The Dentsu ad spending report forecasts global advertising spending in 2023 to be around $740.9 billion. Streaming platforms are set out to spend around $32 billion this year – a Wells Fargo report. Additionally, the media industry, in 2023, is laser-focused on optimized delivery and better placement of ads, shows, movies, and other broadcasting content.
With this new and improved approach, it only makes sense for media companies to expect an improvement in ROI. However, that is only possible if these companies are on-board with audience expectations this year. Media companies must have a clearer understanding of their audience ’s likes, dislikes, the market trend, and where they stand with competition – to overcome them.
The above is only possible through deep media and audience research. Testing media by presenting them to a selected audience before going public will help businesses set accurate spend ROI expectations.
Best Practices for Media Research and Analysis
To get the ball rolling, we’ve put together a list of best practices to adhere to when it comes to setting up audience research on media content.
Have a look: Behavioral Research for Ad Testing
Inclusivity in Audience for Media Research
Thanks to democratization of the internet and technology, the world is becoming increasingly accessible with every passing day. Owing to this, there are different communities of people watching the same content.
This calls for inclusivity when it comes to arranging a panel to test media content, be it ads, TV shows, movies etc.
- Media content must now be tested with inclusive panels that bring together people from different races, backgrounds, genders, and ethnicities.
- With accurate insight into how these groups of people react and respond to the media, marketers and researchers can tweak or predict the spend ROI from it.
The future is inclusive, and it is time media and broadcasting companies hopped on the ride.
Agile Insights for Accuracy in Media Research
Media and broadcast companies must keep up with the pace of the internet, where there’s a new trend every day, and audience expectations keep shifting. Traditional research cannot keep up with it, with their 8-12 weeks insight delivery timeline. Agile audience insights allow companies to be faster and more extensive, churning out insights in a matter of 1-2 weeks.
Agile audience research is where research meets automation. Additionally, in an agile audience research process, the larger scope of the study is split into segments to get deeper audience insights.
- Media and broadcasting companies must set up audience research infrastructure and process flow that generate agile insights
- These insights must then be combined to get an accurate and holistic picture of audience feedback.
Agility in audience research helps media companies stay on top of their audience expectations as well as skip ahead of competition.
Mobile-first – The Future of Media Consumption
There are currently 6.8 billion mobile users across the world, which suggests an 80% penetration given the global population to be 8 billion.
- Audience research projects should involve mobile-first testing infrastructure for the same.
- Audience insights on media content consumption on mobile will give clarity to media and broadcasting companies on how to allocate their spend and the spend ROI they can expect from mobile users.
The average person spends around 4 hours on their phones every day. Media and broadcasting companies will do well to capitalize on this.
In-house Media Research
The audience research industry is slowly but steadily moving towards in-house audience research. This takes away dependency on agencies that deliver slow, inaccurate insights that lack actionability.
- Media and broadcasting companies must investigate and start using integrated audience research platforms that support both qualitative and quantitative studies. This way marketers can get a comprehensive picture of their audience.
- These DIY platforms promise a quick turnaround and assured quality in audience insights.
AI-powered Audience Research
With AI and ML spear-heading the fourth industrial revolution, it is not surprising that these technologies have made their way into audience research.
- AI-powered audience research platforms include technologies such as facial coding, eye tracking, and sentiment analysis via voice AI.
- These technologies empower media and broadcasting companies to tap into unstated audience responses and get accurate feedback on their media content.
These unstated audience responses can help companies measure the performace of the media content. It can measure their eye gaze patterns, engagement and attentiveness to the media, and their emotions associated with it.
Time to Adopt AI-powered Audience Research
Our DIY AI-driven integrated audience research platform comes with
- Online survey capabilities that can recruit inclusive panels from across the globe
- End-to-end automated audience research process. With minimum human intervention, our platform empowers media and broadcasting companies with accurate and accessible audience insights in 1-2weeks.
- Actionable dashboards that are easy to use, consume, and distribute.
- Special mobile testing capabilities where audiences can use their phones to test media content. With the consent of the individual, their facial expression and eye movements are captured to understand attentiveness and engagement.
- Intuitive UX & UI, which allows media and broadcasting companies to conduct audience research in-house, saving costs and time spent on agencies.
With 2023 in full swing, there are new shows and movies coming out on OTT platforms almost every week. Some of these media content – such as movies, TV shows, or prime-time broadcasts fall flat. Sadly, with social media in play, those who produced said media content is not let go easily. There are multi-channel conversations on everything that went wrong – wrapped in hashtags.
With the advent of AI-powered integrated audience research platforms, media and broadcasting companies can say goodbye to guesswork. They can now make data-driven decisions on spend allocation, distribution, and placement.
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