Embedded Advertising Tech: The Smart Enabled Creative Asset

Modernism is defined as, “a style or movement in the arts that aims to break with classical and traditional forms.” From the perspective of advertising and advanced media, I am referring to a strategy called: Mixed Reality – that means delivering content on one device while delivering adverts at another.

Consider the following scenario: Media Brand ‘X’ wants to offer insights that allows advertisers to measure the “real performance” of their ad campaigns, such as whether someone who saw an ad on their Connected TV walked into a retail location and actually converted a targeted promotion.

The Situation @ The Brands

CMOs are confronting a myriad of painful realities: The declining effectiveness of mass advertising, a proliferation of new media, declining trust in advertising, increased distribution channels and digital technologies that give users more control over their media time are only the most visible signs of stress.

When network TV was the preferred advertising medium of choice, marketers and the ad agencies serving them rightly focused on the massive audiences that tuned into the most popular shows.

Disrupters like Netflix and Hulu have moved from streaming videos in browser-only constructs to making content available on virtually any device – changing the way in which audiences watch TV, transforming the medium from appointment-based watching to one where viewers watch what they want, when they want, on the device or devices of their choosing.

But how, exactly, do advertisers understand and measure the impact – the performance of their ad campaigns? Marketers need a more inventive/rigorous approach to a fragmenting world.

Enter the Smart Creative Asset

A Smart Message is an intelligently enabled ad asset designed to be embedded inside of modern applications (or even an embedded HTML5 video) that can be extensively personalized for a particular recipient or campaign, and above all is trackable. Tracking URLs are not novel. So, what’s different?

Smart Messages allow for a mapping between an identifier and provides the ability to profile users behaviorally: providing real-time, unfettered access to multi-dimensional data on how users are interacting with your ad and insight on its related performance.

The benefits: ability to track the actual performance of campaigns in real-time, measure fulfillment, reduce ad loads by presenting relevant adverts at a time and device that affords the highest probability of engagement and improves media investment return.

Data is data — until you make sense of it. Smart Messages provide end-to-end transparency and innovative analytic capabilities.

Expand your audience

Audience insights are essential when it comes to audience development. Avid fans typically represent 10 to 20 percent of a brand’s user base but can drive 80 percent or more of the business value. Content efforts therefore must prioritize initiatives aimed at super-serving them.

Content preference is a segmentation problem and requires the use of clustering algorithms. We look at attribution over a range of media factors are mined from content metadata things: like genre, playing time, album name, release year, artist, actors, director, etc. as opposed to device factors. We want to understand how people’s demographic attributes affect their media preferences – These metadata also describe what users like about the content.

Every type of metadata provides a unique segmentation view – The ultimate goal is to be able to take a demographic profile, and target exactly what type of content they like to consume.

Leverage these insights to reach the right audience and increase, viewership, downloads, subscriptions, and more.

Re-Inventing Advertising: Creating a Mixed Reality Experience

The last decade has shown a change in advertising and the increasing need to diversify marketing strategies.

Advertising is more crucial than ever in many media companies, despite the old traditional ways no longer cutting it. A mixed reality experience is the today’s standard with numerous screens being available for people to watch content from.

Despite these changes, it hasn’t changed the fundamental system behind media advertising. You still need to advertise to make money. Plus, advertising is still expensive as it’s been for decades.

One thing also consistent is most viewers hate advertising, making it necessary to use different approaches in the multi-screen universe.

Defining the Mixed Reality Experience
If you’ve been working in media for a while, you likely remember the days when everyone would mostly watch the same things on one TV screen. Starting in the 1970s, the only competition to television was video games, especially families owning gaming consoles like Atari.

In those days, there were no means to advertise on such a format. Not until the cable era did advertising have a chance to expand beyond the old three-network universe.

By the 1990s, the internet started to change things, despite media advertising being limited there until well into the 2000s.

It’s only been within the last ten years where we’ve truly seen the mixed reality universe come about. With mobile screens, and even different forms of pay TV services, 21st century advertising has to change to a more targeted format.

Creating More Relevant Ads With Analytics
Your own media company may have some relationships with content distributors or various data technologies. Tapping into the use of analytics is more important than ever, because it’s here where you’ll be able to address advertising in a fresh way.

Gaining access to more targeted analytics is the key, something becoming increasingly possible thanks to the use of AI and machine learning.

Machine learning is going to study what the viewing habits are of your viewers and come up with a predictive model for what kind of advertising they prefer.

Keep in mind this works well even for those who hate advertising. With a smarter approach to placing ads, it might change the minds of those who don’t want to see a single ad when watching content.

Reducing Ad Loads and Making Them More Relevant
At stake here is being able to reduce how many ads you present in a given hour and broadcast ones with more value during specific times of the day.

Analytics will tell you when your viewers watch specific content during their most attentive moments. Having access to advanced analytics is going to show you exactly when your audience watches a specific type of content on different devices.

In other words, you’ll know when they’re watching something on their smartphone, a laptop, or on traditional TV.

What makes this so important is most people now watch content on all of these devices at once.

Reaching People on Myriad Devices
Some of you may think of mixed reality as adding VR and video games into the mix. These obviously should have serious consideration since a sizable portion of the population are using these as well on any given day.

Even so, standard devices from TV to tablets often integrate when it comes to watching content. Many people now use these together for both information and entertainment. For instance, social media continues being used by TV watchers for live commentary while watching something on television.

Because of this cross-screen sensibility, advertising needs to address all devices together. With transitional and simultaneous use possible, one targeted ad could appear on one device while content appears on another device later.

Integrating advertising in a thoroughly targeted way is the future of advertising. AI and machine learning will become the technologies that help design the analytics to get there.

Advanced Media: Innovation in Monetization Strategy and Audience Engagement

Big data analytics can help your company tailor content to the interests of your customers.

Some of you are perhaps old enough to remember when all media was confined to one screen (other than movie theaters). This collective way to watch TV gave advertisers a uniform way to reach viewers, outside of creating static ads in magazines or newspapers.

Only in the last decade has this started to shift after decades of uniform TV advertising. Now with new media, traditional broadcasters have to deal with numerous cross-screen technologies from streaming media to multi-screen environments.

As a person in media looking to monetize, how can you go about it using the right technologies like AI?

Monetizing From New Media Tools
We all know that streaming media is giving traditional TV a major run for its money in capturing viewer attention. With Netflix and Amazon at the forefront, you need to adapt to these formats if you want to make money with advertising.

The good news is Netflix and Amazon are fairly transparent on how they monetize and capture viewers. You can learn from them on how they deliver content and advertising in an online environment.

In the case of Netflix, they’ve monetized mostly on producing original programming like a regular network. Forbes even reported last year Netflix is looking toward consumer products soon to expand their earnings. Of course, with already billions rolling in, it might seem superfluous.

With Netflix and Amazon always gathering data from their viewers, you can see how important analytics are for them.

Using Big Data Analytics to Determine What Viewers Want
Earlier this year, Kissmetrics analyzed exactly what Netflix does to help shape their content for their loyal subscribers. It all comes down to big data analytics, giving them a prime model to go on in determining the whims of media viewers.

One thing Kissmetics notes is Netflix is at a major advantage over traditional TV because networks don’t use analytics like Netflix does. This gives you some insight into why streaming services are winning so solidly over regular TV.

Media companies creating content for traditional broadcasting needs to start dealing with advanced media and take metrics seriously to survive.

How Direct-to-Consumer TV Gathers Viewer Data
Another sign of changing times in media is the direct-to-consumer media market. Some major media corporations are already dipping their toe into this field, including Disney. Not long ago, Disney announced they’re starting a direct-to-consumer service focusing on sports first, then one with their own classic content later.

The trend is certainly leaning toward these type of streaming services, one designed to bring better targeting to a specific viewer niche. No doubt Disney will use the best technologies (like AI and machine learning) to gather further big data on their future subscribers.

Even though Disney is obviously one of the most powerful media companies in the world, the means of monetizing in advanced media also leans heavily on another powerhouse: Google.

Google’s DFP Platform and its Role in a New Monetization Model
Those of you who work in media should know about Google’s DFP platform (Double Click for Publishers). Their program allows innovative ad formats for use in every available sales channel.

By being able to create engaging and seamless ads on every screen, you have a new way to target your viewers in the advanced TV universe. Once again, Google creates a practical and easy method to help businesses reach customers. It’s not to say you shouldn’t play by their rules.

When you adhere to their guidelines, you’ll gain access to useful forecasting tools to ensure you’ll never overbook or undersell your ad inventory.

Visit our website so we can use technologies like AI and machine learning to help you find ways to monetize through advanced TV outlets.

Advanced Analytics Driving Strategy for Advertising Sales: Advanced TV

Online movie stream with mobile device. Man watching film on tablet with imaginary video player service.

Those of you who work in video advertising already know the technological shift taking place where the lines between TV and video blur. Even if you’ve already worked with analytics in this arena, it’s obviously a challenge to keep up with the latest technological changes.

The biggest area to explore now is advanced TV and all the options viewers use to watch content. Due to everything from connected TV to OTT apps, the way people watch content is more fragmented than ever. While some lament us not watching TV collectively in one place, it’s still imperative to gather granular data.

What kind of metrics should you scope out in each of these new TV sectors?

Behavioral Targeting

Trying to figure out viewer behavior is always a major hurdle, especially when people change their minds so often on what they want to see. For more advanced media advertising, though, you’ll have to dig even deeper into viewer behavior.

Using third-party data providers, you’ll be able to gain a wider picture of viewer behavior when taking from multiple sources like satellite TV, OTT (Over-the-Top) apps, or smart TV’s.

Reports are that 79% of advertisers and agencies plan to use advanced linear TV throughout the year for the above purposes. As a result, traditional TV advertising is on a major decline.

While you might think of Google and Facebook as the only ones into behavioral targeting, all other media companies are jumping onto the same boat. Out of all those, 56% of them are using behavioral targeting now for more significant ROI.

Areas to focus on here include purchase history and intent, general interest, web navigation history, and customer relationship management information. All of these behaviors can have varying data, depending on the type of media device being used. Using AI and machine learning for this aids in more accuracy.

Geographic Targeting

One thing you’ve likely tried to do is expand your audience on a geographic basis. With the world becoming smaller and other countries watching TV shows we only thought would stay in America or Europe, geographic targeting should become top of mind.

With addressable TV being another medium you’ll want to explore in extracting metrics, different geographic regions are perhaps watching the same live show. Using the addressable TV tactic, it’s possible to send different ads to targeted people collectively watching the same content.

You may want to target or limit ads to specific countries, states, postal codes, or DMAs (Designated Market Areas). Segmenting your analytics into regions like this helps you better understand the diversity of your audience and what their entertainment needs are.

Daypart Targeting

By limiting your ad targeting to specific parts of the day, you’re able to better target the people you know watch certain shows when at their most receptive. The targeting on this is always tricky, though, because advanced TV is so diverse.

Those watching at particular hours could end up bringing varied results considering some might watch at odd times, especially on mobile devices.

As Google points out, AI and machine learning are at the forefront of scoping out this type of ad targeting to better customize personalized marketing experiences. You’re now able to send ads to those watching in the evenings, overnight, or just on weekends.

Contextual and Technology Targeting

In these realms of targeting, you’ll work with relational metrics and broadcast ads based on how people watch shows. For contextual targeting, you’d base your ads on surrounding content from a webpage or a specific streaming device a viewer uses.

Technology targeting goes squarely on what device the person uses to watch content. Keep in mind this isn’t limited to device or service. It can also mean what browser they use or operating system.

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