The video streaming industry has grown at a rapid speed during the past decade and has becomea trending topic in regard to technological advancements. Nowadays, there are manycompetitors in the market, and it is getting harder to stand out from the competition. Furthermore, the customers are demanding more in their choice of video streaming servicesand are expecting several features and functions in order to retain the service. Electronic Customer Relationship Management (eCRM) is a tool that has been used by providers of videostreaming services in order to improve long-term customer relationships. eCRM has beenresearched before as well as implemented by many companies. However, there are stillresearch gaps in relation to a potential impact that eCRM features have on customer loyalty, aswell as a managerial need to investigate these issues further. The purpose of this thesis is to develop deeper knowledge about how pre-, at- and postpurchase features of eCRM affect the customer loyalty in the video streaming industry. Thecontext of the study was the video streaming industry as it has increased majorly in popularityover the past years and there are many features to consider in the purchasing process. Therefore,we developed a conceptual model that was based on prior research to investigate the influenceof specific features on customer loyalty. The empirical data collection was conductedconsidering the conceptual model. This study was conducted by using a qualitative methodwith semi structured interviews, the respondents consisted of active users of a streaming serviceat the age between 18 to 34 years old. By analyzing the empirical data, we could determinefeatures of eCRM that affected and did not affect customer loyalty in our study. The findings showed that all the steps of the purchase process had features that affectedcustomer loyalty. The features of pre-purchase that we found relevant to customer loyalty inour study were captivating customers, which refers to the ability to attract customers' attention. The features of captivating customers are marketing, popularity, and recommendations, andout of these features, it was popularity and recommendation that we found affecting thecustomers loyalty. The features of the second step in the purchase process, at-purchase, that wefound affecting customer loyalty in our study were assortment, price, and convenience. Assortment refers to the content and supply of the service while the price refers to how muchthe service cost. Convenience is divided into four parts, the number of profiles, active users,devices that can use the service and the ability to stream offline, all these features exceptstreaming offline were found to have an effect on customer loyalty. Post-purchase refers to astage after the purchase has been made and, in our study, we found that the feature of clientcommunication has an effect on customer loyalty. Client communication is the communicationthat accrue between company and customer.

La llegada de la era digital ha provocado grandes cambios en los medios de comunicación como el surgimiento de las narrativas transmedia, una forma de contar historias a través de múltiples plataformas donde se requiere la participación activa del usuario. El presente artículo analiza la experiencia transmedia de la serie Stranger Things (2016) y el impacto que tiene en los consumidores al ser un producto original de una plataforma streaming: Netflix. Es un estudio de carácter cualitativo y está basada en las entrevistas y encuestas a comunidades de consumidores de la serie, así como el análisis audiovisual de cada una de las extensiones de la serie. Los resultados demuestran que el factor nostalgia usado en la serie es fundamental para el éxito de la serie y para el surgimiento de la narrativa transmedia dentro de este. Además, la narrativa transmedia permite una interacción más cercana de la audiencia con la serie, así como la fidelización hacia estas extensiones transmediáticas y en consecuencia, la creación de distintas comunidades virtuales en donde los usuarios comparten y crean más contenido a partir de sus propias ideas. / The arrival of the digital era has brought about major changes in the media, such as the emergence of transmedia narratives, a way of telling stories through multiple platforms where active user participation is required. This article analyzes the transmedia experience of the Stranger Things series (2016) and the impact it has on consumers as an original product of a streaming platform: Netflix. The methodology used is qualitative in nature and is based on interviews and surveys of fans of the series, as well as individual analysis of each of the series' extensions. The results demonstrate that the nostalgia factor used in the series is fundamental for the success of the series and for the emergence of the transmedia narrative within it. In addition, the transmedia narrative allows a closer interaction of the audience with the series, as well as loyalty to these transmedia extensions and, consequently, the creation of different virtual communities where users share and create more content based on their own ideas. / Trabajo de investigación

Netflix and other streaming services have grown immensely since they started offering online streaming. In this paper I present a correlation matrix using ticket sales at the domestic box office and the number of Netflix subscribers. They are shown to be negatively correlated with one another, supporting many previous researchers’ thoughts on the topic. I also show using two OLS regressions with data from movies released in 2006-2007 and 2017-2018 that being a part of a franchise has a stronger correlation with increased revenue in the latter model compared to the previous one. In the models one can also see that the general quality of a movie, as measured by IMDb rating, is associated with a higher increase in revenue in the latter model. I argue that this is due to consumers being inclined to watch what they perceive to be high-quality movies in the theaters in the latter model as they can conveniently watch movies of a poorer quality on their streaming service, an option that was not available to the same extent previously. I also argue that consumers are more willing to commit to going to the cinema for a franchise movie, especially in the Marvel cinematic universe, as they are often effects driven movies which are better experienced on a large screen. The budget variable is significant in both models, but the coefficient is much smaller in the second model. I argue that this is due to the fact that a higher budget is required for movies released in 2017-2018 to maintain the same level of revenue as in 2006 and 2007 due to the competition that have come from streaming services. However, I conclude that more research is necessary before drawing definite conclusions as the market for cinema is highly uncertain and difficult to estimate accurately.

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For generations, the technological development of the media has changed human behavior, both for the better and for the worse. From having to buy everything in a store to being able to shop through a live-stream. The problem, however, is that there is a lack of research that has touched on this in the Nordic countries. Especially studies that have touched it in a media archeological aspect. In this thesis, I have analyzed liveshopping as a medium by comparing it with TV-shop. This has been done with the help of media archeology/-ecology as a theoretical and methodological framework. Furthermore, the theory has supplemented with the help of remediation and affordance. Together, they form a theory package that complements each other in the thesis' purpose of examining the medium's properties and design, its environment and how it contributes to the individual's behavior. The analysis of the media has been complemented by the multimodal tools. The material consists of seven broadcasts from TVG (TV-shop) and liveshopping-streams from Lyko, Samsung, MQ MARQET and Lensway The result indicates a change in consumer behavior. During the TV-shop we had a behavior of not shopping from home. Which is proven in the media's attempt to attract viewers with cheap prices on the products. In today's liveshopping, reduced prices are not as common. Due to the fact that the audience actively went into the live-stream, it indicates that they do not necessarily watch the broadcast to be able to buy products at cheaper prices. The technological changes of the media have made possible changes in society through our way of consuming and interacting with the content from companies.

Video streaming on the Internet is increasingly using Dynamic Adaptive Streaming over HTTP (DASH), in which the video is converted into various quality levels and divided into two-second segments. A client can then adjust its video quality over time by choosing to download the appropriate quality level for a given segment using standard HTTP. Scalable Video Coding (SVC) is a promising enhancement to the DASH protocol. With SVC, segments are divided into subset bitstream blocks. At playback, blocks received for a given segment are combined to additively increase the current quality. Unlike traditional DASH, which downloads segments serially, this encoding creates a large space of possible ways to download a video; for example, if given a variable download rate, when should the client try to maximize the current segment's video quality, and when should it instead play it safe and ensure a minimum level of quality for future segments? In this work, we examine the impact of SVC on the client's quality selection policy, with the goal of maximizing a performance metric quantifying user satisfaction. We use acombination of analysis, dynamic programming, and simulation to show that, in many cases, a client should use a diagonal quality selection policy, balancing both of the aforementioned concerns, and that the slope of the best policy flattens out as the variation in download rateincreases.

No description available.

Providers of online services have witnessed a rapid growth of their user base in the last few years. The phenomenon has attracted an increasing number of competitors determined on obtaining their own share of the market. In this context, the cost of attracting new customers has increased significantly, raising the importance of retaining existing clients. Therefore, it has become progressively more important for the companies to improve user experience and ensure they keep a larger share of their users active in consuming their product. Companies are thus compelled to build tools that can identify what prompts customers to stay and also identify the users intent on abandoning the service. The focus of this thesis is to address the problem of predicting user abandonment, also known as "churn", and also detecting motives for user retention on data provided by an online streaming service. Classical models like logistic regression and random forests have been used to predict the churn probability of a customer with a fair amount of precision in the past, commonly by aggregating all known information about a user over a time period into a unique data point. On the other hand, recurrent neural networks, especially the long short-term memory (LSTM) variant, have shown impressive results for other domains like speech recognition and video classification, where the data is treated as a sequence instead. This thesis investigates how LSTM models perform for the task of predicting churn compared to standard nonsequential baseline methods when applied to user behavior data of a music streaming service. It was also explored how different aspects of the data, like the distribution between the churning and retaining classes, the size of user event history and feature representation influences the performance of predictive models. The obtained results show that LSTMs has a comparable performance to random forest for churn detection, while being significantly better than logistic regression.  Additionally, a framework for creating a dataset suitable for training predictive models is provided, which can be further explored as to analyze user behavior and to create retention actions that minimize customer abandonment. / Leverantörer av onlinetjänster har bevittnat en snabb användartillväxt under de senaste åren. Denna trend har lockat ett ökande antal konkurrenter som vill ta del av denna växande marknad. Detta har resulterat i att kostnaden för att locka nya kunder ökat avsevärt, vilket även ökat vikten av att behålla befintliga kunder. Det har därför gradvis blivit viktigare för företag att förbättra användarupplevelsen och se till att de behåller en större andel avanvändarna aktiva. Företag har därför ett starkt intresse avatt bygga verktyg som kan identifiera vad som driver kunder att stanna eller vad som får dem lämna. Detta arbete fokuserar därför på hur man kan prediktera att en användare är på väg att överge en tjänst, så kallad “churn”, samt identifiera vad som driver detta baserat på data från en onlinetjänst.   Klassiska modeller som logistisk regression och random forests har tidigare använts på aggregerad användarinformation över en given tidsperiod för att med relativt god precision prediktera sannolikheten för att en användare kommer överge produkten.  Under de senaste åren har dock sekventiella neurala nätverk (särskilt LSTM-varianten Long Short Term Memory), där data istället behandlas som sekvenser, visat imponerande resultat för andra domäner såsom taligenkänning och videoklassificering. Detta arbete undersöker hur väl LSTM-modeller kan användas för att prediktera churn jämfört med traditionella icke-sekventiella metoder när de tillämpas på data över användarbeteende från en musikstreamingtjänst. Arbetet undersöker även  hur olika aspekter av data påverkar prestandan av modellerna inklusive distributionen mellan gruppen av användare som överger produkten mot de som stannar, längden av användarhändelseshistorik och olika val av användarfunktioner för modeller och användardatan. De erhållna resultaten visar att LSTM har en jämförbar prestanda med random forest för prediktering av användarchurn  samt är signifikant bättre än logistisk regression. LSTMs visar sig således vara ett lämpligt val för att förutsäga churn på användarnivå. Utöver dessa resultat utvecklades även ett ramverk  för att skapa dataset som är lämpliga för träning av prediktiva modeller, vilket kan utforskas ytterligare för att analysera användarbeteende och för att skapa förbättrade åtgärder för att behålla användare och minimera antalet kunder som överger tjänsten.

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Streaming multimedia content over the IP-network is poised to be the dominant Internet traffic for the coming decade, predicted to account for more than 91% of all consumer traffic in the coming years. Streaming multimedia content ranges from Internet television (IPTV), video on demand (VoD), peer-to-peer streaming, and 3D television over IP to name a few. Widespread acceptance, growth, and subscriber retention are contingent upon network providers assuring superior Quality of Experience (QoE) on top of todays Internet. This work presents the first empirical understanding of Internet’s video-QoE capabilities, and tools and protocols to efficiently infer and improve them. To infer video-QoE at arbitrary nodes in the Internet, we design and implement MintMOS: a lightweight, real-time, noreference framework for capturing perceptual quality. We demonstrate that MintMOS’s projections closely match with subjective surveys in accessing perceptual quality. We use MintMOS to characterize Internet video-QoE both at the link level and end-to-end path level. As an input to our study, we use extensive measurements from a large number of Internet paths obtained from various measurement overlays deployed using PlanetLab. Link level degradations of intra– and inter–ISP Internet links are studied to create an empirical understanding of their shortcomings and ways to overcome them. Our studies show that intra–ISP links are often poorly engineered compared to peering links, and that iii degradations are induced due to transient network load imbalance within an ISP. Initial results also indicate that overlay networks could be a promising way to avoid such ISPs in times of degradations. A large number of end-to-end Internet paths are probed and we measure delay, jitter, and loss rates. The measurement data is analyzed offline to identify ways to enable a source to select alternate paths in an overlay network to improve video-QoE, without the need for background monitoring or apriori knowledge of path characteristics. We establish that for any unstructured overlay of N nodes, it is sufficient to reroute key frames using a random subset of k nodes in the overlay, where k is bounded by O(lnN). We analyze various properties of such random subsets to derive simple, scalable, and an efficient path selection strategy that results in a k-fold increase in path options for any source-destination pair; options that consistently outperform Internet path selection. Finally, we design a prototype called source initiated frame restoration (SIFR) that employs random subsets to derive alternate paths and demonstrate its effectiveness in improving Internet video-QoE.