Machine learning is able to tame enormous amounts of data – and now, more than ever, it can create increasingly sophisticated media at a fraction of the cost. Here’s how ML-powered video generation helps scale ad content production.
There are three typical factors that invariably drive up digital video production costs: Time. Talent. Tools/Technology. The more any of these three video production factors are needed, the more costs rise. It’s a relentless, often expensive process.
Enter machine learning (ML). A distinct subset of artificial intelligence (AI), ML uses learning algorithms to build models of understanding regarding relationships between existing data, so that precise predictions can be made for new data. In effect, ML builds on ML in a never-ending loop of increasingly sophisticated self-learning.
So, why is ML such a huge boon for the advertising professional and, in particular, video production? It’s because these professionals are bogged down with terabytes and even petabytes of demographic data that can make deciding on how or what ad to produce overwhelming. Ad tech embedded with ML has the ability to self-learn and self-create, which has enormous and beneficial implications regarding how digital creatives are produced, generated and selected. In advertising, it can save hugely on the ‘3Ts’ – time, talent and needed tools/tech.
It was reported in June 2019 how a team of scientists at Google Research had been able to create AI-generated videos that had “unprecedented complexity,” to the surprise of even the scientists. This was just a few months after it was shown by a team from the Massachusetts Institute of Technology (MIT) and Nvidia that a wholly synthetic 3D gaming environment could be generated by a form of ML that had been ‘trained’ on videos of real urban landscapes.
The technology has improved even more in just two short years. Now, advertising professionals can get super-smart video generation software that enables ML-generated videos that are tailored to their unique business needs with the purpose of converting audiences – and all at a fraction of the cost, time and hassle it usually takes to produce videos.
The beauty of ML-generated videos is the sheer simplicity of the process. So, for example your ML video generation adventure could go like this:
Follow this link to watch a quick demo and see how simple it is to generate new videos using the Genus AI Growth Platform.
Every advertising professional has to justify their digital ad spends; providing objective justification for the performance of a given campaign can be especially difficult to do. Analytics just doesn’t cut it for many marketing professionals, with an October 2020 survey by Gartner showing that a majority of CMOs were unable to quantify the relationship between insights provided by analytics and actual bottom line/cost considerations. Machine learning can greatly assist with improvements across all ad tech-related metrics, whether quantitative or qualitative.
In advertising lingo, the simplest metric to maximize for any video is its click-through rate (CTR), i.e. the ratio of clicks per number of times an ad is displayed, aka impressions. ML-generated video software will inform the user of the potential impression rate per generated video. It’s well-known that digital ads have notoriously low conversion rates, which is why even the slightest difference in impressions can have a significant overall impact. For example, if an ad is able to generate 0.2% more impressions per 1,000 impressions than another ad, that may result in $2 more revenue – nothing special. However, at a million impressions that mere difference of 0.2% more impressions can equate to $2,000 more revenue.
Extrapolate those subtle yet powerful differences across multiple generated ads, and the impact on revenue can be huge. Furthermore, ML software may make use of reinforcement learning (RL), whereby ads are located that users are more likely to click on.
Return on ad spend (ROAS) is an essential marketing metric that measures the amount of revenue earned per each dollar spent on advertising, akin to the better-known return on investment (ROI) metric. As already stated, big data can be immense and overwhelming and ML can greatly assist in making advertising sense of it. ML-generated video production lowers production time and technology costs, resulting in instant and direct ROAS gains. Furthermore, intelligent use can be made of platforms such as Facebook that actively use ML in order to improve campaign success rates on their own platforms – and with Facebook that means cross-promotional advertising scaling up across other platforms it owns, such as Messenger and Instagram.
Cost per acquisition (CPA) is also important, given that, using AdRank as an example, acquisition bids are not based on whether you bid highest but, instead, on factors such as keyword relevance, user experience and the all-important CTR. Bottom line: more than ever, content has to be engaging in order to score well if CPA is chosen. Once again, ML-generated videos are scored according to potential success rates, thereby resulting in less ‘shot in the dark’ CPA efforts than usually occurs with traditional video ad campaigns.
In 2011, Google coined the term “Zero Moment of Truth,” that all-important customer’s first step in deciding whether to commit to a product or service. That ‘digital path’ is more crowded and competitive than ever, which is why video ads need to be as on-point and cost-effective as possible. ML-generated videos achieve just that.
Remember, machine learning is able to tame enormous amounts of data – and now, more than ever, it can create increasingly sophisticated media at a fraction of the cost. What digital marketing professional can say no to that?
You are in luck: it has never been easier to start scaling your ad content production with AI. Get a free 7-day trial of the Genus AI Growth Platform and start generating your ad videos today!