How Data Science is Changing the Media & Entertainment Industry

How Data Science is Changing the Media & Entertainment Industry

With digitisation and data-driven decision-making revolutionising the majority of functional domains, industries with traditional workflows are completely transforming themselves to match their stride with the advancement in data and analytics. Sophisticated data science tools are increasingly being used by managers to create better and more personalised content to drive higher consumer satisfaction.

A large volume of data is generated in this sector that holds the power to change how consumers interact with brands. The problem here is not the collection of data but the analysis of this plethora of data to improve decision-making and increase the engagement of the audience with the content generated, be it a television series or an advertisement campaign. Data analytics has the potential to bring about a massive change in the structure of the media and entertainment sector by reducing costs and introducing campaigns that have better targeting and the ability to match with audience interests.

Usage of Data Science in Media and Entertainment

There are a number of ways in which media and entertainment companies can utilise data science applications to improve customer engagement and establish an edge over their competitors.

Customers are the focal point of everything that happens in the advertising industry. Understanding consumer psychology, availing their digital footprint to mould ad campaigns accordingly, reforming the product based on customer feedback, generating content based on past viewership data - all of this and more, is what analytics in the entertainment space contributes to.

Some of the ways in which data science and analytics are changing the media and entertainment industry are as follows:

Prediction of Audience Behaviour

Platforms like Netflix and Amazon have enormous potential to analyse the data that they collect and use the inferences to create more user-focused shows in different genres. For instance, let’s take the case of one of the biggest hits of Netflix, House of Cards. The platform made incredible use of the viewership data by doing an in-depth, granular analysis on the viewing habits and preferences to infer that political dramas with Kevin Spacey as a protagonist were likely to become popular with the audience. Hence, it placed much higher bids to buy the rights of the show House of Cards against HBO and other cablers.

Not only that, with better insights using data science, these platforms are able to gauge when customers are most likely to view content and what devices will be used for watching. With big data’s ability to analyse huge amounts of data, this information can be used to enable localised distribution of content.

Analysing Customer Sentiment

All consumer-facing organisations, whether product-based or service-based, seek to understand how the visitors feel about their content, website, and their social media presence. This understanding gives them the opportunity to alter their strategies to suit the viewer’s taste. Customer sentiment analysis allows brands to gather that information. The algorithms are capable of classifying the posts, messages, and even conversation fragments by the sentiment they express, culling out the inclination of customers towards the brand. This results in understanding the repeat customers’ behaviour as well as what might attract a similar audience.

Personalisation of Content

Using data science applications, companies can easily detect consumer behaviour across various platforms. With advanced segmentation tools, companies have the abilities to create highly personalised and targeted advertising campaigns, leading to an increase in conversion rates and ultimately increasing user engagement with a brand or the platform.

Recommendation engines work on the same principle, using past data trends to show users customised content recommendations. For instance, streaming platforms like Netflix, Hotstar, Amazon Prime and others analyse your past viewing patterns to show you the next most recommended watch.

Using data science and machine learning to dictate what to show users even if they don't know it's what they want was once a left-field strategy, but has proved extremely effective. Personally, some of my all-time favourite shows are the ones that I discovered through recommendation engines.

Conclusion

Data science in the field of media and entertainment has become a prerequisite to drive decision-making if companies want to stay ahead of the competition. A data scientist's ability to collect, store, process and analyse data, and make recommendations based on it is a huge benefit for the media and the entertainers.

The future of the media and entertainment industry is largely centred on the application of data science and analytics to conceive path-breaking concepts, case in example being the recent blockbuster by Netflix, Bandersnatch. The one and a half hour show rendered the best of minds deeply bewildered with its ingenious concept of switching the show outcome according to the choices made by the viewers. This mid-stream personalisation of content was certainly an implausible concept, offering viewers the chance to affect the final ending according to their decisions. The entire spectrum of online media and entertainment space dove right into analysing and speculating the technology used behind it.

Use of data science and machine learning algorithms in media and entertainment have transformed the art of creating content into a scientific process. The creators are feeding on the user data and deriving minute insights that go into creating the most innovative of screenplays, scripts, ad campaigns, among other aspects of the media and entertainment industry. With the way creators and consumers are interacting with the content, it’s highly exciting to anticipate what else data science has the power to revolutionise.