methods to get a better understanding of the way people perceive personalization [2].
‘I would not mind personalization if the results are truly good. However, most times the suggestions are pretty terrible.’ As you can also see in Figure 1. there’s a fine line between good and bad personalization. The second research method we used was the Break up/Love letter [9]. This method gives insight in the perception of the user by eliciting feelings based on real-life experiences and interactions through writing a love or break-up letter [9]. With this method, we build on the answers we got in the survey to get a better view on what people like or don’t like, love or hate in personalized services [Figure 2]. “Dear Netflix, I have a confession to make. I think I’m in love with you. […] You always show me new things and there hasn’t been a time that I didn’t like your suggestions. You have opened my eyes to a whole new world of things.”
“Dear Facebook I hate you for suggesting me marriage consultants after changing my status from married to nothing.” Another interesting Break up letter we got is the following: Dear YouTube, You used to be good. different and personal. […] At least, that was until a couple of months ago. It was just another night of checking out videos, but this time I did so with the children of my neighbours, which I take care of sometimes. With them, I started checking videos of animated rabbits. It was a fun, easy night but since then, every time I went on YouTube, the first suggestions that I got were always about animated rabbits. It was terrible. I wanted to check out some Italian disco tunes and what was my next suggestion? An animated rabbit. […] Thanks for everything, but unfortunately, we have to break up. Till another life. From this break up letter we found out that the context in which the user is watching, plays an important role for personalization. For good personalization, it is necessary to know for who you are personalizing at that moment. When comparing the answers from both research methods we can say that people want personalization but that it often goes wrong. Personalization only works when the right data is collected so the right suggestions can be given to the user.
Prototype: Creating OOPS Below we tried to combine our main insights of liveness, combined with group belonging and personalization into our final prototype. With this combination of research, we tried to come up with a solution for our research question: How can we create new interactions with new data opportunities in livestreaming in different fields? Through our research on liveness we discovered the value of live content in contrast to non-live content. Live is authentic, and more unpredictable. This makes it more exciting. With our research on sense of belonging we found out that the way we connect with each other changed because of new technologies. We can interact with each other when and where we want. People have to feel needed and accepted to belong. Within this group we want to feel special. Through our research on personalization we found out that personalization is only useful when the right recommendations are given. Having the right data and using this in the right way is key to offering good personalization. As a solution, we build a new platform for livestreaming that makes new interactions between streamers and viewers possible. We named our platform OOPS, based on the unpredictability of livestreaming. 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. We will now go through the features of our platform.
stream, for how long they are watching the stream and in which ways they interact.
Group belonging: Voice Recognition and cohosting
Personalization: Content in context As we’ve seen in our research on personalization, context plays an important role when creating an online profile for a user. As a solution to this problem, we want to give the user the opportunity to share the context in which they are watching. When you watch with friends, family or by yourself, the viewing experience is different. We also wanted to make it possible for users to watch together, even if they are not together. This builds on Couldry’s group liveness, with which he describes the possibility to be constantly in touch with each other, although we are not in the same place in real life (Figure 2.). With these features, we create a bigger sense of liveness and group belonging between the users, but also personalization for the individual. Through this feature we get more information about the context in which the users are watching and how they act when they are watching in these contexts. We know for example the time the viewer started watching, with how many people they are watching, the stage of the
A sense of belonging is created when people feel that they are an integral part of a system or an environment. On the OOPS platform we want to create a more intense sense of belonging by giving the active user and the streamer the chance to create an interactive group. Through polls, inputted by answers that are detected through voice recognition, the streamer can find out user’s preferences. The streamer can for example get to know if the watchers are ready to move on to the next step of the recipe (Figure 4). This without having to read a chat. Through voice recognition the users are able to respond to a streamer and be an active part of the stream. The streamer can also choose a user to be a co-host so they can share their own experiences with the group (Figure 3). Their screen will be enlarged and the microphone will be enabled so streamer and user can interact with each other.
Nowadays, users often need their hands to type a reaction or click on a poll to create interaction. These features are therefore especially useful for livestreams where people are already busy with their hands, as is the case with cooking. Through these features we can get new information through sound waves, sound signatures, volume and word recognition
Liveness: Before, during and after the livestream To create a bigger sense of liveness we want to create interaction not only during, but also before and after the livestream. With these features, we want to create a bigger sense of liveness for the viewers, by giving them the possibility for participation and give them the necessary information. Antecedent to the cooking livestream, potential viewers will get a list of ingredients that are necessary for participation. Potential viewers are viewers who are following the livestream, or are users of the OOPS application. Through this list of ingredients, we notify the potential viewers on what is about to come and what time the viewer will go live. The potential viewer will get another notification right before the start of the livestream. During the livestream not only the ingredients, but also the products that are being used by the streamer can be bought by the viewers. By using artificial intelligence products can be detected and linked to platforms from other companies. In this way, viewers have a reference and an explanation with the product, before buying it.
Also after the live moment we want to make a feature that makes it easier for the viewer to see the essence of the livestream. In the post-live video, the streamer can highlight key-moments for the viewers. One of these key-moments can be an important step in the recipe. The viewer can choose to watch only the highlighted moments, making the live stream feel like it was edited.
These features not only create a bigger sense of liveness, they also create new ways of earning revenue for streamers. They can partner with different companies that would like to promote their products, without interfering with the course of the livestream. Through these interactions we create several data points: clicks in the link, number of viewers buying products, time of buying the product, leave the stream for buying and the location of buying.
OOPS: towards a data driven business The data driven business ecosystem is basically formed by big international companies such as Facebook or Google who have been able to implement data driven business models. In that context, many small and medium size companies and startups are trying to be part of that data driven ecosystem and create their own data driven business models to have the ownership of the data generated in through their products and services. In a data driven business model, the value proposition offered through a product or a service has different amounts and combinations of data that give the company a competitive edge and the opportunity of predict business results in a more effective way. In this scenario, data becomes one of the most important resources for the company [1]. We can conclude that in a data driven business model the key resources are probably the most important thing in the development of a product or a service. Understanding data as the most important resource, the company should be able to know which is the data generated through their products or services, which is the source of the data generated and which are the key
activities in order to transform the data into valuable inputs both for the end users, for the company itself and for commercial partners they could have. With OOPS we tried to create new opportunities for data collection through new features. With these interactive features, we will get to know more about the costumer’s cooking habits, which is valuable for the streamer, but can also be valuable for companies that supply food. Supermarkets, for example, know what is being bought at the supermarket. They don’t know what people do with this food when they get home. With OOPS we can get this data and make a more interesting profile of a user.
Conclusion In this research we focused on resolving the following question: How can we create new interactions with new data opportunities in livestreaming in different fields? By researching personalization, sense of belonging and liveness we found out more about the way a livestream can be more interesting for the user. With our prototype OOPS, we tried to create new data resources by creating different features that can be used for livestreaming. With these features users can be more involved in the live moment, making this moment more unique and valuable for them. When adapting a data driven business model, companies should make sure the data they are gathering is useful and will give extra value to the experience of the user. Understanding this data by making the right combinations is the most important resource, not the amount of it. Using this data to get to know the costumer
For this prototype, we focused on cooking classes, but our platform can be used for many different sorts of education. In the future, we would like to expand the possibilities so our platform can also be used for other fields. References 1. Cambridge Service Alliance. Data-Driven Business Models. Retrieved from https://cambridgeservicealliance.eng.cam.ac.uk/Re search/DDBM. 2.
CXL. Why content personalization is not web personalization (and what to do about it). Retrieved from https://conversionxl.com/blog/webpersonalization/
3.
Daniel Dayan en Elihu Katz. 1992. Media events: The life broadcasting of history. Cambridge: Harvard University Press. 1-25.
4.
Esther Hammelburg. ‘#stemfie: reconceptualizing liveness in the era of social media’. In Tijdschrift voor mediageschiedenis (18.1), 85-100.
5.
Hagerty, Bonnie M. K., Lynch-Sauer, Judith, Patusky, Kathleen L., Bouwsema, Maria, Collier, Peggy (1992/06).”Sense of belonging: A vital mental health concept.” Archives of Psychiatric Nursing 6(3): 172-177.
6.
Jane Feuer. 1983. The concept of live television: ontology as ideology. In Regarding Television. Los Angeles: American Film Institute. 12-21.
7.
Julie Willcott. 2016. A sense of belonging in a digital world. Retrieved January 24, 2018 from https://medium.com/@Sevenzo/a-sense-ofbelonging-in-a-digital-world-69ac4b88f0a8
8.
Karin van Es. 2016. The future of live. Polity press, United Kingdom.
9.
Medialab Amsterdam. Break up/love letter. Retrieved January 24, 2018 from
https://medialabamsterdam.com/toolkit/methodcard/break-uplove-letter/ 10. Nick Couldry. 2004. “Liveness, ‘reality’, and the mediated habitus from television to the mobile phone.” In The Communication Review (7.4): 353361 11. Simon Sinek. 2009. Hoe grote leiders tot actie inspireren. Video. In Ted Talk. https://www.ted.com/talks/simon_sinek_how_grea t_leaders_inspire_action?language=nl