Audience Research in 2023: All You Need to Know

Audience research is essential for understanding your target market. Learn all you need to know about audience research in 2023 and how it's evolving.

Shireen Noushad
March 24, 2023
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Around 5.6 billion people had access to the internet at the start of 2023, which accounts for about 64% of the population. Be it content consumption, for assignments, planning a road trip, or looking at movie reviews, nothing gets down without the internet.  

However great this may sound; it is challenging for those businesses creating content for consumption. With the internet being so widely used, there is no scope for a gatekeeper when it comes to consumption of TV shows, movies, teasers, trailers, or even advertisements. Media and broadcasting companies must ensure that the media they publish online is consumable for people worldwide.  

This is why Audience Research is more important now than ever.  

Need for Audience Research  

Let us get into the details of why media and broadcasting companies must get involved in audience research.  

Audience Trends 2023

Reduce Failure Rate  

Without audience research, there would be zero visibility into how well a TV show, teaser, or advertisement might do in the market. In today’s age of social media reviews, it is unwise to publish any media that is not pre-tested among a panel resembling the target audience. If the media does not resonate with the target audience, the social media backlash is unanimous and harsh.  

With audience research,  media and broadcasting companies can gain insight into how well their media performs or even tweak it if needed. This way, businesses can reduce their market failure rate with audience research.

Budget Allocation  

As mentioned before, internet and technology penetration has worked out well for the audience of this era. They can consume content/information from varied mediums – phones, tablets, laptops, smart TVs – and from varied sources – OTT platforms, YouTube, social media, etc. To get the best spend ROI, allocating the budget suitably when planning media is necessary.  

The only way to know a media’s performance rate with respect to the sources is to pre-test their placements. Once the media is pre-tested, the unbiased consumer insights will allow brands to make data-driven media planning decisions for the best spend ROI.  


While OTT and YouTube have a content lifespan ranging from 20-30 days, some social media platforms have trends decaying in a matter of hours. Ads made for these platforms must consider the lifespan of the content and come up with plans to boost the content.  

To do so, media and broadcasting companies must have the infrastructure to churn out agile audience insights. Media content must be tested for various platforms at various touchpoints to ensure the content stays relevant. Relevancy is what encourages consumption. Using agile audience insights, businesses can be ready with their customized strategies to boost content.  

Have a Look: Top 5 Media Research Best Practices in 2023

Current Challenges in Audience Research  

Audience research is not a new concept. However, the way audience research has recently undergone drastic changes. However, not everyone has caught up.  media and broadcasting companies still rely on traditional audience research to drive their media planning. Let us go through why this is not optimum.  

Traditional Research Cannot Keep Up

Traditional research is a crutch holding back media and broadcasters from fulfilling their potential. In a world run on real-time data, traditional research methods are time-consuming, not to mention cumbersome. Collecting data from respondents in one thing. Researchers must clean the data by sorting and filtering out the unnecessary ones – then analyze it and develop insights. Additionally, all this data is stored in different sources in different formats. Cleaning the data in itself takes a long time.  

The absence of a single source of truth is problematic for decision-makers when they require visibility into the audience’s responses. There is also a lack of actionability that comes with inadequate quality insights and a half-baked insights repository.

Biased Feedback  

Traditional research is highly analogue. It is a pen-and-paper approach to audience research where the audience is asked questions in groups or in face-to-face interviews. In such cases, respondents often feel the pressure to answer in a certain way, thereby diluting the authenticity of the response.  

In a traditional research environment, responses are not a true reflection of what the audience feels and thinks. Implicit feedback is almost impossible to procure in traditional research. With 90% of the decision-making process being subconscious, businesses cannot depend on these responses to make media planning decisions.  

Also Read: How to Reduce Bias with AI-led Behavioral Research  

Changing Viewing Patterns

Storytelling and consumption have gone through many phases. From books to movie theatres to OTT platforms – consumption patterns have been on a constant change loop. In tandem with viewing patterns, media publishers are also expected to keep up with the changes as well as the latest trends.  

In traditional research, the research alone takes around 4-8 weeks to complete. Then there is the whole process of deriving insights and implementing these insights into GTM (Go to Market) plans. By the time the GTM plan is executed, the audience insights collected, let us say around 10 weeks prior, would be redundant. Traditional audience research can no longer be of use in a fast-paced market.  

Lack of Research Reach  

One of the reasons traditional research projects are inaccurate and biased is owing to their method of data collection.  media and broadcasters involved in traditional methods have access to limited respondents.  

When responses are limited, researchers and marketers cannot accurately plan their GTM strategies. This puts businesses at the risk of disappointing their audience, which would eventually result in a backlash and a decline in spend ROI. Both of which are unsavoury for businesses.  

To mitigate such challenges,  media and broadcasters must adopt research methods that promise agility and, thus, future-proof. The solution lies in AI-led consumer research.  

Also Read: A Guidebook to Online Panels for Surveys

Audience Trends 2023

Emerging Audience Research Trends in 2023

As AI penetrated through to audience research, there are a couple of trends that have peaked in the recent times. Here are the trends in audience research that media and broadcasting houses need to be aware of, in 2023.


Geo-specific marketing and targeting no longer work for movies, TV shows, and ads anymore; except for movies that release in “nearby” cinema halls. But for all content that goes up online – on the myriad of OTT platforms, social media, video streaming channels, etc. Content will find its way across the world even if businesses intend it for a certain segment of the population.  

Therefore, it is essential for media and broadcasting companies to be inclusive when pre-testing their content. Respondents must be from across genders, ethnicities, countries, and communities. Businesses can then identify if their content was offensive to any group of people or indulged in negative stereotyping and portrayal of another group. Content must be crafted in a way that all enjoy it. In short, when it comes to audience research, the panel of respondents must be inclusive.  


With audience constantly on the move, content consumption happens of smaller screens. Mobile phones are heavily used for content consumption – on OTT platforms or YouTube. Social media content rarely gets consumed on desktops these days.  

From the throng of Netflix users alone, 30% use their smartphones or tablets to consume content. Owing to such high margin of mobile consumption, the smart thing to do is device audience research that is mobile-friendly. Mobile responsiveness must take the forefront when it comes to setting up audience research processes.  

Also Read: How to Handle the Choice Conundrum in OTT with UX Research

AI and D-I-Y  

D-I-Y or Do It Yourself is not recently coined or a new phenomenon. D-I-Y has a long and complicated history dating back to the late 1600s, when Joseph Moxon published his book –  Mechanick Exercises, the grandfather of all modern DIY manuals. It outlined how to cast metal, engrave wood, print books, etc.  

As the world evolved, so did the scope of D-I-Y. In the coming years, businesses will be encouraged to do audience research in-house. This is possible with the emergence of AI-led audience research platform that takes care of end-to-end audience research automation. With cutting-edge technologies like facial coding, eye tracking, and voice AI – media and broadcasting companies can take up audience research in-house. What’s better? The audience insights derived from the audience research platform will be far superior to any external insight supplier.  

Advantages of AI-led Audience Research  

Although the benefits of adopting AI-led audience research were sprinkled throughout this article – let’s dive a little deeper into the advantages for media and broadcasting companies on adopting AI-led audience research.  

AI-led Audience Research

Reduce Risk of Investment  

House of Dragons, the prequel to the massive hit series – Game of Thrones, costs 20 million USD an episode. Avatar 2’s production budget alone was around 450 million USD. In 2023, advertisers are paid average of 7 million U.S. dollars to air a 30-second commercial during the Super Bowl broadcast.  

It is up to the decision-makers to ensure good spend ROI on these numbers. With audience research, it is possible.  

Testing segments of a movie, a TV show, or an ad with a representative audience can arm decision-makers with the actionable insights on the performance – whether it will bring in the expected viewership. Or convert to purchase intent and sale numbers in the case of ads. With in-depth audience insights, media and broadcasting companies can make data-driven decisions on media planning and allocate their budgets across platforms and mediums accordingly. This way, there is no guesswork involved in the spend ROI.  

Identify Strengths and Weaknesses  

Shows, movies, and ads have different points of interests across the length of the content. Audience tends to remember certain characters or elements from the content more clearly than others.  

With facial coding and eye tracking that comes in the picture, businesses can accurately identify audience interests. Well liked characters can be given more screentime. Products can be placed in segments with high area of interest, logos can be placed where people would notice it, so and so forth.  

Elements in the content that negatively impact the overall attention and engagement can be edited out as well. Therefore, with AI-led audience research, media and broadcasting companies can capitalize on the strengths and remove their weaknesses from the content before they go live to the public.  

Spot Trends  

One of the features of an AI-led audience research platform is that of a single source of truth. This goes on to show that audience insights collected on previous content types will be available and up for analysis.  

Digging deeper and comparing audience insights over time, media and broadcasting companies can identify trends that pop up.  There could be an affinity towards a particular colour, a type of storytelling, or even a shift in viewing patterns.  Analysing audience insights, predicting and identifying shift in trends could help businesses prepare themselves with content that would fit the upcoming trends.  

Keep Your Audience Engaged  

Moreover, businesses can retain their audience once they have access to agile, accurate, and actionable audience insights.  

With an AI-led audience research platform, media and broadcasting companies have access to agile insights, which tells them about audience preferences continuously. Research projects are run parallelly, one after that other, so audience intelligence is deeper. This way, businesses have access to laser sharp insight into what exactly the audience wants. Delivering on audience insights and catering to their interests is essential to keep them happy, engaged and ensure retention.

AI-led Consumer Research for Broadcasters  

As AI adoption grows across industries, it has seeped into audience research – promising a fruitful future for those who use it. Let us have a look at how AI-led audience research platforms help solve the dilemma of slow, biased, and inaccurate research insights.  

Agile Audience Research  

Agile audience research relies on iterative processes that analyze existing business practices to address the problems that stem from them – in a continuous manner. It involves adopting the latest technologies to enable continuous feedback collection to churn out continuous audience insights, reducing the research time by four times. AI-led audience research platforms are agile in nature, empowering research teams with efficient end-to-end audience research.  

Adopting an agile approach to audience research, decision-makers in these businesses have the luxury of making data-driven decisions. The absence of manual intervention also assures the insights team that there is no margin of error. Additionally, once audience research is agile, media and broadcasters can utilise the agile audience insights to modify their publishing efforts as viewing patterns change. Who wouldn’t want to adopt agile audience research?  

Have a look: Making Sense of Consumer Research Data with Smart Analytics

AI Technologies  

Doing audience research is essential to media and broadcasting agencies. However, there is the case of biased feedback on account of unstated responses. Respondents may say one thing when they are in fact feeling the opposite.  

  • With AI technologies such as facial coding, media and broadcasters can gain visibility into what their respondents feel by decoding their facial expressions into universally recognized emotions.  
  • Eye tracking is another up-and-coming AI technology which tells the researchers with pin-point accuracy what the respondents are looking at. Eye tracking can help businesses analyse elements such as characters, creative components, brand elements and so on.  
  • Voice AI can breakdown the intent behind conversations in focus group discussions. It empowers audience research with sentiment analysis by examining the shifts in voice tonality.  


Audience research does not end once the insights are in. With agile audience research, gathering insights and incorporating them into decision-making is an iterative process. To aid the pace of the process, decision-makers must spend less time identifying the call to action within audience research reports.  

With AI-led audience research, the feedback collected is reflected on an easily consumable dashboard – thanks to data visualisation and storytelling within the dashboard. These dashboards come with actionable audience insights, making it easier for decision makers to identify the call to action and take the necessary steps to improve ROI.  

Also read: Data Visualization and Storytelling Making Lives Easier for Researchers & Marketers

Thanks to AI technologies, heatmaps and eye gaze maps tell decision-makers what works and what doesn’t. The heatmaps and eye gaze maps gives insights into what the audience was looking at, what they felt when they looked at certain characters or props, and what as ignored. With this information, media and broadcasting houses can make creative modifications, improve the narrative, adjust characters’ screen time, etc.  

Single Source of Truth

Traditional audience research consists of planning, crafting research questions, collecting audience feedback, cleaning it, analysing it and then reporting it. In traditional research, all the above are manual or using different tools. Using technology to fast-track the process does not always solve problems. In this case where different tools are used for each process; audience feedback tends to be scattered and even if different formats.  

An AI-led audience research platform enables media and broadcasters to conduct end-to-end audience research. As it is capable of end-to-end audience research, the platform acts as a single source of truth. All stakeholders in the project have visibility into audience insights in a single place.  As audience research is agile, decision-makers can also view audience insights of previous projects, compare it to the current ones and make highly accurate data-driven decisions.  

AI-led Audience Research is the Future  

AI-led audience research platform is a powerful tool that media and broadcasting companies can leverage to gain valuable insights into their target audience. With the help of AI-powered algorithms and advanced data analytics, companies can collect and analyze vast amounts of data to understand audience preferences, behavior, engagement, and viewing patterns. This enables them to make data-driven decisions, tailor their content to meet audience needs, and optimize their GTM strategies.  

Moreover, AI can assist in predicting future trends and anticipating changing audience demands. Its potential to enhance the audience experience and improve content performance makes it a valuable investment for media and broadcasting companies looking to stay ahead in an ever-evolving industry.  

Supercharge your research with actionable insights faster on Decode's integrated consumer research platform with Insights AI.
Want to conduct lean and unbiased research? Try out Entropik's tech behavioral research platform today!
Want to conduct lean and unbiased research? Try out Entropik's tech behavioral research platform today!
Want to conduct lean and unbiased research? Try out Entropik's tech behavioral research platform today!
Build the Right Products, the Right Way: Elevate your UX with Qatalyst's integrated user research platform with Insights AI.


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Author Bio

Shireen Noushad
Shireen adores writing and currently, she’s all about making B2B content fun, engaging, and 100% non-boring. She's intrigued by the scope of AI(not scared, yet), and how it helps advance operations on all fronts across industries. On a typical day, she scours the internet for anything and everything AI-related, and goes on a loop of writing and re-writing cause it’s just not perfect.

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