How AI & Machine Learning Helps Facebook Ads and Google Ads?
Both Facebook and Google have their own massive advertising platforms that are used to distribute advertisements to millions of people on a daily basis. These platforms have a variety of features for many different use cases and situations. At the same time, neither of these platforms would be at the top of the market today if they were not fast enough when it comes to adopting new technologies – which is why both Google Ads and Facebook Ads are now provided with the help of AI, and this exact theme is the centerpiece of this article.
Introduction to Artificial Intelligence and Machine Learning
Terms such as “Artificial Intelligence” and “Machine Learning” have become extremely popular in the last few years, invading and revolutionizing all kinds of industries and processes. Advertising as an industry was never against implementing new technologies and methods into its workflows, so it is not that surprising to see some of the biggest advertising platforms on the planet adopting AI and ML in some way or another.
Even though this article is mainly about the influence of AI on the marketing industry, it is still essential to understand what both machine learning and artificial intelligence actually mean.
Artificial Intelligence, as the name suggests, is intelligence (the capability to perceive information, synthesize it, and draw conclusions from said information) that machines demonstrate, as the opposite of intelligence usually demonstrated by either animals or people. Some of the more common examples of tasks that artificial intelligence can perform are computer vision, speech recognition, and translation between languages.
Machine Learning, on the other hand, is a method that leverages data to improve the performance of a specific task or a set of tasks. It is a process of understanding and improving upon methods that are capable of “learning” – being capable of making predictions or decisions without a direct inquiry about said decision or prediction. These algorithms are created using data sets, often referred to as “training data”, or “sample data”.
It is a rather common assumption to claim that Machine Learning is a part of Artificial Intelligence, even though it is not particularly true. Machine Learning as a field of research started off as a part of Artificial Intelligence as an “umbrella term”, but Machine Learning actually got reorganized as a separate field of research as early as the 1980s, as soon as Artificial Intelligence efforts started to rely less and less on statistics in its approach.
The biggest difference between Machine Learning and Artificial Intelligence is in their approach – AI is interacting with the environment to both learn and take useful actions, while ML only has passive observations to learn from and predict possible outcomes.
With that being said, it’s not like all AI applications have only one specific way of changing or improving something like a marketing industry. As such, we are going to perform an overview of how two of the biggest advertising platforms (Google and Facebook) incorporate AI into their advertising-related tasks.
Usage of AI in Google Ads
Writing ads can be a rather complicated task for a professional of any level, especially since a lot of ad placements have a very limited number of characters allowed per ad in the first place. Each and every one of these ads has to be captivating and convincing to attract more potential customers.
Luckily enough, Google Ads changed quite a lot in recent years, with the introduction of AI being one of the most important changes. Now it has become quite easier to start advertising and to make your marketing efforts successful. There are several different areas in which Google Ads uses the help of AI to improve advertising performance:
Optimization Score as a metric seems extremely obvious in its nature – a general metric of performance for your entire Google Ads account, with an obvious explanation of 0% being the least effective and 100% being the most effective your account can be. There are three main types of factors that are taken into account when determining this score – recent history of recommendations, current trends in ads that you have working right now, as well as various settings and statistical parameters of your account in general and your marketing campaigns specifically.
Optimization Score has a great influence over a number of areas inside of your Google Ads account, including ad extensions, keywords, recommendations, AI Google ad copies, budgets, and so on. Google uses a combination of ML and AI to evaluate the current state of your marketing campaigns, while also offering various ways to improve said score.
There is also a separate metric that evaluates your individual ads instead of entire marketing campaigns, and it is called Ad Strength. It is a measurement of several parameters for each of your AI Google ads – quality, diversity, relevance, and so on. Having unique content for your advertising efforts is important, and one of the biggest goals of this metric is to improve your existing ads so that they could perform better. As with the previous example, Ad Strength also has its own recommendations for each and every one of your ads, offering a multitude of approaches to making your advertisement game stronger than before.
Responsive Search Advertisements
This particular ad format may not be out of its beta phase right now, but it does already offer some of the biggest ad capabilities available on the platform, with three headlines and two 90-character description placements combined with automated testing capability. This automated testing gives Google the capability to figure out the most capable combination of description and headline by rotating both of these parameters on a regular basis and evaluating the results of every swap.
These three advancements in ad creation are extremely effective already, but the capabilities of Google Ads AI do not stop here. Google Ads also offers you several ways to automate a number of tedious tasks such as bidding, extensions, and so on. We can go over these advantages in a bit more detail, as well.
Ad Extension Automatization
Google Ads has quite a lot of extensions that could drastically increase the potential of every ad type in specific situations. At the same time, there are quite a lot of these extensions, and manually adding them may be quite tedious in the long run. Luckily, Google Ads can now utilize the power of AI to add and enable specific extensions automatically whenever it sees that a specific extension can be used. Additionally, the owner of the account only pays for the extension in question if it was actually useful in terms of attracting a click from a potential customer – making a great feature even better than before.
Manual bidding is also a topic of the past since now Google gives its users the ability to perform logical bid adjustments while eliminating guesswork altogether – all thanks to the help of AI. There are several different automated bidding strategies that AI Google Ads can offer – with the ability to maximize conversions, target CPA, target ROAS, maximize clicks, enhance CPC, and more.
Last, but not least part of this list is Google Smart Campaigns – marketing campaigns created and managed by AI to boost customers’ existing pay-per-click campaigns while also demanding minimal effort from the account owner themselves. Smart Campaigns can generate text-based AI Google ads based on nothing but a short description of a product or service, and perform automatic maintenance on the entire campaign as a whole in terms of bidding, targeting, and keyword combinations. There is also the fact that Smart Campaigns can be created manually, eliminating the need for each and every Google Ads client to hire an agency for marketing purposes – which can be a deal-breaker for some smaller businesses with a limited budget.
The rise of Artificial Intelligence within different industries is feared by some people, for a number of reasons – but the marketing sphere acquired a massive number of benefits from the introduction of AI to its work, making it so much easier for businesses to attract new customers in the first place.
This is also applicable to Google Ads specifically – the platform itself is sophisticated enough, and it would have been far more difficult to navigate for non-experienced people if it was not for all the different features that Google Ads AI can offer, from general performance analysis to the ability to run entire marketing campaigns that are generated by an AI from start to finish.
With that being said, it would also be fair to mention that Google is not the only player in the field of advertising, and is also not the only company that started using AI in its marketing efforts. As such, we are also going to go over how Facebook’s advertisement game changed with the introduction and popularization of Artificial Intelligence.
AI in Facebook Ads
Both Facebook and Google were quick enough to see the potential that lies in AI as a technology, especially when it comes to advertisements. As such, now Facebook also has its own AI-powered methods and tactics to improve advertising across the entire platform, for both the customers and the advertisers themselves. One of the biggest contributors to that idea is a new way to optimize ad delivery with the help of Artificial Intelligence, boosting the number of conversions and making sure that the end user always sees the most suitable ad.
Facebook’s own AI-based approach to digital marketing and eCommerce is called Power 5. It is an entirely new framework that is powered by AI to ensure the best ROI (return of investment) for advertisers across the board. There are five core parts that Power 5 has:
- Simplified account structure for the sake of easier performance evaluation for creatives with subsequent optimization.
- Automatic ad placement to display relevant ads to specific audiences across all of Meta’s products – Instagram, WhatsApp, Facebook, etc.
- Advanced matching with automatization capabilities to ensure better conversions and higher audience reach, it can be achieved by providing both Pixel events and hashed customer details to the platform itself.
- Optimization of the campaign budget to make sure that the advertising funds are only spent on the best-performing campaigns.
- Dynamic advertisements to ensure that targeted ads are delivered to potential customers based on their history in the web store as well as other actions they took within the platform as a whole.
While correct Facebook ads AI delivery is important, it would be more or less useless if there was no way to monitor and track it. This is where DDA comes in – a Data-Driven Attribution model created by Facebook and powered by ML. It measures the performance of the aforementioned AI Facebook ads in several different ways, showcasing how customers’ actions turn into a conversion (called “touchpoints” by Facebook themselves). These actions are web store visits, clicks, and impressions that happen before the aforementioned conversion.
Facebook also takes care of the potential issue of accuracy when it comes to machine learning by comparing all of the results with Facebook’s other conversion lift studies that were performed without the use of DDA. This is how Facebook checks the results of the machine learning algorithm for bias.
Third-party Facebook advertising AI software
Facebook’s AI-based advantages are quite impressive, but they tend to lack a lot of industry-specific features in many use cases. And the process of creating and optimizing a marketing campaign for Facebook has quite a lot of “moving parts”, so to speak:
- Picking the correct target audience is paramount;
- At least several images for this marketing campaign have to be relevant to the campaign’s goal and attractive to your target audience;
- One of your biggest goals is to achieve the best ROI possible;
- The written part of this ad or campaign has to be appealing to the customer while also being fit to Facebook’s guidelines;
- You also have to keep in mind various modifications and other options of your ad or campaign when creating it.
Juggling all of these elements at once can be rather difficult even for the most experienced people in the industry. This is where third-party software comes in, offering additional features on top of Facebook’s existing advertising toolkit. At the same time, there are a number of these solutions that also use their own implementations of AI to improve specific tasks or methods. For the sake of keeping up the theme of this article, we are going to present a number of AI Facebook ads solutions in a list below, with a lot of emphasis on what parts of this solution are AI-powered and how they are better than what Facebook already has.
Zalster is a marketing solution that aims to provide the biggest possible ROI by optimizing budgets, targeting, and bids with the help of AI. As with many other similar services, it also works for multiple platforms – be it Facebook, Snapchat, Pinterest, or Instagram. It can also integrate with Slack to offer status updates, has a centralized dashboard for all of its operations with a graphic representation included in the package, and more.
Zalster also has a feature called split testing – the capability to test for both conversion events and manual bidding to determine what option is the most effective at the moment. Ad distribution can also be automated with Zalster, with its “story creator” feature that can optimize, test, and design different ad placements at a moment’s notice.
Revealbot is a marketing platform that also aims to increase its customers’ ROI via scaling, launching, and managing AI Facebook ads. Revealbot can be integrated with Slack, and it is also one of the few solutions on this list that can work with both Google and Facebook, while also supporting Instagram and Snapchat. It has a multifunctional rule builder to perform extensive automated operations – bidding processes, budget-controlling tasks, restarting or pausing ads, etc.
As a marketing platform, Revealbot also expands upon what Facebook has to offer in terms of marketing, with one of the easiest examples being Revealbot’s capability to perform automated rules more often than Facebook can.
Trapica offers a larger list of supported platforms if compared with other examples on this list – including Reddit, Amazon, TikTok, YouTube, and, of course, Facebook. It is an AI-assisted marketing platform that focuses its efforts on optimizing advertisement campaigns for various social media websites. Trapica’s goal is to analyze your running campaigns to see which ones are converting to be able to adjust your bidding and targeting efforts in accordance with that information. As for Facebook ads AI, Trapica has a number of unusual parameters that it can show in relation to demographic, be it age, location, or gender.
Another example of a marketing platform that supports both Google and Facebook is AdEspresso. This platform specializes in ad optimization and testing, and it has earned its popularity with a number of high-grade clients such as Microsoft and Hubspot. It offers split testing with multiple AI Facebook ads at once (exceeding the limit of what Facebook’s own service can work with), and it can also cover multiple images, multiple headers, and even multiple target audiences. It also has its own share of special features, such as “Dynamic Ads” – the capability to deliver ads to customers based on user-defined segments.
These last two examples are a bit different from the rest, providing solutions that have advertising and AI-supported features as secondary capabilities, not as their primary purpose. Canva is a first example of that, offering an extensive user-friendly design tool that also has a variety of features for many purposes – with one of the bigger examples here being Canva’s vast library of images combined with an AI-assisted search engine. That’s not to say that images and backgrounds have to be from Canva’s repository only – there are also options to upload your own images, or even take them from your Facebook account in the first place.
Viewst is an extensive ad builder that puts a lot of emphasis on the B2B audience with its SaaS approach, offering creative versioning and approval streamlining, among many other features. It also has a number of AI-powered tools in its package, such as ad analysis for A/B test purposes, the ability to generate copies of ads with different variables at a moment’s notice, and so on. Viewst also has a lot of design-related features in general, being a capable online ad builder with a user-friendly interface and drag-and-drop capabilities for most of its creative elements.
Both Artificial Intelligence and Machine Learning have a lot of applications in the advertising industry, and many companies try to keep up with the existing trends to not get left behind when a specific AI-powered feature becomes an industry standard. It is also a great way to eliminate or reduce the amount of tedious and repetitive tasks that are usually a part of the overall marketing process – combine that with a variety of ways for AI and ML to improve the overall marketing results for almost every company out there, and it becomes easy to see why the adoption of these features was so fast in the first place.
Victoria is the CEO at Viewst. She is a serial entrepreneur and startup founder. She worked in Investment Banking for 9 years as international funds sales, trader, and portfolio manager. Then she decided to switch to her own startup. In 2017 Victoria founded Profit Button (a new kind of rich media banners), the project has grown to 8 countries on 3 continents in 2 years. In 2019 she founded Viewst startup. The company now has clients from 43 countries, including the USA, Canada, England, France, Brazil, Kenya, Indonesia, etc.