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8 Machine Learning Examples From Brands To Inspire Digital Marketers

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8 Machine Learning Examples From Brands To Inspire Digital Marketers

Machine learning is all the rage but what does it actually look like in practice, as part of a digital marketing strategy?

You’ve encountered a machine learning strategy if you’ve used a website that recommends products based on previous purchases.

Machine learning is a facet of artificial intelligence (AI) that uses algorithms to complete specific tasks, such as product recommendations.

It can achieve a multitude of functions for digital marketers, including:

Machine learning has been in digital marketing for years.

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In fact, you are using machine learning whenever you use search engines.

While still a new strategy for most, many businesses have begun implementing this technology into their marketing campaigns.

Below are eight examples of machine learning in digital marketing.

1. Chase

In 2019, the banking giant, Chase Bank, partnered with Persado to help create marketing copy for its campaigns.

They challenged the AI company to generate copy that yields more clicks — which they did.

Examples of the machine learning generated copy are:

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Human copy: “Go paperless and earn $5 Cash Back.”

Machine-generated copy: “Limited Time Offer: We’ll reward you with $5 Cash Back when you go paperless.”

Results: AI copy generated nearly double the clicks.

Human copy: “Access cash from the equity in your home” with a “Take a look” button.

Machine-generated copy: “It’s true – You can unlock cash from the equity in your home” with a quick “Click To Apply.”

Results: AI copy attracted 47 applicants a week, while human copy attracted 25 applicants a week.

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Human copy: “Hurry, It Ends December 31 Earn 5% Cash Back At Department Stores, Wholesale Clubs.”

Machine-generated copy: “Regarding Your Card: 5% Cash Back Is Waiting For You”

Results: AI copy generated nearly five times the unique clicks.

While the machine-generated copy may have performed better with customers, it’s important to remember that it worked with human copywriters feeding it ideas.

Together, human copywriters and machine learning can create and optimize copy that resonates.

2. Starbucks

With stores worldwide, Starbucks obtains a lot of data.

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Starbucks can access purchase insights and turn this information into marketing collateral with the Starbucks loyalty card and mobile app. This strategy is called predictive analysis.

For example, machine learning collects the drinks each customer buys, where they buy them, and when they buy them, and matches this with outside data such as weather and promotions to serve ultra-personalized ads to customers.

One instance includes identifying the customer through Starbucks’ point-of-sale system and providing the barista with their preferred order.

The app can also suggest new products based on previous purchases (which can change according to weather conditions or holidays).

Machine learning can take the guesswork out of product recommendations.

Retail giants like Starbucks have millions of customers, yet they can make each feel like they get personalized recommendations because they can sift through data quickly and efficiently.

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3. eBay

eBay has millions of email subscribers. Each email needed engaging subject lines that would cause the customer to click.

However, delivering over 100 million eye-catching subject lines proved overwhelming to human writers.

Enter machine learning.

eBay partnered with Phrasee to help generate engaging subject lines that didn’t trigger spam filters. Additionally, the machine-generated copy aligned with eBay’s brand voice.

Their results show success:

  • 15.8% increase in open rates.
  • 31.2% increase in average clicks.
  • Over 700,000 incremental opens per campaign.
  • Over 56,000 incremental clicks per campaign.

Machine learning can take the most daunting tasks and complete them within minutes at scale.

As a result, businesses can focus more on big-picture campaigns than microtasks.

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4. Doordash

Doordash operates thousands of marketing campaigns across its marketing channels.

Their team manually updates bids based on the ads’ performance.

However, the team found that this task was time-consuming and overwhelming.

So Doordash turned to machine learning to optimize its marketing spend.

It built a marketing automation platform based on attribution data.

This data tells the company which channel the customer converted on and with what campaign.

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However, it can be hard to promptly collect that type of data with thousands of campaigns going on at once.

Machine learning helps tackle this task by collecting that data and creating spending recommendations so they can optimize their budget quickly and efficiently.

5. Autodesk

Autodesk saw the need for more sophisticated chatbots.

Consumers are often frustrated by the limitations of chatbots and therefore prefer to speak with a human.

However, chatbots can help efficiently guide customers to the content, salesperson, or service page they need.

So Autodesk turned to machine learning and AI.

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Autodesk’s chatbot uses machine learning to create dialogue based on search engine keywords.

Then, the chatbot can connect to the customer on the other end, allowing for faster conversion rates.

Since implementing their chatbot, Autodesk had three times the chat engagement and a 109% increase in time spent on the page.

6. Baidu

In 2017, Baidu, the Chinese search engine, built a system called Deep Voice that uses machine learning to convert text to speech. This system can learn 2,500 voices with a half-hour of data each.

Baidu explains that Deep Voice can lead to more immersive experiences in video games and audiobooks.

Baidu’s goal with Deep Voice is to teach machines to speak more human-like by imitating thousands of human voices.

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Soon, the search engine hopes the system can master 10,000 or more voices with different accents.

When perfected, Deep Voice could improve things we use every day, like:

  • Siri.
  • Alexa.
  • Google Assistant.
  • Real-time translation.
  • Biometric security.

It can even help people who have lost their voice communicate again.

While there haven’t been any recent updates, Baidu remains hopeful that Deep Voice will revolutionize our tech.

7. Tailor Brands

Tailor Brands uses machine learning to help its users create logos.

The machine, “This or That,” helps Tailor Brands understand a user’s taste using decision-making algorithms.

By choosing examples of what they like, users tell the logo generator their preferences for styles, fonts, and other design aspects.

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Tailor Brands uses linear algebra.

Each user’s decision is fed into an equation that helps the machine learn the user’s preferences.

The next time someone generates a logo, Tailor Brands can show styles similar to what they’ve used before.

8. Yelp

Yelp receives millions of photos every day worldwide.

The company realized it needed a sophisticated way to match photos to specific businesses.

So they developed a photo understanding system to create semantic data about individual photographs.

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This system allows Yelp to sort photos into categories relevant to the user’s search.

First, Yelp created labels for the photos they received from users, such as “drinks” or “menu.”

Next, the company collected data from photo captions, photo attributes, and crowdsourcing.

Then, it implemented machine learning to recognize the photo labels, from which the system could put the photos into categories.

This photo classification system helps create a better user experience on Yelp.

For instance, it can help diversify cover photos and create tabs that let users jump to the exact information they are looking for.

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Digital marketers are only scratching the surface of what machine learning can do for them.

Humans and machines can work together to create more meaningful customer experiences and more optimized campaigns in less time. It’s a win-win-win.

More resources:


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Google Answers A Crawl Budget Issue Question

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Google Answers A Crawl Budget Issue Question

Someone on Reddit posted a question about their “crawl budget” issue and asked if a large number of 301 redirects to 410 error responses were causing Googlebot to exhaust their crawl budget. Google’s John Mueller offered a reason to explain why the Redditor may be experiencing a lackluster crawl pattern and clarified a point about crawl budgets in general.

Crawl Budget

It’s a commonly accepted idea that Google has a crawl budget, an idea that SEOs invented to explain why some sites aren’t crawled enough. The idea is that every site is allotted a set number of crawls, a cap on how much crawling a site qualifies for.

It’s important to understand the background of the idea of the crawl budget because it helps understand what it really is. Google has long insisted that there is no one thing at Google that can be called a crawl budget, although how Google crawls a site can give an impression that there is a cap on crawling.

A top Google engineer (at the time) named Matt Cutts alluded to this fact about the crawl budget in a 2010 interview.

Matt answered a question about a Google crawl budget by first explaining that there was no crawl budget in the way that SEOs conceive of it:

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“The first thing is that there isn’t really such thing as an indexation cap. A lot of people were thinking that a domain would only get a certain number of pages indexed, and that’s not really the way that it works.

There is also not a hard limit on our crawl.”

In 2017 Google published a crawl budget explainer that brought together numerous crawling-related facts that together resemble what the SEO community was calling a crawl budget. This new explanation is more precise than the vague catch-all phrase “crawl budget” ever was (Google crawl budget document summarized here by Search Engine Journal).

The short list of the main points about a crawl budget are:

  • A crawl rate is the number of URLs Google can crawl based on the ability of the server to supply the requested URLs.
  • A shared server for example can host tens of thousands of websites, resulting in hundreds of thousands if not millions of URLs. So Google has to crawl servers based on the ability to comply with requests for pages.
  • Pages that are essentially duplicates of others (like faceted navigation) and other low-value pages can waste server resources, limiting the amount of pages that a server can give to Googlebot to crawl.
  • Pages that are lightweight are easier to crawl more of.
  • Soft 404 pages can cause Google to focus on those low-value pages instead of the pages that matter.
  • Inbound and internal link patterns can help influence which pages get crawled.

Reddit Question About Crawl Rate

The person on Reddit wanted to know if the perceived low value pages they were creating was influencing Google’s crawl budget. In short, a request for a non-secure URL of a page that no longer exists redirects to the secure version of the missing webpage which serves a 410 error response (it means the page is permanently gone).

It’s a legitimate question.

This is what they asked:

“I’m trying to make Googlebot forget to crawl some very-old non-HTTPS URLs, that are still being crawled after 6 years. And I placed a 410 response, in the HTTPS side, in such very-old URLs.

So Googlebot is finding a 301 redirect (from HTTP to HTTPS), and then a 410.

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http://example.com/old-url.php?id=xxxx -301-> https://example.com/old-url.php?id=xxxx (410 response)

Two questions. Is G**** happy with this 301+410?

I’m suffering ‘crawl budget’ issues, and I do not know if this two responses are exhausting Googlebot

Is the 410 effective? I mean, should I return the 410 directly, without a first 301?”

Google’s John Mueller answered:

G*?

301’s are fine, a 301/410 mix is fine.

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Crawl budget is really just a problem for massive sites ( https://developers.google.com/search/docs/crawling-indexing/large-site-managing-crawl-budget ). If you’re seeing issues there, and your site isn’t actually massive, then probably Google just doesn’t see much value in crawling more. That’s not a technical issue.”

Reasons For Not Getting Crawled Enough

Mueller responded that “probably” Google isn’t seeing the value in crawling more webpages. That means that the webpages could probably use a review to identify why Google might determine that those pages aren’t worth crawling.

Certain popular SEO tactics tend to create low-value webpages that lack originality. For example, a popular SEO practice is to review the top ranked webpages to understand what factors on those pages explain why those pages are ranking, then taking that information to improve their own pages by replicating what’s working in the search results.

That sounds logical but it’s not creating something of value. If you think of it as a binary One and Zero choice, where zero is what’s already in the search results and One represents something original and different, the popular SEO tactic of emulating what’s already in the search results is doomed to create another Zero, a website that doesn’t offer anything more than what’s already in the SERPs.

Clearly there are technical issues that can affect the crawl rate such as the server health and other factors.

But in terms of what is understood as a crawl budget, that’s something that Google has long maintained is a consideration for massive sites and not for smaller to medium size websites.

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Read the Reddit discussion:

Is G**** happy with 301+410 responses for the same URL?

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SEOs, Are You Using These 6 Mental Models?

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SEOs, Are You Using These 6 Mental Models?

People use mental models to comprehend reality, solve problems, and make decisions in everyday life. SEO is not an exception here, yet it’s not a topic you often hear about in this industry.

The thing is, you need to be careful with mental models because they’re sneaky. We tend to develop them during our lives, inherit them from our colleagues and mentors, and rely on them almost instinctively while not fully aware of their influence or the existence of better alternatives.

So, let’s talk about mental models you will find helpful in your SEO work and the ones you should approach with caution.

3 helpful mental models

In the noisy, uncertain world of SEO, these will be your north star.

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First principles thinking is a problem-solving approach that involves breaking down complex problems into their most basic elements and reassembling them from the ground up.

It’s about asking oneself what is absolutely true about a situation and then reasoning up from there to create new solutions.

Using first principles thinking to rearrange the same building blocks into a brand new shape. 

Uncertainty is a chronic condition in SEO. And it is so by design because the whole industry is based on Google’s secrets. Access to the truth is extremely limited. We got to the point that we got used to accepting speculation and theories on SEO so much that we started to crave them.

This is where the first principles come in. Whenever you need a brand new solution for a problem or when you feel that you’ve gone too far into speculation, come back to the first principles — things that have the best chance to be true in this industry. For example:

The Pareto Principle (aka the 80/20 rule) is about a disproportionate relationship between inputs and outputs, effort and results, or causes and effects. A small number of causes (20%) often leads to a large number of effects (80%).

The Pareto principleThe Pareto principle
The 80/20 rule: 80% of results come from 20% of the projects.

This concept was named after Vilfredo Pareto, an Italian economist who, in 1906, noticed that 80% of Italy’s land was owned by 20% of the population.

If we use this principle as a mental model in decision-making, we’ll find it easier to prioritize work. It’s ok to ignore some things because they likely won’t matter that much. The result that you’re after will come from focusing on the things that will likely have the biggest impact, and not from spreading yourself too thin.

For example, if you want to build links to your site, pitch your best content. That can be the content that has already proven to earn links in the past.

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Best by links report in Ahrefs.Best by links report in Ahrefs.

Or if you need to recover some of that lost traffic, home in on the pages that lost the most traffic.

Top pages report in Ahrefs. Top pages report in Ahrefs.

The key is to treat the 80/20 as an approximation, a heuristic, and not take the numbers literally. To illustrate, roughly 80% of our site’s traffic comes from no more than 6% of pages.

Total organic traffic breakdown in Ahrefs. Total organic traffic breakdown in Ahrefs.

But on the other hand, if we try to find the top 20% pages that contribute to the traffic the most, we’ll find that they bring not 80% but 96.8% traffic. However you look at it, the idea still holds — a small amount of causes led to a large portion of effects.

“It takes all the running you can do, to keep in the same place.”

Sounds very much like SEO already, doesn’t it?

This quote comes from Lewis Carroll’s “Through the Looking-Glass,” and it’s how the Red Queen explains to Alice the nature of her kingdom, where it requires constant effort just to maintain one’s current position.

It was used to name an evolutionary biology theory which posits that each species must adapt and evolve not just for incremental gains but for survival, as their competitors are also evolving. Sorry, we’re in an endless race.

The Red Queen Theory as an endless race.The Red Queen Theory as an endless race.
SEO is like a road with no finish line—the race continues forever.

You can probably already guess how this applies to SEO — rankings. If you want to maintain high rankings, you can’t stop improving your pages. There will always be enough competitors to challenge your position.

But in our world, pressure comes from competitors and the environment. Google keeps evolving too, pushing the bar for content higher, making elements that used to give you an edge a standard.

I’m sure we’ve all been there – even our top backlink-earning, top traffic-generating, most time-consuming content gets pushed down. But if you keep optimizing, you get a chance to come back to the top.

Position history graph in Ahrefs.Position history graph in Ahrefs.

This mental model is another way of saying that SEO works best as an always-on strategy without a set end date or final goal.

3 mental models to watch out for

It’s not so much about avoiding them but being able to spot them when they happen or could happen.

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A local maximum (aka local optimum) refers to a solution that is the best solution within a neighboring set of solutions, but not necessarily the best possible solution overall (global optimum).

Local maxima.Local maxima.

So if you’re feeling that you’re spending immense effort just to make marginal improvements, you have to be willing to assume that you’ve hit a local maxima. Then, the question to ask is: what can I do differently?

Here’s an example.

Until November last year, traffic to our site was a series of local optima. Our content marketing was delivering the results, but the growth was relatively slow. Obviously, we were doing the same tried and tested stuff. But then we launched two programmatic SEO projects that instantly elevated us to a level we’d have to work years for — look how fast the yellow line grew (pages) and how that corresponded with the orange line (traffic).

Organic performance graph in Ahrefs.Organic performance graph in Ahrefs.

The sunk cost fallacy is a cognitive bias that occurs when people continue to do something as a result of previously invested resources (time, money, effort) despite new evidence suggesting that the current path will not lead to a beneficial outcome.

Sunk cost fallacy as a graph.Sunk cost fallacy as a graph.
Sunk cost in action: the more you invest in something, the more attached to it you become.

We all know SEO is a long-term game, right? Strategies like these are crowded with long-term projects with big time and money investments. Sometimes, despite the investments, you just can’t go beyond a certain level of traffic, backlinks, etc.

Now, this mental model, this voice in your head, will tell you to keep going down the same path no matter what. Loss aversion kicks in, acting like a defense mechanism for your past selves and actions. And the more aggressive and blind the “hustle” culture is at one’s team, the harder it is to see clearly.

But, overall, it could be better for you and the company to let it go and focus on something else. You can even come back to it later with a fresh mind. But continuing something just because you’ve been doing it for some time is a losing strategy.

Example. Despite several attempts and time counted in years, Ahrefs doesn’t rank for “seo”.

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Position history for "seo" via Ahrefs.Position history for "seo" via Ahrefs.

Sad but true. And from our point of view, it’s frustrating. Almost like we’re the only ones not to get invited to the party, the only ones not to graduate from high school… you get the idea.

But not ranking for “SEO” hasn’t hindered our growth, so it’s better to cut losses and deal with unfulfilled ambition than to let that goal hold us back from other projects (like that programmatic project mentioned above).

Confirmation bias is the tendency to give more attention and weight to data that support one’s own beliefs, while simultaneously dismissing or underestimating evidence that contradicts those beliefs.

Confirmation bias - beliefs outweigh the facts. Confirmation bias - beliefs outweigh the facts.

We’re all guilty of this. It’s human nature. And it’s not exclusively a bad thing. I mean, in some situations, this tendency can keep us on “the bright side” and help us go through tough times or keep our motivation up.

So, I think that it’s not something to get out of your system completely. Just be mindful of situations where this can negatively affect your judgment:

  • Selective evidence in ranking factors. You see a page ranking high, and you think it’s because of an aspect you strongly believe in, disregarding all of the evidence against it (e.g., long-form content, social signals).
  • Bias in keyword selection. Your keyword selection runs along the lines of your beliefs about the audience preferences without substantial evidence to back up these beliefs.
  • Bias in strategy development. After developing a new strategy, you encounter a talk or an article advocating a similar approach, which immediately reinforces your confidence in this strategy.
  • Focus on confirmatory data during audits. During a content audit, you find a small piece of data that confirms your belief. As a result, you may prioritize minor findings over more significant but less personally affirming data.
  • Overconfidence in familiar tactics. Leaning on SEO tactics that have worked in the past, you develop a sense of overconfidence in them. You resist trying anything new or the idea that a dip in performance comes from an unfamiliar factor.

Keep learning

If you like what you’re reading, I think you will find other mental models fascinating:

Want to share models you find useful? Ping me on X or LinkedIn.



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PPC Experts On AI In PPC: Potential & Limitations

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PPC Experts On AI In PPC: Potential & Limitations

This is an excerpt from SEJ’s PPC Trends 2024 ebook, our annual roundup of expert opinions on what you can expect over the course of the next 12 months. 

This year, new AI features rolled out on PPC platforms, and marketers began adopting generative AI in earnest.

The dust is settling after the initial exuberance about AI, and we’re starting to see more nuanced and cautionary opinions develop.

In this section, you’ll see contributors highlighting the benefits of both AI-powered automated ad campaigns and adopting generative AI in your workflow.

You’ll also see cautionary words, reminding you that human thinking and creativity still drive online interactions.

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If I had to summarize these insights in three sentences, they would be;

  • AI is very good at the things it’s good at, and very bad at the things it’s bad at.
  • AI is a square peg, so beware of round holes; AI is not a panacea.
  • AI can be a multiplier of productivity and results, but some processes are worth the difficulty.

How AI Can Improve Social Media Advertising Performance

Akvile DeFazio, Founder, AKvertise

Akvile DeFazio

Artificial Intelligence (AI) is quickly becoming an integral part of the advertising industry, transforming how companies reach their target audience and how advertisers increase effectiveness and efficiency in managing ad accounts.

Here are some ways AI can help drive more results in 2024:

Targeting Improvements

Just a few short years ago, campaigns and ad sets were set up more granularly, but after iOS updates, Meta launched several new machine learning options that advertisers can leverage for better results and find their customers.

Now, in Meta Ads Manager, there are Advantage+ Audiences that leverage machine learning to help advertisers reach the most valuable audiences much faster.

By enabling this, you can also share an audience suggestion, such as recent purchasers, so then the system can prioritize people matching using this high-value audience profile before expanding the targeting net wider.

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If you work in ecommerce, Meta’s Advantage+ shopping campaigns can help find new customers using its automatic placements, lowest-cost bid strategies, and more by serving the best ads to the people most likely to convert using its AI.

Creative Optimization

When it comes to creative optimization, particularly on platforms like Meta Ads, running dynamic ads with various creatives can be highly effective.

Platforms like Meta leverage AI to serve your target audience with the most relevant creative content, increasing the likelihood of achieving your campaign optimization goals.

By trusting the system to determine the best approach, you can expect improved and faster results compared to manual testing by humans.

In this past year, its performance has improved significantly, and I believe it will continue to do so.

Measuring Results

AI also offers extensive analytics and reporting capabilities, enabling advertisers to measure the success of their campaigns accurately.

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With data-driven insights, advertisers can identify the most effective ads and targeting strategies, enabling them to make informed decisions for optimizing campaigns.

We use tools that allow us to import data, conduct trend analysis, create graphs, and obtain valuable insights.

By streamlining reporting and analysis, the right AI-powered tool serves as a time-saving asset that can guide optimization efforts and drive favorable outcomes.

This is only the start of the AI revolution transforming the social media advertising landscape. Brands can now connect and interact with their target audience in a more impactful manner and achieve their various goals.

Embracing AI experimentation can be worthwhile, as it elevates our human capabilities, increasing our efficiency, productivity, and effectiveness in our work.

If you haven’t already, add AI to your advertising stack to elevate your growth goals for 2024.

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AI & Personalization In Marketing

Alex Macura, Founder/CEO, Your Digital Assembly

Alex MacuraAlex Macura

The world is a fast-paced place, and the marketing industry is even more so. It has to be, just to keep up.

Over the past 50 years, we’ve seen growth in digital marketing, social media and mobile marketing, television, and database marketing.

But what does the future and, more specifically, 2024 hold for the industry as a whole? Let’s take a look.

A Surge In AI Marketing

AI gives marketers the ability to analyze huge amounts of data in seconds, boosting efficiency and productivity.

Predictive analytics can help to predict consumer and purchase behavior, allowing for more tailored, targeted ad campaigns.

And it can learn over time, too, constantly evolving into a more competent version of itself. So, if you’ve resisted getting on board the AI train, it’s time to step up to the platform.

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More Personalized Content

Another area AI excels in? Personalization – which is why, in 2024, hyper-personalization is set to become our new reality.

Customers want to feel seen, so any brand that takes the time to curate a buying experience specifically for them will gain traction.

Thanks to AI and advanced analytics, content can become more tailored than ever, strengthening brand relationships and boosting return on investment (ROI).


Finding The Balance Of Generative AI In Ads

Amy Hebdon, Founder + Managing Director, Paid Search Magic

Amy HebdonAmy Hebdon

There are many ways to use generative AI to enhance your campaigns – and only two ways to get it wrong:

  • Blindly rely on it for everything.
  • Refuse to use it for anything.

Generative AI is in its infancy and capable of making mistakes, so fully relying on it for 100% accuracy is a bad idea.

At the same time, avoiding it because it can’t completely replace you needlessly limits your ability to be more creative and productive.

Between those extremes are countless opportunities to improve and streamline your work. Use generative AI for discovery, challenging assumptions, brainstorming, iterating and refining ideas, editing, and strategy.

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You don’t need costly subscriptions to get started, either. The free version of ChatGPT is a great entry point to meaningfully improve your work and workflow.


Standing Out In A Playing Field Leveled By AI

Andrea Atzori, Director, Ambire

Andrea AtzoriAndrea Atzori

Automation serves as a formidable ally in streamlining the mundane aspects of our operations, such as campaign build and reporting.

By harnessing automation, we not only expedite these processes significantly but also diminish the likelihood of human errors creeping in.

Nevertheless, it remains undeniable that the very innovations ushered in by AI and machine learning (ML), if not managed, also bear the capacity to homogenize content, often yielding results that hover around the realm of mediocrity or average at best.

Consequently, if we do not settle for average but instead strive for marketing excellence, this pursuit involves leveraging the full spectrum of available data and tools to our advantage.

Only by adopting this approach can we mitigate rising costs and consistently deliver outstanding outcomes.

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Scale Isn’t Everything, Don’t Forget The Power Of Humans & Creativity

Ben Wood, Director of Growth & Innovation, Hallam

Ben WoodBen Wood

One trend we’ve been referencing for years is the growing impact of machine learning and automation on advertisers.

In 2023, we’ve seen a huge acceleration in technological innovation.

We’ve experienced the democratization of creative production via generative AI tools built into Google Ads and other networks, reducing cost and increasing the speed of production.

This has lowered the barrier to entry to platforms such as YouTube, and display formats for smaller advertisers with less budget to spend on assets.

We’ve also seen much-publicized advances in large language models (LLMs), enabling the development of scripts with limited programming capabilities, and offering huge economies of scale for campaign creation and PPC account expansion.

What we’ll start to see in 2024 are the second-order effects of generative AI. These are the less obvious ripple effects caused by AI over the longer term.

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Despite the increase in our capabilities to create ads at scale using generative AI, this might not enhance performance but could hamper it:

  • AI is already adept at creating ads at scale, such as automatically created ads and demand generation features in Performance Max.
  • It’s easier than ever for advertisers to get started and enable more features due to automated creative capabilities. The lower barrier to entry could mean users see even more ads than they’re used to.
  • Relying on automated creative may result in generic, feature-based ads.
  • Buyers will learn to tune out these ads.

Increased Value On Human Perspectives And Creators

As consumers learn to tune out to the homogenous advertising enabled by generative AI, we’ll see an increased desire for human perspectives and creativity.

We’ve already seen Google start to surface creators and influencers via their “perspectives” feature with the introduction of Search Generative Experience, and I expect this to bleed through into the advertising landscape.

Partnering with consumer-facing creators and influencers as part of your paid media strategy will increase in importance in the year ahead to maximize your reach across Google’s evolving search landscape and beyond.

Back To Basics: Creative-First Advertising

Today, we have so many channels to manage that it’s easy for things to become disconnected. What holds it together? A creative idea.

If your campaigns lack a coherent, consistent creative concept, your campaigns will not perform.

Sometimes, we get so caught up in the platform choices we forget about the message we’re trying to get out through them.

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With the advent of generative AI, I think creativity will be a key differentiating factor for successful campaigns. Starting with a strategy, then a creative concept should always come before media planning.

This serves as a golden thread – a compelling creative idea that ties all your marketing and advertising activities together and helps you stand out from the crowd.


AI-Powered Campaigns Deliver A Future Where Marketers Can Spend Less Time On Optimization

Corey Morris, President/CEO, Voltage

Corey MorrisCorey Morris

AI-generated content is not going away anytime soon and is inevitably making its way into AI-powered ad campaigns in 2024.

AI can craft descriptions, headlines, and ad copy tailored to your client’s campaign objectives, resulting in effective, personalized content.

This personalization is possible because AI can understand user behavior patterns and apply experimentation and winning results to campaigns in real time.

You can monitor and manage your client’s campaign performance in real-time, ensuring that your campaigns perform relative to your goals.

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Performance Max Campaigns

Performance Max campaigns will now utilize machine learning and artificial intelligence technology more thoroughly in 2024.

Performance Max campaigns, following search campaigns, are subjectively one of the most effective ways to reach a broader audience and achieve a higher return on investment.

Google now offers the option to upgrade various campaigns, including dynamic search ads and display campaigns to Performance Max campaigns.

Some current benefits of transitioning to Performance Max campaigns include:

1. Increasing creative assets.

The benefit of Performance Max campaigns utilizing your creative assets allows search engines to properly convert your search ad to best fit the intended user base on their search queries.

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Creative assets will now have more flexibility when changing any text in your ad copy.

2. Implementing inventory-based ads.

When your product data feed is connected to a Performance Max campaign, your ads will now function based on the inventory you have left in stock.

This can be a huge time-saving benefit because you won’t have to manually examine your product inventory amount.

The upgrades to Performance Max campaigns will ultimately lead to a higher usage rate with advertisers.


Automate Campaigns, Not Strategies: What Are You Doing & Why?

Tim Jensen, Sr. Search Marketing Specialist, M&T Bank

Tim JensenTim Jensen

As PPC managers move forward in a world of increasingly automated, “done for you” campaigns, fully understanding the concerns and goals of your client/boss will help set you ahead.

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This is not an excuse for not staying up-to-date with current ad platform functionality, but it’s too easy to drift into a “plug-and-play” mentality with the direction in which the PPC world is headed.

Setting up a conversion pixel is relatively easy these days (in many cases), but ask yourself why you are tracking that conversion, and how it ties into the business goals the company ultimately cares about.

Churning out 15 responsive search ad headlines is easier with AI, but will those stand out in the search engine results page (SERP) against creatively brainstormed headlines that speak to the heart of the customer’s needs?

Generating a list of keywords can be as simple as plugging a URL or a couple of seed phrases into Keyword Planner, but are those the most relevant terms that ideal customers are searching for?

On the positive side, increased automation in platforms has reduced the need for constant hands-on tweaking, such as in bid management. This frees up more time you can spend keeping the lines of communication open with the stakeholders you answer to.

Take some time in 2024 to think through how you can better understand stakeholder goals, and how to tie in your targeting, creative, and bidding approach to best meet those objectives.

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Expect Less Campaign Control – Find Exciting New Ways To Spend Your Time

Lauren Weisel, Director of SEM, Media.Monks

PPC Experts On AI In PPC: Potential & LimitationsPPC Experts On AI In PPC: Potential & Limitations

One major theme of 2023 has been automation, and I expect this to continue well into 2024.

Google continues to roll out campaign types that are heavily automated and give less control to marketers, starting with Performance Max and, most recently, with the launch of Demand Gen.

As Performance Max has evolved over the years, we see many cases where this automated campaign type works incredibly well.

With the recent rollout of Demand Gen campaigns, I suspect Google will continue to move toward either expanding these campaigns’ coverage, or rolling out more automated campaign types.

As Google continues to emphasize these automated campaign types, I expect the percentage of account spends on these campaign types to increase, as well. And beyond this, who knows!

There could be a world where traditional search campaigns as we know them sunset completely, but that’s merely a hypothesis.

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Speaking of traditional search campaigns, I’m also seeing a reduction in control with the emphasis on broad match with auto-bidding this year.

While many clients were skeptical of this new match type, it’s working quite well for many advertisers.

While still available, I’m also seeing less account spend go towards phrase match keywords, and many times without any performance losses for client accounts.

From an account structure standpoint, this rollout has, in a way, been a catalyst for campaign consolidation – a far cry from the SKAG structure I was taught early on in my career.

This reduction in control that advertisers are experiencing within Google will shift how search marketers work.

However, as I reflect on my career as a search marketer, I can point to other industry shifts that seemed huge at the time, but truly freed up time to expand my skillsets.

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I remember when auto-bidding strategies first came on the scene. What would I do with all my time freed up from daily bid adjustments? As automation evolved, marketers shifted how we spent our days (and thankfully, there was plenty of other work to be done).

As control becomes limited in the evolution of Google Ads, search marketers will need to become more creative with strategies to ensure that we continue to move search programs forward with the levers we can pull.

While automated, these campaigns shouldn’t be approached with a “set it and forget it” mindset.

It is a privilege to be able to educate clients and guide them in this ever-changing search landscape. There are so many testing and learning opportunities on the near horizon.

The search landscape has certainly changed a lot, especially over the past year.

While all this automation may seem scary, we must embrace automation to stay ahead of the curve. I suspect we’ll see the trajectory of automation continue to accelerate during the next year.

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Not only is this a hot topic in the search space, but in our culture as a whole. I look forward to all of the automation developments 2024 has in store for search marketers.


Searcher Intent & Audiences Are A Complex Human Formula

Lisa Raehsler, Founder And SEM Strategy Consultant, Big Click Co.

Lisa RaehslerLisa Raehsler

While AI and automation are always hot topics – and the technology advancements amazingly helpful – in 2024, connecting with the customer will be key.

Many advertisers will get away from this by buying into the fast and easy option: Allowing machines to do the work for their digital advertising.

That’s great for tedious task-oriented optimizations – but human strategy, experience, and even intuition will be critical for success in reaching and converting the right customer.

The pros are already in the know. Searcher intent and audiences are a complex human formula advertisers should focus on.

Societal culture, economic conditions, and political concerns change rapidly. Messaging targeting people who experience evolving needs and pain points should take center focus.

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Featured Image: Paulo Bobita/Search Engine Journal

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