Data analytics has become an invincible tool to stay competitive in any business. A Gartner survey reveals that 73% of organizations have already invested or are planning to invest in analytics. So there is hardly any sector not impacted by big data. The entertainment sector is one of the early adopters of big data analytics. A large volume of data is generated in this sector, mostly in digital form and has the power to change the consumer research space. So, analytics has much to offer in this sector. However, much more progress is required to reach the full potential.
The problem concerned with this sector is not the collection of data but the analysis of this plethora of data to improve decision-making and business processes. Data analytics has the potential to bring about a massive change in the structure of the entertainment sector by reducing costs and targeted campaigns and programmes. IBM used predictive models for the movie Ram Leela and predicted a 73% success for the movie based on the right selection of cities.
Major Concerns of the Industry
• Competition and crowded sub-sectors: Sub-sectors such as digital marketing, broadcasting are immensely crowded with very few dominant businesses.
• Changing consumer preferences: One of the major problems faced by this industry specifically is the sudden change of consumer preferences. New paradigms can emerge very quickly.
Application of Analytics in this Sector
A Large volume of unstructured data is often available with the media houses. Analysis of these datasets can be done to derive insight, stories, and contents. There are substantial opportunities to monetize these contents that can ultimately be sold to customers. This helps media companies to find alternate revenue sources.
Semantic publishing refers to online publish of documents along with linked metadata that describes them. This helps in efficient targeting.
Social Media Analysis
According to eMarketer, nearly one-third of the world uses social network regularly and Facebook has 1.13 billion daily active users. These generate humongous amount of data. Batch and real-time analysis of such bulks of data through text mining of social media contents with regard to sentiment, topic and other aspects of the text is required. This helps to performs large-scale data processing with low costs.
Product Development and Product Diversification
Data mining and analyzing trends are required to predict the development of new and improved products. This helps to keep the business in competition and minimises risk by offering a more data-driven solution in an uncertain market. Product diversification becomes especially important since consumers have heterogeneous preferences.
Programmatically analyzing data from various sources like viewing history, searches, ratings, reviews, location and other such factors build up a comprehensive database of a customer. This helps to understand customers on a micro level. For instance, Netflix’s success can be explained by the company’s detailed analysis of network data to develop and market content.
This includes micro-channelling preference-based content for consumers and segregating them in terms of preferences and other attributes from a business point of view.
Customer Churn and Retention Insights
It has been found that about 30% of customers share their reviews through social media. Without deeper insights of data, it is nearly impossible to reduce customer churn and develop strategies to attract and retain customers. Through data analytics, customer churn can be reduced by content pricing, media content, etc.
Marketing and Advertising
Effective marketing and advertisement targeting by understanding digital media and entertainment along with demographic data are required to provide advertising in the correct context. The box-office success of the film Chennai Express in 2013 can be attributed to analytics intensive targeted social media and digital marketing campaigns.
These include algorithmic scalable approaches to produce more interesting results, predictive analysis, semantic graph data canalization using inference engines, data visualization, cross-sell etc. IBM used analytics to get a predictability of success by genres and named it Social Sentiment Index (SSI). SSI predicted with 75% confidence that a film with political background stands a strong chance of scoring well at the box office.
Through these techniques, companies can refine their services, and create content that is consistent with consumer requirements.
So, there is the massive potential of analytics in the media sector, much of which remains unutilized. If properly executed, it can give a huge impetus to revenue and businesses. It can help to control the biggest risk factor in this consumer market-changing preferences. Engagement and interaction with customers and creating customer-centric atmosphere are the key factors which influence this sector. Data analytics helps to bridge the gap between producers and end-users by allowing media sectors to understand their consumers at an individual level and thus helping the industry which thrives on the preferences of the end-users.