Data Analytics in Media Industry: Key Benefits and Use Cases

Data Analytics in Media Industry FI

How do you separate yourself from the competition as a media company?

You use data to lend that personal touch to your service, making you that much more memorable to your customers.

Here’s an example.

Spotify CMO Seth Farbman said that Spotify Wrapped was created to help people remember how music impacts their lives through their data.[2]

But what’s going on under the hood?

Spotify Wrapped used data visualization to transform raw listener data into a personalized story that recounted every user’s listening habits over the past year. But Spotify Wrapped didn’t just stop with a simple recollection of the songs they listened to the most.

They also gave them “badges” that depicted certain accomplishments — for instance, if they listened to a hit song before anyone else, they were given a Pioneer badge.

And using data to deliver a more personal touch has worked wonders for Spotify; in 2021, Spotify Wrapped was shared 60 million times by users![3]

In this article, we’ll go over how companies like Netflix, EA Sports, and Disney are using data to offer amazing experiences to their customers and stay a step ahead of their competitors.

Here are a couple of benefits that you can get through data analytics for the media industry:

Improve Content Acquisition, Development, and Monetization: With media analytics, you can discover key trends in listening or viewing habits. A keen understanding of the shifts in consumer preferences would allow you to guide content development, acquire new viewers more easily, and help media executives monetize content more effectively.

Enhancing Viewership: Media companies can analyze customer data to optimize their content scheduling and program offering. The native data collated on types of podcasts, videos, or music accessed by individual consumers can be fed to recommendation engines that provide suggestions based on these interests. For accurate recommendations, these engines might use content format, content length, genre, niche topics, content medium, and specific content creators.

Scope Out Newer Markets: Media analytics can also be used by companies to penetrate high-potential markets to increase their customer base. This would involve using algorithms that use similarity scores to help you uncover markets that are similar to your current audience, indicating a possibility of your content being relevant to the untapped market as well.

Enhance the Effectiveness of Your Advertising Efforts: To achieve a high ROI, you need to possess the ability to accurately track ad campaign performance. Media analytics can be crucial in determining the impact of factors like page format, ad type, ad location wrt different page elements, page dwell time, scroll parameters, and page interaction parameters like clicks to name a few. Advertisers can also analyze customer data from on-platform and third-party sources to accurately understand customer sentiment about current content offerings. Media companies can also better monetize the value of their digital ads through ad targeting and tighter segmentation across visitors coming from third-party websites and social media.

Predict Box Office Success: For the film industry, you can use data analytics to predict if a film would perform well at the box office or go bust. For accurate prediction, you can gather information through social media monitoring, pre-release surveys, and website traffic analysis. Studios like Warner Bros can use this data to give projects the green light or shut them down for good.

Some statistics that’ll put the importance of data analytics for the media industry in perspective:

The video streaming service market was worth $455.45 billion in 2022 and is projected to grow from $554.33 billion in 2023 to $1902.68 billion by the end of 2030.

Netflix and Disney got close to 1.3 billion new subscriptions in 2021, a 14% increase over 2020.

That’s a lot of people. It’s a big enough pie.

Let’s go over how data analytics can help you get a bigger slice of the pie:

Making a successful TV Show is no easy feat. Here’s a graph of over 2,500 shows mapped to their IMDb ratings:

Shows with a rating of 9 or higher land in the top 2% of shows across the globe. These shows join the ranks of “Breaking Bad”, “The Wire”, and “Band of Brothers” among others.

Roy Price was the man tasked with coming up with a hit show for Amazon Studios. He was primarily worried about creating a show that lands under the ‘bulge of average TV’ in the graph. These are the shows that aren’t really that good or bad.

To engineer success, he chose a bunch of ideas after holding a competition and evaluated them to get the number down to eight ideas. He created the first episode for each, put them online for free, collected millions of data points, and created “Alpha House” – a sitcom about four Republican US senators.

However, despite what the data dictated, they ended up with a pretty average rating — 7.5 on IMDb.

At around the same time, Ted Sarandos, Netflix’s Chief Content Officer, also used data to find that great TV show.

But he went about it a little differently.

Ted and his team looked at the data they already had about Netflix viewers like:

Ratings given to shows

Likes on Shows

Viewing histories and much more

Types of producers, directors, and actors people liked

They used all these data points to create a drama series about a single senator.

House of Cards.

Well, at least in the first two seasons, when they got the rating they were vying for — a solid 9.1!

The difference of 1.6 points lies in choosing the right information. Netflix took specific information from specific demographics to come up with a show that was a hit with said demographic, while Amazon went broad in their experiment.

They took information from everyone, and without a specific demographic in mind, came up with an average show.

Takeaway: Producers can collect data on the demographic factors of their target audience to craft content that resonates with them. This helps ensure storylines, themes, and characters align with viewers’ preferences and interests.

For Youtube videos, getting people to view your content is dependent on various factors:

Audience Demographics

Traffic Sources for Your Video

Content suggesting your video, external apps and sites embedded in your video, search terms leading to your video

Thumbnails

Let’s go over the impact of thumbnails on your Youtube strategy.

Viewers can decide which videos they want to watch when browsing through Youtube based on the information and impression they obtain from thumbnails. A thumbnail could either be a frame taken from the video, or it could be a customized image that represents the content of the video.

Even though Youtube provides thumbnail choices from the video itself, close to 90% of the top-performing videos use customized thumbnails instead of the suggested thumbnails. Anecdotal evidence suggests that optimized thumbnails increase the click-through rate of videos by 15%.

Here’s how Vireo Video used TubeBuddy to carry out split testing on thumbnails for Hayden Crabtree.

But, that’s not all. You can also use social media analytics for:

Competitor Analysis

Analyzing Content Performance

Media companies can use social media monitoring tools to track consumer perceptions of their brand and their competitors.

A metric you can look to track here would be Social Share of Voice (SSoV). Social Share of Voice measures how many people are talking about your brand as compared to your competitors.

You can calculate SSoV by first adding up every mention of your brand on social media. Next, you need to do the same thing for all your competitors. This is what the formula for calculating your SSoV would look like:

(Brand Mentions/Total Mentions)*100 = SSoV percentage

Total mentions = Your mentions + competitor mentions.

Media companies can use audience insights tools to access detailed psychographic, demographic, and behavioral information about the groups they’d like to reach. They can explore how the likes, dislikes, habits, and values of their audience change over time, using these insights to create content that is tailored to their target audience.

Using audience insights is the key for media companies to better engage consumers on social channels and amp up the ROI of their marketing campaigns.

Key engagement metrics that you can measure to gauge your content performance are:

Amplification Rate: The more shares your posts get compared to the number of followers you have, the higher your amplification rate. When your amplification rate is higher, it means more of your followers are helping to spread your message.

Virality Rate: In layman’s terms, the virality rate measures the exponential rate at which your content is spreading across the internet. This metric is similar to the amplification rate since they both measure how much your content gets shared. But, virality rate will measure how many times your content is shared as a percentage of impressions as opposed to a percentage of followers.

Engagement Rate: It measures the number of engagements your content gets as a percentage of your audience.

A primary source of revenue for media companies is the ads they run. Media analytics can be used to create a recommender-like solution that provides context-based choice and placement of trailers and commercials within the streaming videos.

This kind of solution would combine audio-based image captioning models along with object-detection models to set up a semantic and syntactic context for every frame in the video.

To do this, a data analytics agency SG Analytics:

Extracted the frames from videos based on the similarity score indexed by time, followed by object detection to extract features, where a deep convolution neural net over Yolo V3 was built using transfer learning.

Applied the pre-trained weights of deep search to pull audio features present in the video and ad content.

They finally combine the video and audio features while calculating the similarity score using the BERT algorithm. This allowed them to provide the best spot to place a contextual ad in their content.

This translated to a 20% CTR improvement for the client, allowing them to monetize their ads more efficiently!

EA Sports, one of the world’s largest software gaming companies faced several challenges when they depended heavily on intuition for decision-making:

Short product life cycles

High-volatility, high-risk, high-reward development cycles

Over-reliance on popular products

Premium on creative quality

Laura Miele, Senior VP of Marketing realized that this dependence on intuition had served them well, but the advertising performance had plummeted. Owing to its core audience spending more time online, EA Sports decided to retool its marketing analytics by applying a new allocation, optimization, and attribution framework to its entire portfolio of games.

As opposed to traditional methods of measuring advertising performance like media-mix models and customer surveys; EA went through each phase and tweaked its strategy which marked a shift in ad investments from TV to search and online video.

This improvement helped them make Battlefield 3’s launch the most successful launch they’d ever seen, bringing in 23% more sales of Battlefield 3, as compared to previous versions of the game.

With a steady surge in the number of social media users, it is more than necessary for brands to tune into their audience’s conversations.

This is called Social Listening.

Brands can uncover valuable insights through social listening and adjust their social media marketing strategy accordingly. Social listening can also come in handy to discover emerging trends based on your audience/prospect conversations, viral videos/topics, and other information that can come in handy for creating content and messaging that’s relevant to your audience.

You can use social media monitoring tools like Brandwatch and Sprout Social to mine text for specific keywords on social networking websites, discussion forums, and blogs.

These tools would transpose specific phrases or words in unstructured data into numerical values connected to the structured data in a database. Now, you can analyze the data using traditional data mining techniques.

Netflix has been one of the top players who’ve been using social listening to connect with its users, develop long-lasting relationships, and come up with new solutions.

Case in point, the Netflix Socks.

Because Netflix has positioned itself as this audience-friendly brand, it actively listens and responds to online chatter. Through these measures, Netflix discovered that many users were falling asleep while binge-watching shows only to wake up several episodes later.

Netflix developed Netflix socks to tackle this issue. It’s a pair of smart socks that can sense when the user is dozing off and then send a signal to the device to pause the show.

You can find audience data in a vast array of locations. Here are a couple of primary sources for mining data around your audience:

Website: For your website visitors, you can track content consumption and behavioral patterns to uncover insights on the paths prospects use to become customers.

SaaS Tools: CRM systems like Salesforce, customer service platforms like Zendesk, chat tools, and marketing platforms will give you an understanding of common pain points customers face with your product, common questions they pose, and places where they hang out and consume content around your brand. All of these insights will go a long way in meeting prospects where they are and providing personalized service, thus strengthening brand loyalty.

Brand Mentions: You can look up who’s talking about your brand on review sites, forums, and social media to see what’s on your audience’s mind. Sentiment analysis will help you personalize your service to your ideal customer’s needs.

In the interest of user privacy, third-party cookies are slowly being phased out by all web browsers. The loss of third-party cookies will have a major impact on the advertising industry will find it harder to run personalized ads.

This is where Data Clean Rooms come into the picture.

They allow advertisers to run personalized ads, report and measure the performance of campaigns, and more while being privacy-friendly.

A case in point would be NBCUniversal’s Audience Insights Hub.

It allowed the participants to design the level of transparency and protection that they felt was appropriate for activating campaigns and building audiences, among others.

The Audience Insights Hub was built on a cross-cloud data clean room environment and it improved interoperability between NBCUniversal and its advertising ecosystem partners by enabling:

Cross-platform Planning: NBCUniversal is combining the clean room environment with its proprietary Linear TV APIs. This provides self-service access to its partners who want to use NBCUniversal’s aggregate digital and linear data for cross-platform media planning.

Digital Audience Exploration: Partners can study the overlap of customers and audiences, which allows them to uncover valuable insights without exposing the underlying data from any sources.

Frequency and Reach Management: Audience Insights Hub also provided reach measurement models to partners. They can use these models to conduct their analyses of ad exposure data. Armed with the insights gained from these analyses, NBCUniversal’s partners could deduplicate campaign frequency and reach for more efficient media measurement and planning.

Disney Advertising Sales are responsible for integrated marketing campaigns and advertising sales through linear, social, digital, ad-supported, and audio-streaming businesses. By using the Snowflake Media Data Cloud, they launched an innovative new clean room solution powered by their first-party data offering Disney Select.

With Disney Select, marketers can choose their desired audiences from Disney’s extensive library of first-party segments for scaleable analytics and targeting.

Every segment in Disney’s library was built to meet their client’s needs like household characteristics, buyer behavior, and psychographics.

Media companies can use data to offer personalized experiences to their customers and stay ahead of competitors.

Companies like Netflix, EA Sports, and Disney use data analytics to optimize scheduling and programming, develop tailored content, penetrate new markets, and enhance advertising effectiveness.

Data clean rooms can also help advertisers run personalized ads while protecting user privacy.

In the increasingly AI-laden world, differentiation would boil down to personalization. This is where data analytics is proving to be a key asset to the media industry.

Data analytics can be seen as a ‘magic wand’ that helps you create personalized experiences for your audience. It also ensures that the content you produce is more in sync with what your viewers want.

The data analytics train has left the station with bigwigs like Netflix, Spotify, and EA Sports.

The big question you need to answer for yourself is will you be boarding the train? Hevo has helped companies like Ole Connect, Elevux, and Vertical City board the train and modernized their data stack.

Schedule a demo today to see if Hevo would be the right fit for you!

Resources:

Spotify Uses Real Customer Data to Build Personal Emotional Connections