The Importance Of Predictive Analytics For Digital Entertainment Businesses
Techniques for Predictive Analytics in Digital Entertainment
This report aims to discuss the topic Big data in digital entertainment. A brief discussion of the industry of entertainment before the implementation of big data is provided in this report. A detailed discussion of the application of big data in the sector of entertainment and media is provided in this report. A comprehensive discussion of the analysis of the needs of industries in the sector of media and entertainment is provided in this report. A brief discussion of the importance of big data analytics is provided in this report. A discussion of the techniques of predictive analytics is provided in this report. The major issues of big data is provided in this report. Lastly, this report concludes with an appropriate conclusion for this report.
The term big data refers to the datasets that are significantly large or complicated for the conventional software application of data-processing for adequately deal with (Swan, 2013). The data with several cases provide greater power of statistics, while the data with higher complexity can lead to the higher discovery rate that is false. The challenges of big data includes the data capturing, storage of data, analysis of data, search , transfer, sharing, visualisation, querying, privacy of information and updating (Provost & Fawcett, 2013). It also refers to the utilisation of predictive analytics, analytics of user behaviour, or several other advanced methods of data analytics that can extract important value from data1, and produce data to a specific size of data set.
The publishers, organisations of news, broadcasters and the companies of gaming in the entertainment and media industry are now facing new models of business for the methods by which the creation, marketing, and the distribution of the content is executed (John Walker, 2014). This is happening because of the present search and access content of consumers from anywhere, at any time and on any device. This leads to the increased pressure for executing innovative digital production and the advertising that is done multi-channel and strategies of distribution that relies on a particular understanding of the preferences and the behaviours of the consumption of media of the consumers (Wu et al., 2014). And, due to the interest shift of the consumers from the analog to the media that is digital, there are substantial opportunities for monetising the content and for identifying innovative services and products.
In the age of digital media, there are several connected devices for the automation tasks (Lazer et al., 2014). These devices produce a huge amount of information that can provide huge information about the customers. The information varies in formats and frequently altering the result in the formation of the big data. When the organisations of media and entertainment do not leverage this kind of technology, they can miss on a number of opportunities (Raghupathi & Raghupathi, 2014). One of the primary losses in the losing out on the actionable insights. Commonly, the conventional entertainment authorities focusses on the decision taking that are based on the patterns that are long-established for the decisions that are not working in their respective favour (Murdoch & Detsky, 2013). While undertaking such decisions that are based on the traditional model can usually land the organisations with huge debts or it can also suffer enormous losses when there is a failure in the strategy.
Integration of Predictive Analytics in IT Architecture of Digital Entertainment Companies
With the increase of the consumers of digital media from thousands to millions, the industry of media and entertainment are in a unique position of leveraging the assets of big data for increasing the profitable engagement of customer (Gandomi & Haider, 2015). The usage of big data in the entertainment industry helps in obtaining the insights of the trends of customers, the schedules of shows and the trends of advertisements. The utilisation of smart devices is rising in the present times. According to a research, 29% of the people all over the world spend approximately 4-6 hours on their smartphones daily (Hashem et al., 2015). The viewing of entertainment has been changed with the increase of smartphones users. As a broad platform of entertainment is provided by the smartphones, the companies that are dealing with the media and entertainment are also turning towards the technological platforms, which can help them in experiencing the transformation. As enormous amount of information are generated by the smart devices, when all the big data is analysed, it can assist in the obtaining of actionable insights about the information that can assist them in the addition of strength to the processes of decision-making (Chen, Mao & Liu, 2014). The aspect of big data in the industry of entertainment can be a technology that can assist the houses of media in experiencing the analytics innovation. Some of the examples how entertainment and media companies can gain benefits from the applications of big data.
Several organisations tend to schedule the shows at the times and the days according to the customers for preventing the losses of TRP and the revenue loss. With the analytics of big data, the CIOs and the CTOs of the companies have the information of the slots of time when their shows can generate the highest TRPs (Chen & Zhang, 2014). With the help of the available platforms of social media, finding the likes and the dislikes of the customers poses a challenge for the houses of media, as these companies have the ability of analysing the collected data from these sources for knowing the preferences of the customers. Actionable insights can help the companies in the process of planning the shows in a manner, which can attract the attention of the customers.
With the help of the analytics of big data, the companies can also strategize the methods of tweaking the subscription offers for generating increased profits (Sagiroglu & Sinanc, 2013). The CTOs and CIOs of the house of media and entertainment should also study the success of other companies for generating plans for development. Apart from the learning processes, the companies can also focus on the training of the employees for working with the software that can assist them in the insight obtaining from the information in their possession.
Big Data in Digital Entertainment
The scope of the collected big data by the industry of media and entertainment and for potentially mining it for understanding the preferences of content, shows, music and the movies of the customers (Kitchin, 2014). The viewing history, reviews, searches, ratings, location and clickstreams, device data, log files and the sentiment of social media are some of the common sources of data that can help in the identification of the interest of the audience.
With the insights from the big data, the companies of the industry of entertainment and media gained the capability of understanding the most likely schedule of content viewing and predicting the device from which the customers view the content (Marx, 2013). This information can be analysed by conducting scalability of the big data at the ZIP code level that is granular for localised distribution.
The companies of media and entertainment can perform the development of best strategies of promotion and product creation for attracting and retaining customers with the help of big data for understanding the pattern of subscribing and unsubscribing any content (Fan & Bifet, 2013). The unstructured sources of big data are best handled by the applications of big data like the records of call detail, sentiment of social media and email reveals some of the commonly overlooked factors that drives the interest of the customers.
The use of big data makes it possible for understanding the consumption of digital media and entertainment and the behaviour that can be utilised with the conventional demographic data for providing the advertising that are personalised in the correct context, at right time and in correct place (Kitchin, 2014). The applications of big data can help in improving the targeting of advertisements in the increasingly complicated behaviour of consumption of content. For example, as the consumers access the media and entertainment on several devices at one instance, its beneficial for using the insights of big data for understanding the situations when any customers is using second screen for the optimisation of the campaigns across devices (Ward & Barker, 2013). The companies of digital media and entertainment can increase the rates of digital conversion with offering micro-segmentation of the customers for the advertising of exchanges and networks.
Big data can assist the companies of media and entertainment in generating additional revenue sources by suggesting innovative methods of incentivising the behaviour of customers, revealing the true value of content in the market, or identify any new service or product opportunity.
Benefits of Utilizing Big Data in the Entertainment Industry
Data has been generated from the sector of media in the form of research, customer databases, sales, log files and several others. Similarly, the huge majority of the broadcasters and publishers have continuously faced the requirement of competing right from the early days of the publishing of newspapers in the eighteenth century (Jagadish et al., 2014). Even the government or the media bodies that are privately funded have to continuously prove the relevance to their audiences for staying relevant in the world of extensive choices and for securing the funding in the future. But the mind-set of big data, the technical solutions and the strategies offers the ability of managing and disintegrating the data at quick speed and at scales that have existed before.
There are three major areas where the big data has the potential of disrupting the current status and help in the economic growth in the sector of media and entertainment. These major areas are:
- Services and products: The businesses that are driven by big data have the ability of publishing content in a more complicated method. Human expertise in curation, psychology and the editorial intelligence can be complemented with the quantitative insights that are derived from the analysis of large and heterogeneous datasets (Kaisler et al. 2013). But, with the use of the analysis tools of big data, the prediction is done easily which is easy to use for the business users and the data scientists.
- Suppliers and customers: Big data will be utilised by the ambitious companies of media for discovering about the customers, like the preferences, attitudes and the profile and this information will be used for building relationships that are more engaged. Media companies get content from the individuals who have become suppliers with the usage of social media tools and the capturing of data (Bettencourt, 2014). Without the applications of big data, the approach will be random and wasteful for discovering the content that is most interesting.
- Process and infrastructure: While the SMEs and the startups can function efficiently with the open source and the infrastructure of cloud, this is a challenge for the bigger companies as the upgrading of the infrastructure of IT is significantly difficult for them. The standards and the legacy products still needs to be supported in the transition for big data method of working and thinking. The culture of organisation and the process might also need to be updated with the expectation of the features of big data (George, Haas & Pentland, 2014). The failure of transforming the culture and the skillset of the staff could affect the companies who are more profitable in the present times but they cannot adapt to the business models that are driven by data.
Big data can prove to be beneficial to any organisation when it is used with the predictive analytics that enables the businesses in making swift strategic decisions. It is the roadmap to developing better business. Predictive analytics is the technology that is driven by data and the statistical methods that examines the large data sets for discovering patterns, reveal new information and also predict the points in failure and the future outcome (Xiaofeng & Xiang, 2013). The usage of big data can be a huge advantage to any organisation when it is used with the predictive analytics that enables the businesses for making swift strategic decisions. The importance are big data analytics are as follows:
Provide rigorous customer insight and improve the customer relationship: It is easy to predict the customer spending habits of every customer by conducting analysis of all data related to the behaviour of customers such as the transactions, activity on social media, browsing of the web, demographics, interests and then transform these into meaningful trends
Identify the key issues in the business processes: For avoiding the inefficiencies that costs the company several customers and revenues, the methods of predictive analysis can be used for providing focus to the processes of the business. These analyses might help in determining the areas of problem from the beginning to the end of the work cycle and then optimise the processes (Lee, Kao & Yang, 2014). This data can be backed up with the help of the feedback from the customers with the reviews and social media.
Improve the networks of suppliers: The methods of predictive analysis are extensively becoming more advantageous for the management of supply chain as this makes the processes more accurate, reduced cost and more reliable. As the management of supply chain is a continuous and cohesive process, any kind of failure in the system will result in the causing of insufficient execution (Katal, Wazid & Goudar, 2013). This is the main reason why predictive analysis must be applied in each step such as the discovery of the demand data and the effort of calculating the demands in the future, and converting it for forecasting the requirements of production and backwards in the requirements of procurement and logistics.
The techniques and the approaches that can be utilised for conducting predictive analytics are widely categorised into regression methods and the techniques of machine learning.
The regression methods are the most common method of predictive analytics. In this method, the focus lies on the establishment of an equation of mathematics as the structure for representing the connections among the various considered variables. There are several methods of performing the predictive analytics depending on the instance. Some of the methods are as follows:
Linear regression models: This model analyses the relationship among the dependent or the response variable and any set of the independent or else the predictor variables. This connection is expressed as the equation, which can predict the response variable as the linear function of these parameters. Adjustment of parameters is done for measuring the optimisation of a fit. Huge amount of effort in the fitting of model is basically focussed on the minimisation of the residual size and the ensuring, which is randomly distributed depending on the predictions of the model.
Discrete choice models: The method of multiple regression is used in the situations when the variable of response is found to be continuous and consists of a range that is unbounded. Sometimes the variable of response might not be continuous but it can be discrete. Although mathematically, multiple regression can be applied for the dependent variables that are discretely ordered, bulk of the assumptions on the multiple linear regression theory are outdated and there are several other methods like the models of discrete choices that are most preferred for this analysis kind (Waller & Fawcett, 2013). When the dependent variable is found to be discrete, the methods of logistic regression, the models of probit, and multinomial logit are superior.
Logistic regression: Within any setting of classification, allocating the probabilities of outcome to the observations might be obtained by utilising the model of logistic that is fundamentally a technique that performs information transformation about the dependent variable that is binary into a variable that is continuous and unbounded and it guesses a model of regular multivariate.
Multinomial logistic regression: this is an extension of the model of binary logit for the cases where the considered variable that is dependent consists of more than two groups is the model of multinomial logit. The multinomial logit model is the most suitable technique in such cases, particularly when the ordering of the dependent variable is not done into categories.
Even though the utilisation of big data is growing to be extensive in the recent times, there are several issues related to the use of big data. Some of the key issues while using big data are:
Breach of privacy: This is the biggest issue in the use of big data. This refers to the release of the private information to the unauthorised persons who must have no access to the data, whether it is done deliberately or mistakenly. The breaches of privacy can occur when any business deploys weak measure of security. Even though any hacker is majorly liable for this kind of act, it can be prevented with the implementation of strict tools and extensive security measures. For combatting the privacy breaches, investment must be done on software of anti-malware that provide an entry point and then employ the connections that are secure from the system of data collection to the system of data storage.
Issue of anonymity: The identification of the individuals with the help of anonymised data in the datasets that are public is now possible. Even though this process is not easy, but it provides the opportunity of identity theft.
Accuracy of analytics: In any kind of research, there is an aspect of error margin or the possibility of miscalculations, exceptions and several other factors that causes a comparatively dismissible error amount. The analysis of big data possess this factors as it consists the analysis of significantly huge amount of data (Townsend, 2013). As it is extensively difficult to verify the analysis manually, the best possibility is that the analytics will not offer huge comprehensive inaccurate data by utilising a data analysis tool that is trusted, which guarantees the greatest accuracy level.
Conclusion
Therefore, the use of big data in the sector of digital entertainment is advantageous for the companies. The term big data denotes to the datasets that are significantly large or complicated for the conventional software application of data-processing for adequately deal with. The data with several cases provide greater power of statistics, while the data with higher complexity can lead to the higher discovery rate that is false. The publishers, organisations of news, broadcasters and the companies of gaming in the entertainment and media industry are now facing new models of business for the methods by which the creation, marketing, and the distribution of the content is executed. In the age of digital media, there are several connected devices for the automation tasks. These devices produce a huge amount of information that can provide huge information about the customers. With the increase of the consumers of digital media from thousands to millions, the industry of media and entertainment are in a unique position of leveraging the assets of big data for increasing the profitable engagement of customer. The scope of the collected big data by the industry of media and entertainment and for potentially mining it for understanding the preferences of content, shows, music and the movies of the customers. With the scalability of big data, this information can be analysed at the ZIP code level that is granular for the distribution that is localised.
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