User behaviour data is becoming a staple in the media and entertainment industry. It helps companies understand audience insights and make more personalised content. User behaviour data is not just about collecting metrics; its most important role is turning them into actionable insights that can boost revenue. So, let’s see how big data is influencing digital entertainment and any future trends to be aware of.
Understanding Behavioural Analytics in Digital Entertainment
Behavioural analytics is a technique that helps digital entertainment companies understand how their users interact with their platforms. It helps them understand various aspects, including:
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What they like
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Which issues they might encounter
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What keeps them interested and returning
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Whether their behaviour and preferences change over time
Types of Data and Analytics
It is also worth noting that there is a variety of data analytics these companies use. All of them serve varying purposes and present several types of forward-thinking actions:
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Descriptive analytics: A detailed view of historical data that can help digital entertainment companies understand past trends and behaviours.
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Predictive analytics: They use statistical models and forecasting tools to try to predict future outcomes.
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Prescriptive analytics: Analytics data that determine actions that can lead to certain results.
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Big data: Can process large volumes of data in a much more structured matter and identify complex patterns that humans may not be able to notice.
Use Cases of User Behaviour Data and How Companies Monetise It
Now that you know what user behaviour data is, let’s consider some use cases in the digital entertainment industry and how it helps its economy.
Predicting the Audience Interest
Entertainment websites, media outlets, and streaming platforms can greatly benefit from big data analysis. These seek to have the necessary materials to cater to every viewer’s preferences and offer much-needed content diversity.
User behaviour data gives these platforms classified data, which can assist them in predicting users’ interests.
In online gaming, for example, this is how high engagement slots come to life. Developers pay close attention to how players respond to things like bonus rounds, volatility, and visual design. By understanding what keeps people hooked, they can create titles that are genuinely fun and exciting. These insights also help gaming platforms recommend the right activities to the right users at the perfect time.
Monetisation
Data analytics can help companies create more enhanced monetisation strategies. By enabling more personalised user experiences, platforms can expect subscription growth and ad revenues. New revenue opportunities can also arise in the form of data-driven merchandise and targeted promotions.
Recognise Cancelled Memberships
All companies have their membership prices, which can sometimes be updated or amended. Here, user behaviour data can help companies understand which customers are devoted, but more importantly, why certain ones decided to unsubscribe.
There might be cases of reoccurring push notifications and CTA requests that irritate users and decide not to renew. Entertainment companies need to pay attention to this and check whether they “spam” their users, thus changing their content in response to current demand.
Advertising
The primary factor that determines the value and profitability of a digital entertainment company is advertising. Here, user behaviour data can help assist in analysing user preferences and what they are likely to buy through targeted ads.
Not only does it help companies create content-related ads, but big data can also enable them to develop effective strategies based on numerous factors – location, timing, second-screen usage, and so on.
User Behaviour Data Challenges Entertainment Companies Face
While the integration of user behaviour data can lead to big profits for companies, it comes with its own set of challenges. So, here are some hurdles entertainment companies will need to overcome if they are to successfully use user behaviour data:
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Regulatory landscape: Regulations such as the GDPR and CCPA require companies to be extremely cautious about how they handle user data.
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Data breaches: Anything that is stored online is a potential target for cybercriminals, so companies need to use robust security features to safeguard sensitive data.
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Data quality: Companies must ensure the reliability and accuracy of data sources, while data cleaning is also a necessity if the quality is to be maintained.
A New Era of Entertainment
The role of big data and analytics in entertainment is set to become even more influential in the coming years. The rise of technologies such as AR and VR will present more opportunities for interactive and entertaining experiences, which can also be powered by insights from user behaviour data.
