Automation. AI. Machine Learning. Natural language processing. These terms are everywhere lately, especially in reference to digital marketing and advertising. It’s easy to feel like the computers are taking over. But are they?
Computers and machine learning can only take your business so far. Critical parts of your company’s digital advertising strategy still need a human touch and always will.
(Unless, of course, we end up in an apocalyptic, Matrix-esque world where machines have gained complete control over humans, and the inevitable war ensues.)
For now, though, let’s focus on using AI in advertising campaigns strategically. It’s important to understand how this new age of technology will impact your business and how to marry human expertise with machine learning to continue to build and maintain successful businesses.
What is AI technology good at?
Simply put, computers are very good at the repetitive parts of digital advertising. The parts that require the use of large data sets and the ability to spot trends in all that data. Let’s look at a few examples in real-life digital advertising scenarios.
Repetitive tasks
When digital advertising first started, everything was based on keywords and bids. Advertisers would put a set of keywords together based on how they expected people to search for what they were advertising. Then, they’d set bids for those keywords and make adjustments based on what was doing well and what could generate optimal ROI.
Advertising professionals had to constantly evaluate keyword bids and make modifications based on what was working and what wasn’t. Automation has been able to unload a lot of that manual work. Machines can do in minutes what had previously taken advertisers hours on end to go through and adjust.
Data Sets and Trends
It takes a lot of manipulation and evaluation of raw data for humans to draw conclusions from large data sets. We need some way to aggregate, visualize, and parse out this data. Even then, we can still arrive at faulty conclusions because we have our own intrinsic expectations and biases. Computers can take in a lot of data, quickly spot trends, and don’t have these inherent biases.
Machines can take data that has always been available to advertisers, such as age, gender, time of day, interest categories, etc, then combine it with platform-owned data, like browsing history and shopping behavior, to adjust bids in real-time.
Let’s look at how this might play out in a real-world advertising scenario. Say historical data indicates men between the ages of 25-34 on a mobile device at 1 p.m. tend to convert most often. AI systems can see that someone outside of these categories might be highly likely to take action and show the ad, winning you more business. This level of personalization and bid management would not be possible with manual bidding.
Even beyond simple bid management, machines can use this trend-spotting ability to match the right ad message to each user. Dynamic ad formats, such as responsive search or dynamic display ads, combine multiple text and/or image assets and compile them for each ad auction based on numerous signals from your target audience.
A user who has been looking at sales online recently, for example, might get a more price-oriented message than someone who demonstrated more interest in quality, even if all other demographic signals were identical.
So, why is a professional, human touch still a necessary component of great digital advertising in 2023?
Machines have data. Humans have instincts, intuition, and creativity.
As good as an AI platform can be, it’s never going to be able to replicate the uniquely human way that we relate to each other. That emotional, human-to-human connection and critical thinking are key for successful advertising and digital marketing strategies.
Creativity
Creativity is an indispensable part of any advertising strategy. Human creativity allows us to see a problem from multiple angles.
If something is not working, we can problem-solve and improve the results or suggest a different tactic. If something is doing exceptionally well, we can think of new ways to build on that success.
We can look at an image asset and tell if something is simply “not quite right.” And we can think of different ways to say something that feels more personal.
Strategy
Artificial intelligence systems don’t innovate — they base strategies and responses on what already exists. If you want a fresh perspective or innovative ideas, there’s no replacing the humans on your team.
Although many platforms have their own proprietary machine-learning algorithms, they can’t see or build on things that happen on other platforms. But we know that most customer journeys don’t occur on just one platform and are rarely (if ever) linear.
A good marketer knows how to work with clients to understand their goals and design a cross-platform strategy to achieve those goals. It takes skill to translate the idea of sales or lead growth into recommendations for ad platforms, ad types, messaging, and more.
The Marriage of Man and Machine
We have seen first-hand how leaning into machine learning can benefit our clients and achieve results that we could not replicate with manual settings. But it does take a thorough understanding of how the systems work to get the most out of them.
Be Clear With Your Goals
Using the wrong strategy or signals can adversely affect a campaign or account. While you need to tell these AI systems what you hope to get out of a campaign, your goals must be rooted in data and realistic to achieve.
If you have historically gotten a cost-per-lead of around $100, telling the system to optimize for a $5 cost-per-lead will quickly ruin the campaign. In fact, if the historical data doesn’t show that this is achievable, your ads may not serve at all, and your lead volume will drop.
Similarly, if you set a goal of $100 per lead and you only give the campaign $50 to spend daily, you aren’t spending enough in a given day to even generate one lead.
Give It Time
When you first switch to machine learning to optimize a campaign, give the system time to test different components and draw data to optimize. After any changes, most platforms will need at least 2-4 weeks to learn and optimize a campaign.
During this learning phase, modifications should not be made to any settings so that you are not continually moving the target you are asking the machine to hit.
Don’t Make Drastic Changes
AI-based systems do their best work when data is predictable. Even after the initial learning phase, roll out changes slowly.
Making drastic changes to budgets, goals, ads, or other elements of your campaign will reset the learning phase and could negatively impact results, at least in the short term.
For example, significant increases or decreases in campaign budgets could throw off the system and reset the learning phase. Sudden increases will push the machine to try and find more people to reach that will still achieve the goal, while sudden decreases could limit the number of users you can reach.
Ready To Optimize Your Digital Ad Campaigns?
If this all seems overwhelming, don’t worry. We’ve got your back. Here at E/Power Marketing, we’re experts at making AI work for our clients.
We combine the powerful abilities of the machine with the creative, strategic power of the human brain. We love hearing clients tell us their hopes and dreams for their businesses, then putting our expertise to work to achieve those dreams.
Schedule a no-obligation consultation today and see how we can help!
