If the phrase artificial intelligence makes you think of virtual assistance services Alexa and Siri, or Tesla’s self-driving cars, you’re on the right track. But you’re also forgetting about the small ways artificial intelligence and machine learning are already impacting your life. And while there has been a lot of talk about the future of artificial intelligence (AI) and how it may impact us at some point, the truth is that AI is already here. Yes, it’s applied in autonomous cars, but also face recognition and biometric systems (have you unlocked your iPhone today?), warehouse and supply chain management, image analysis and translation software and a multitude of other everyday use, including - and this should come as no surprise - digital marketing and advertising.AI is being applied across popular digital platforms such as Facebook, Google and Instagram to enhance user experiences and improve the performance of your paid ads. And as AI becomes increasingly popular, you can expect a significant disruption of your day-to-day marketing operations. Not to worry you, but a recent study suggested that nearly 60 per cent of current marketing specialist jobs will be replaced by AI. So while there’s an opportunity for growth, many marketers will also need to evaluate whether or not their job will still exist in five years.Artificial intelligence is the marketing trend you need to grasp to remain competitive in the future. Here’s what you need to know to make it happen!
What is Artificial Intelligence (AI)?
Artificial intelligence (AI) refers to the application of computer systems that can simulate human behaviour or tasks that generally are considered to require human intelligence.Machine learning is a type of artificial intelligence. It allows a ‘machine’ to automatically learn from historical data to predict future outcomes without being programmed explicitly. Of course, machine learning can be misleading because it’s not the machine itself doing the learning but the software behind it. While we’re accustomed to using devices to overcome our physical limitations, we’re now increasingly seeing them helping us with jobs requiring quick thinking and fast decision-making.It is no question that machines read software and can answer questions and queries far quicker than we humans. In its simplest form, think of search engine results pages and how many listings they can generate within the blink of an eye.Similarly, machine learning software can chain together complex strings of queries to come up with predictions, including what actions a user is likely to take or what ad format they are most likely to respond to positively.
AI and Machine Learning Application in Digital Marketing
As a natural consequence, AI is often applied where speed and accuracy are essential. There is a myriad of use cases for AI in marketing, some of which include:
- Data analysis
Do you struggle to stay on top of the data your paid campaigns produce, analyse their outputs and tie your successes or shortcomings back to specific ads? One of the many use cases of AI in marketing that can make your life that much easier is automated decision-making based on data collection and analysis, considering external observations and influences on an audience, such as economic trends or weather phenomena, all of which may impact your marketing efforts.
- Real-time personalisation
Personalisation is a key driver of marketing success, and thanks to AI, you can take things to a whole new level. AI can, for example, help you deliver personalised messages to customers at the exact right points in their consumer lifecycle, without human interference, ensuring maximum efficiency.
- Natural language processing
Thanks to the development of natural language processing, automated chatbots are now acting as customer service agents and, more importantly, delivering personalised results that give immediate, accurate answers. Marketing teams rapidly adopting AI solutions are seeing many benefits such as increased operational efficiency, improved customer experience and satisfaction, and a significant increase in sales on the more quantifiable end of the spectrum.The insights and lessons gained through the application of artificial intelligence include a more comprehensive understanding of target audiences. And often underrated, but equally important, easing the workload for marketing teams.
Machine Learning in Action
Two examples of machine learning you’re most likely very familiar with are Facebook’s News Feed and Facebook Ads.
- Facebook’s News Feed
There is a reason why some posts appear at the top of your News Feed while others are obscured. Facebook uses the power of machine learning to rank and personalise News Feeds, filter out offensive content, highlight trending topics, and more.In December 2021, Anna Stepanov, Head of Facebook App Integrity, explained in an official statement on recent changes to the News Feed:“News Feed uses personalised ranking, which takes into account thousands of unique signals to understand what’s most meaningful to you. Our aim isn’t to keep you scrolling on Facebook for hours on end, but to give you an enjoyable experience that you want to return to.”The rank of anything that appears in your News Feed is decided on many factors. Still, most noteworthy are perhaps the following two:
- Interaction: Since 2018, Facebook’s machine learning algorithms have given preference to posts that encourage interaction. This broadly prioritises posts by friends, family, public figures, and businesses you interact with.
- Interests: Your feed is customised to your specific interests and the type of content you like. This has caused controversy over the past years, as it tends to create information landscapes that reflect a user's values and views.
- Facebook Ads
Facebook also uses machine learning to determine which ads to show to which people. Here’s how that works: Once you have chosen a target audience, your ads undergo an auction stage during which Facebook chooses the top ads to show people within your audience. This is necessary because you’re likely not the only one bidding for their attention. Facebook selects ads based on their total value score, a combination of advertiser value and ad quality. Advertiser value is calculated by multiplying an ad’s bid by the estimated action rate, defined as the likelihood of a user taking a desired action based on your campaign objective.This process applies machine learning models to predict estimated action rates and determine an ad’s quality score. This is also why your Facebook Ads undergo a learning phase (with time, models get better at predicting estimated action rate and ad quality) and why the highest bids don’t always win an auction.
- Learn more about how Facebook uses machine learning to deliver your ads.
As Brisbane’s leading digital marketing agency, we’ve got our fingers on the pulse of creative and marketing strategies with a watchful eye on everything new in artificial intelligence and machine learning. Looking for a reliable partner in climb? Visit our website to learn more about our service offering or book an obligation-free consultation. We’re looking forward to hearing from you!