Netflix is able to predict and suggest what you should watch next. If you have searched for a bag online, be ready for a slew of bag-related ads on Facebook. Bought a ticket to the Maldives on Cleartrip? You can be sure to see information on this destination throughout your online experience for the next few days. These are just a handful of examples where data and analysis intersect media and entertainment. In every industry, big data applications are changing the present and the future.
In the last decade, the media and entertainment industry has made great progress on how content is created, marketed and distributed. Let’s deep dive into different trends, challenges and opportunities of using data and analysis in the industry.
The Internet-savvy consumers of today search and access content anywhere, anytime – on the desktop, phone, tablets and the TV. As a result, brands, publishers, broadcasters, news channels and even gaming companies are under extreme pressure to execute new digital production, multi-channel advertising and distribution strategies to reach the right customer at the right time.
They need to have a detailed understanding of consumers’ media consumption preferences and related behaviours to find a way to differentiate themselves from the clutter. There is also a change in the media landscape, with a massive shift towards digital content from analogue, offering opportunities to monetise content and identify new products and services. This is the best time for media and entertainment companies to leverage their big data assets for a more accurate and profitable customer engagement than ever before.
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Data collection and analysis is not new to the media and entertainment industry. There was a time when these companies had to read reviews, interview customers, have focussed group discussions and follow and analyse TV and chart rankings to gain customer data and insight. This process has become far simpler with the help of big data and analytical tools. Today, companies can track clicks, views, engagement and shares across all devices and media types in order to tweak content, pricing, marketing and distribution strategies.
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Some of the ways in which data is being used by media and entertainment companies across the world are as follows:
1. Predicting what the customer wants
Sustained data collection and analysis allows companies to predict audience interest based on viewing history, ratings, searches, reviews, location, device data, clickstreams, log files and social media sentiment. By utilising this data in conjunction with customer sentiment analysis, companies can create and distribute the right content to the right audience.
Natural language processing (NLP) guarantees accurate customer sentiment analysis with the help of algorithms that measure positive and negative language manifestations. The algorithms are capable of classifying posts, messages and conversation fragments by sentiment and defining the emotion, and companies can leverage this data to tweak or create content accordingly.
In the case of Netflix, the company harnesses data from its 167-million subscribers and uses data analytics models to analyse customer behaviour and buying patterns. It then uses that information to recommend movies and TV shows based on their subscribers’ preferences. Read more to learn more about applications of Data Science and ML in Netflix.
2. Scheduling and optimisation
Media and entertainment companies can use big data to understand when customers are most likely to view content and what device they will be using to view it. With big data’s scalability, the information can be accessed at a PIN code and device level for localised distribution. This is not only used in the context of social media but also in programming content on streaming media services, TV and gaming platforms.
3. Customer acquisition and churn
Subscription and subscription patterns are one of the most studied data sets for media and entertainment companies. With this, they can develop the best promotional and product strategies to attract and retain customers. Call detail records, emails and social media sentiment are the three main channels that reveal customer interest and why there may be a retention issue, if any.
Spotify, the largest on-demand music service in the world with more than 150 million active users scours more than 600 gigabytes of data to perfect its algorithms and learning to improve customer experiences and extrapolate insights.
Spotify also crawls the web constantly to look for blog posts and other content about music to understand what people are saying about specific artists and songs. By doing this, the company is able to create value for the customers and curate personalised content on ‘Recommended Playlists’, ‘Discover ’ and ‘Insights’ sections of the platform. Naturally, this has a direct impact on customer acquisition and slows down the churn.
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4. Ad targeting
It is imperative for every industry to reach the consumer in the right context, at the right time and on the right platform. The access and consumption of content on the Internet has made digital media complex and ever-changing. Data analysis of consumption patterns when used with traditional demographic data can offer insights for personalised advertising.
Big data applications constantly improve ad targeting despite complex content consumption behaviours by micro-segmentation of customers. For instance, since consumers access content on multiple devices, big data insights can be helpful in understanding when consumers use a second screen so that campaigns can be optimised across devices. This can help media and entertainment companies increase digital conversion rates.
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5. Content monetisation
Big data can also help media and entertainment companies generate additional sources of revenue. For example, these insights can suggest new ways to incentivise consumer behaviour by offering discounts and longer subscriptions. Big data can also help companies in identifying an opportunity for a new product or service.
In 2018, Disney signed a deal with Reliance Jio Infocomm to allow JioCinema and Jio’s digital app, to offer its content on its platform. JioCinema hosts a dedicated Disney branded section on the homepage with content spanning across movies, animation and series. For Disney, this has been a great way to widen the revenue stream. Read more about the applications of big data in pop-culture.
Media and entertainment companies use some of the following tools to streamline data and derive valid insights:
Hadoop is an open source framework that permits reliable distributed processing of large volumes of data across clusters of computers. In fact, Netflix’s data structure includes Hadoop, Hive and Pig with other traditional business intelligence as well
Qubole is a cloud-native data platform that develops a machine learning model focusing on data activation. It can process all types of data sets to extract insights and build artificial intelligence-based applications.
This is an open source tool that provides a single platform and architecture for data processing.
- Apache Cassandra
Cassandra is a free, open source, NoSQL distributed database management system that can handle a high volume of unstructured data across commodity servers.
This database management tool is a cross-platform document database that provides facilities for querying and indexing.
Some of the other big data tools and software used by companies are Apache Storm, Couch DB, Statwing, Flink, Pentaho, Hive, Rapidminer, Cloudera, Openrefine, DataCleaner, Neo4j, Apache SAMOA, Teradata and Tableau.
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New technologies and tools are emerging every month to help media and entertainment companies accelerate their data-driven journeys. This is sure to pose some challenges for marketers. Some of these problems are listed below:
1. Change of mindset
While content creators and marketers have made great progress in using data insights, they still view the customer journey in silos of awareness, branding, acquisition, retention and loyalty. Looking at these areas as separate and distinct stages rather than a smooth continuum leads to issues related to budget spends. Organisation structures are such that each team may be given deliverables and budgets as per the old mindset, but the need of the hour may be different.
2. Low penetration of high-speed Internet
Even though 4.54 billion people (59% of the global population) use the Internet actively as of January 2020, high-speed Internet is still a challenge in many countries. China, India and the United States are the top countries in terms of internet users, but 4G connectivity remains fractured, especially in India. This can throw off budgets and programming by a wide margin.
3. Lack of consolidation
Some companies still treat display, video, mobile, social and native channels of distribution and marketing separately. Each channel is tracked using separate key performance indicators (KPIs). With target rating points (TRPs) and click-through rates (CTRs) dominating success metrics, media businesses are unable to grasp the effects of content on business results immediately.
There is still time for the digital age to have truly arrived in all parts of the world. What may be true in terms of consumption patterns for India may not be relevant for the USA.
4. Urgency for result-oriented metrics
In the chase to achieve quick results, most media companies are beginning to organise themselves to become omni-channel by design. Since many times only one person is viewing all the screens (TV, phone and tablet) companies need to tweak their KPIs accordingly. There is also a significant waiting time to draw ample volumes of data and insights for accuracy and trends. In the rush to dominate the customers’ minds, companies may miss more enduring insights that have more long-term results.
5. Data privacy concerns
In the last few years, many companies have been accused of data breaches and leaking personal information. Adobe, Dubsmash, LinkedIn and Facebook are some of the companies that have recently come under the scanner. As a result, consumers have become more sensitive towards their data and are concerned regarding how it will be used.
Across the globe, policymakers have addressed these issues and have implemented regulations for industries that handle personal data. Such a challenge can pose problems when it comes to accumulating sufficient user data, without which accurate analysis cannot be performed.
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Despite the challenges faced by the media and entertainment industry in using big data, several opportunities have emerged over the years. Most of these opportunities are embedded in cloud infrastructure and artificial intelligence. The legacy of a company and its infrastructure can limit it to scale rapidly as a response to the break-neck speed of data and analysis. This makes the use of cloud technologies inevitable.
Cloud technologies can help large organisations in handling the sheer size of the data and massive computing challenges. Failing to adapt to infrastructure requirements of the new data-driven world swiftly enough can cost media and entertainment companies a certain amount of profit. Artificial intelligence (AI) is the only reliable way to analyse large volumes of data and make predictions for accurate consumer behaviour.
AI allows companies to derive nuanced insights for specific consumer segments and persona rather than focusing only on impressions and CTRs (click through rates). Advanced analytics can be used to drive true personalisation.
Investing in modern, up-to-date data technologies and revamping business processes to reflect data-driven objectives is imperative for the media and entertainment industry. This is the time for companies to take a programmatic-first approach to both media spending and programming content.
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What is Big Data analytics?
Big Data analytics comes from Big Data and the different analytic measures we need to work with Big Data. It is said that data is not considered big enough until it simplifies 10TB worth of information. That is where data ends and Big Data begins. Moreover, several organisations have kept themselves several TBs away from implementing data analytics. Big Data has a relatively big size, and working with zillions of data is not new. However, in the case of analytics, it is highly unexpected.
What kind of analytics blend easily with Big Data?
Big Data often works well with advanced analytics, also referred to as discovery analytics. Business analysts typically use Big Data analytics to uncover various business-related facts that most people are unaware of. Plus, to conduct operations like these, business analysts need loads of data with many magnified details. Also, organisations keep this analytics data away from public access. Customer churn became a solid concern during the recession. It is the business analyst’s job to analyse the primary cause of the churn. A business analyst collects a humongous amount of data to detect customer behaviour, and then uses this information to figure out the root cause of the churn. The analyst can also mix the operational data with previous data, which does not make much difference. After numerous attempts of analysis, the analyst finds out the hidden behaviour of the churn and converts it to an analytical model.
What is the importance of Big Data analytics?
Organisations are mainly data-driven, and with the integrated use of Big Data analytics, they can make concrete, revolutionary decisions. This will eventually help improve the output of the organisation and business in the end. New revenue streams, increased operational efficiency, more wide-spread targeted marketing, and customer personalisation are some advantages. If these benefits are used right, it can help you get an edge over competitors.