Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Views Are Not Enough – How Deep Learning Is Transforming the Effectiveness of Digital Video Campaigns

Why is Deep Learning so useful in terms of digital video campaigns?

A major consumer trend over the past few years has been unprecedented growth in video consumption across the digital ecosystem. As a result, brand marketers are increasingly relying on video ad campaigns to expand their reach, and for good reason – recent research by WYZOWL found that 88% of consumers have been convinced to buy a product after watching an explainer video online. The same research revealed that 61% of the marketers surveyed believe that the number of views a video gets can be regarded as a key metric of success. However, this is a common misconception: in reality, success is determined by target consumers receiving the right content at the right time. By chasing total video views over viewer quality, brands are actually reaching too-wide an audience for the effectiveness of their digital video campaigns to be truly optimized. Thankfully, the latest innovations in ad tech mean that this situation is changing. 

In 2022, Deep Learning technology is transforming the way brands run digital video campaigns.

But, what is Deep Learning and how does it help to improve the performance of video campaigns? And what metrics should brand marketers be using to determine a campaign’s success?

Deep Learning explained

It’s increasingly important for marketers to understand the distinctions between Artificial Intelligence (AI), ‘classic’ Machine Learning, and the cutting-edge Deep Learning technology that is the focus of this article. 

In basic terms, AI refers to any technology (usually computer software) which attempts to simulate some aspect of human intelligence. Machine learning, meanwhile, is a subset and application of AI that includes algorithms that parse data, learn from that data, and then apply what they’ve learned to inform decisions within predefined parameters. Finally, Deep Learning is a subfield of machine learning that structures algorithms in layers to create an ‘artificial neural network’ that can learn and make intelligent decisions on its own, with only the desired objective as an input.

Classic ‘machine learning’ is not able to take decisions without human input to guide its actions and struggles to handle unstructured, granular or dynamically changing data sets. It is unable to ‘improvise’ to make performance decisions on its own.

By contrast, Deep Learning technology is capable of conducting its own decisions, needing only the desired objective as input. Based on neural networks akin to the human brain, Deep Learning algorithms can respond to real-time events, based on its knowledge of previous similar events. 

Deep Learning allows advertisers to maximize the efficiency of their contextual targeting strategies by making the most out of every single ad impression.

The technology can analyze masses of user information in fractions of a second and decide not only which ad creative to present, but also whether its display is likely to trigger the desired action.

Deep Learning tech can make literally millions of these decisions in a short space of time, something that is far beyond the capabilities of even the world’s best media planners. Our own research found that campaigns utilizing Deep Learning in this way are up to 50% more efficient compared to those using standard machine learning approaches. 

Related Posts
1 of 5,780

So, why is Deep Learning so useful in terms of digital video campaigns?

The right focus

As mentioned above, the video advertising market is growing rapidly but, unfortunately, so are the costs. Advertisers spend serious money on the production of video ads so they want to make sure their ads will be seen online by the right people. 

That’s where Deep Learning comes into play. By applying the power of predictions done in real-time, Deep Learning helps advertisers by picking the right time and the right context to deliver each ad, increasing visibility (the frequency at which your brand is seen by the consumer) and optimizing the cost per completed view (CPCV). 

CPCV is a metric for an advertising campaign’s efficiency assessment, but it is also an advertising pricing model – in that sense, CPCV advertising means that companies pay each time a video has been viewed through to completion. In many cases, it may be a better way to invest the marketing budget. 

This metric allows you to calculate how much you have to pay for presenting the entire video to a user. Information of this type can be used later to optimize your video campaigns towards the completed views rather than clicks and impressions (as those two don’t really tell you if you have reached a potential customer with your message).

A focus on CPCV allows you to learn where the weaknesses of your video advertising strategy are.

Do you have to make your videos shorter?

Or maybe improve their quality?

Does your targeting require optimization? 

With long-standing methods of targeting audiences (based on the use of third-party cookies) under threat from both privacy regulators and tech giants, the time is right for marketers to understand and harness the power of Deep Learning and contextual targeting to help optimize the performance and impact of video campaigns.

Recommended: B2B Companies Need Deep Learning “Therapy” to Overcome Modern Data Challenges

Comments are closed.