The Importance of VoD Analysis for VoD Consumption

22 Jul 2015

Marketers will tell you that data analysis makes a world of difference. And it truly does. How can any brand deliver outstanding services if it doesn’t know what customers want? Analytics provide useful and relevant information that helps build business models and makes updating these models according to current trends easier.

Far too often, companies tend to rely on assumptions and intuition in today’s business world but with everything changing so fast and technology developing so rapidly, this could diminish the potential of working models. The same goes for VoD consumption. There are several reasons why VoD analysis matter in terms of video-on-demand services and why publishers should make this a top-notch level of approach. Competitive differentiation is one. The rest follow.

VoD analysis — Competitive differentiation

If there is one thing that analytics provides, that is the means for competitive advantages in the light of so many video on demand entrants. At the same time, potential entrants can use data models to set their services apart from those already on the market. If we take into account the fact that a firm looking to make its entry into the VoD market is subject to high investments (let’s only consider the costs of movie licenses), then it’s clear that data matters. Experiential assumptions might work for well-established service providers but new entrants cannot afford the luxury of simply probing. The old management adage of “You can’t manage what you can’t measure” is accurately relevant today.

Data analytics define customer behavior

VoD analysis lets publishers know about user behavior. Normal or interactive states can be analyzed, data showing at what moment during the viewing experience has the user moved from a normal state of viewing (watching the video at normal speed) to an interactive state (performing interactive actions during the experience such as stopping, forwarding, etc.).

VoD analysis can also show the number of times this behavior was performed during the viewing experience. Because interactive actions, depending also on each action’s type, will affect the system differently, this type of data becomes relevant since it relates to bandwidth support capacity and other system technicalities that make up the VoD services. Analytical VoD methods can also measure the probability of access for less popular video content. Using data such as video content requests and particular mathematical models, researchers can estimate how likely or unlikely it is for an unpopular movie to be accessed by users. Such inquiries help publishers with delivering content strategically.

Data analytics define the performance of VoD services across all devices

It’s not just about video-on-demand content anymore; it’s about delivering video content to any device. And this requires the implementation of procedural steps that can respond to new mobile devices and device developments (different screen sizes, different delivery methods, etc.). The various demands can only be responded to when there is information that addresses new challenges as they come along.

Through data analytics, the entire web of information is glued together to provide a uniform work model for publishers. Content management and analysis can be performed within a unified infrastructure that addresses multiscreen activities. Adopting an analysis model for VoD consumption will optimize content management, distribution strategies, and monetization.

Analytics frameworks in the video-on-demand business environment are relevant for optimizing network bandwidth and advertising placement also. Since there will probably never be sufficient video content types and programs to satisfy the entirety of customers, the main goal is to be able to provide suggestions for users. And in order to do so, publishers must first investigate and analyze the type of content and specific content that viewers watch.

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