FACT: We get so much help from artificial intelligence (Ai.)
Various Ai-based platforms help us navigate personal and professional lives, often without us even knowing about it.
Consider these examples:
- Google Maps uses Ai and machine learning to suggest the fastest route to wherever we’re going.
- Gmail’s artificial intelligence ensures that most of the spam never reaches our inboxes.
- Uber uses machine learning to predict rider demand, and ensure that there are rides available whenever we need them, and so on.
The situation is no different when it comes to advertising. Various Ai and machine learning algorithms help us predict ad performance, optimize campaign targeting, generate a much higher ROI, and more.
But how is Ai used in advertising, actually? And how could you benefit from using Ai in advertising campaigns?
Well, let’s find out, shall we?
What are Ai and Machine Learning?
I mentioned both terms – Ai and machine learning – a few times in this post’s short opening. However, I believe that each requires a proper explanation before we move any further.
When we talk about artificial intelligence (Ai,) we, typically, refer to machines and computer systems that can learn with the help of a human (or without.)
Ai systems can analyze data, read and analyze text (to some degree, of course,) and in the case of some, even move around a physical space, avoiding obstacles.
The term machine learning (ML), on the other hand, describes the ability of Ai tools to learn and improve through data and experience automatically. With machine learning, Ai’s can improve their data processing skills and other functions and do much of that without any human supervision.
ML-powered machines can quickly evaluate millions of data points, correlate them together, and draw conclusions from them. And then learn from that experience, and improve their data processing capabilities further.
As a result, such tools can evaluate and predict events or factors much faster than humans.
That is precisely why companies turn to Ai and machine learning. Again, let me share a couple of examples of how well-known brands use machine learning:
- Yelp, for example, uses machine learning to improve its picture classification technology.
- ML powers Twitter’s feed and timeline.
- Thanks to machine learning, Google can understand our search queries better and deliver highly-relevant results.
But what about marketing and advertising, though?
How Ai and Machine Learning Improve Advertising Campaigns
In advertising, machine learning algorithms allow us, advertisers, to use data to build a buyer persona, and predict, with some degree of certainty, how they would think and act.
To do so, ML systems evaluate tens of millions of data points to practically replicate how the target audience’s brains work, and optimize campaigns based on how we believe they’d act and respond.
What’s more, since ML systems learn over time, these tools can continuously improve the buyer persona based on the campaigns’ performance to generate better results.
The above is also the key reason we turn to Ai and machine learning systems when managing ad campaigns.
You see, we have an incredible amount of marketing data at our disposal. Our CRMs, email platforms, ad platforms, ecommerce systems, and more are filled with information about our customers’ needs and preferences.
We can’t do much with it, though. There is simply too much of it for us to process effectively.
Ai, on the other hand, can sift through all this information quickly, delivering us the insights we need. Thanks to ML, it can also learn and improve its cognitive capabilities to draw even better conclusions over time.
Here are the two most common use cases for using Ai in advertising.
Optimizing Ad Spend and Targeting
In the modern digital world, advertisers are limited by one thing only – their budget. We have an incredible amount of data to launch and optimize ad campaigns, after all. We can run those ads on the best advertising platforms and reach out to target audiences quickly.
But as an advertiser, you can only do as much as your budget allows.
Hence the issue of optimizing ad spend. If we can lower the spending while maximizing the return on that investment, we can run more advertising campaigns and generate greater success, right?
The problem is that we, humans, are often limited in our ability to digest and evaluate large data sets. Ai-based tools, on the other hand, can make sense of it almost instantly. Artificial intelligence platforms can detect audience patterns, identify what works and what doesn’t in a campaign, and correlate that data to adjust the spending, often in real-time.
Ad Creation and Ad Management
For example, some advertising platforms – Facebook, use Ai’s to help customers create ad copy and ad variation based on their past campaigns’ performance.
Tools like Phrasee can even write the ad creative to increase clicks and conversions.
Other solutions, like Wordstream, use machine learning to analyze a campaign’s performance. Those tools, then, deliver insights to help you adjust and maximize the performance further.
Challenges with Implementing Ai in Advertising
So far, I’ve been painting quite an encouraging picture of using artificial intelligence in advertising. You know that Ai can help to optimize the ad spend significantly.
However, the advertising industry faces challenges with Ai as well. The most notable ones relate to:
Data quality. Ai’s can analyze, evaluate, and correlate millions of data points. But if the quality of information isn’t high enough, then it doesn’t matter how fast the tool processes it. Your insights will be flawed, anyway.
Training and following the best practices for using Ai in digital advertising.
Getting the buy-in. 80% of US digital spending is now programmatic advertising, much of which is assisted by Ais. However, the adoption is skewed towards large brands buying millions of ad impressions per day.
Small and medium businesses seem to struggle with adapting to programmatic and Ai-based advertising.
Privacy concerns around correlating data from multiple sources also raise concerns.
Finally, there is the question of conversion and ROI tracking. This issue mainly concerns professional service businesses. For those companies, the goal of advertising isn’t often the sale but lead generation.
However, when a conversion is not a direct sale, it’s hard to inform a machine learning algorithm about its success. Other factors may affect whether a lead has become a customer, after all.
Naturally, an Ai system can be set to track conversion vs. clients. But even that becomes problematic in this case. The Ai wouldn’t tell how good a lead is or what is their value to a company.
For many of our clients, for example, parts of their budgets are spent on:
- Free consultation inquires that do not lead to hiring a lawyer,
- Property damage cases whereas the target was an injury client,
- Attracting views and lead magnet downloads from people who aren’t interested in hiring professional services.
If ad management were left to Ai, the system would see the conversion (which are not personable leads.) It could think of those conversions as ‘a honey pot’ and optimize the campaign to get more of those.
Unfortunately, in this case, it would ultimately burn out the clients’ cash without delivering much of a return.
Artificial intelligence has already become an integral part of our lives. Ai systems assist us with a growing number of tasks, including ad targeting and optimizing and improving advertising campaigns to increase ROI.
Although we don’t use any standalone Ai-based tools at Envoca, many advertising platforms we use include Ai-based components that help our clients get more results from their budgets.