Marketers are keying in on how artificial intelligence can improve their advertising strategies. As we’ve been witnessing, AI is changing the way online marketing is happening. With marketers keenly focused on how AI can enhance their advertising strategies, it's essential to examine companies that are leading the way.
What can we learn from them? We’ve gathered 11 of the best AI advertising examples so we can dissect what makes them great.
Discover:
- What kinds of AI are successful companies using?
- What areas of their advertising strategies are brands using AI with?
- The key takeaways for every example and what we can learn from each company.
What have the effects of AI in marketing been so far?
Effective advertising aims to reach the core of how people think in order to sell a product or service. Bringing AI into the equation, which can take countless amounts of human behavior data points, is bound to make waves.
Marketers no longer need to question what their ideal customers are thinking or doing online, because AI can decipher that.
Currently in 2024, the global AI market is valued at 142.3 billion USD. And while different studies show varying forecasts, it’s projected to reach about half a trillion USD in 2024 and about 1.5 trillion by 2030. Much of this funding has come from interest in AI startups - tech that will most likely be focused on helping companies like chatbots and generative AI.
11 best examples of AI in advertising
Let’s take a look at these 11 companies incorporating AI into their advertising.
1. Meta continues with their AI innovation
Image courtesy of Meta
While we’re all at least a little bit familiar with the machine learning algorithms that Meta has created to keep users scrolling, they’ve also used AI in other ways.
Meta has begun testing AI generated ads for Facebook with the announcement of their AI Sandbox for advertisers in May 2023. This testing ground has the possibility of bringing more advertisers to their platform and generating more successfully targeted ads.
Some of the areas advertisers will be able to experiment with are:
- Generating text variations
- Text to image generation
- Image outcropping to fit different aspect ratios
Image courtesy of Meta
Sandbox has the potential to put Meta in the position of having the best AI-driven mobile advertising platform.
What can we learn from Meta?
- Save time by using AI to create new image creatives. This is the direction the market is going.
- Continue to innovate your processes. Make space for your team to brainstorm and try out new technologies.
2. Coca-Cola runs AI contest and announces alliance with OpenAI
Image courtesy of Coca-Cola
Coca-Cola has been in the advertising game for a long time. Founded in 1892, their first ad ran in a newspaper in 1896. Since then, the company has continued to stay on top of trends and kept their advertising modern.
In 1955 they turned to radio and televised ads, and in 1993 they made another switch to a new advertising company to keep their ads fresh. All this to say, it’s no surprise that Coca-Cola has now embraced the use of AI.
In February 2023, management consulting firm Bain & Company announced a global service alliance with OpenAI in order to integrate AI into their internal systems. Coca-Cola was the first company after this announcement to take part in this alliance.
In order to kick off this partnership, Coca-Cola launched a contest called “Create Real Magic”. They invited users to combine ChatGPT, DALL-E and historic Coca-Cola ad creatives to create new works of art that would be shared on the website.
What can we learn from Coca-Cola?
- Stay on top of current market innovations and use AI wisely.
- Find ways to involve your customers and increase brand awareness.
3. Calm App uses Amazon Personalize to increase app usage
Image courtesy of Calm
As a part of AWS (Amazon Web Services) there’s a product called Amazon Personalize. This allows developers to show shoppers super personalized product recommendations in real time through the use of machine learning.
Because of their ever expanding library of content within the app, Calm needed a solution to help users find the right content for them. If a user is spending too much time browsing, they may give up and exit the app due to not finding anything relevant to them.
They introduced a dynamic rule that recommended users the most popular content, in this case Sleep Stories, in the style that a user showed a preference for while removing the Sleep Stories they had already listened to. This kept the recommendations fresh and aligned with the user’s preferences.
Through training Amazon Personalize with Calm’s data and a lot of testing, they were able to increase their daily app use by 3.4%.
What can we learn from Calm?
Users can be excited about your product, but there may be challenges with growth if it’s not easy to use. Implement AI into your systems to give users a better experience.
For example, if you’re an online retailer, the more specific your product recommendations are, the more likely shoppers are to add those products to their shopping cart before checking out.
4. Nike creates AI generated ad with Serena Williams
Image courtesy of Nike
Another company that has been pioneering in the AI territory is Nike. In 2018 and ‘19 they acquired predictive analytic companies and then used that data to get a better understanding of their customers’ habits.
The Nike Fit app is a perfect example of how the company has used this data to market to their customers. By combining AR (augmented reality) and AI, users are able to scan their foot in the app and then get perfect shoe recommendations based on the scan.
Nike also used AI to create an ad campaign called “Never done evolving” with Serena Williams. They created an AI generated match between Serena’s younger self, specifically her first Grand Slam in 1999 and a more modern version of herself from the 2017 Australian Open.
This award winning, 8 minute video ad commemorated Nike’s 50th anniversary.
To promote this ad, Nike set up a livestream on YouTube broadcasted to their 1.69 million subscribers at the time.
What can we learn from Nike?
Make use of the historical data your company has. People also connect with stories in advertisements, so find ways to use AI to emotionally connect to your audience.
5. ClickUp grows blog traffic by 85% with SurferSEO
Image courtesy of ClickUp
ClickUp was able to enhance their content marketing strategy with the use of AI. Their goal was to optimize their content while increasing both the quality and quantity of their output. With an already established blog of 500+, they needed a boost that would go beyond the surface and take them to the next level.
They used SurferSEO for the job, a generative AI tool that claims to also boost traffic through content optimization. SurferSEO met their needs of a tool that combined quality SEO suggestions, SERP data all in one place, all while being easy to integrate and use.
The variety of tools enabled them to improve in these key areas:
- Granular insights during content planning, like which keywords should be targeted.
- Creating thorough content briefs to make sure all the important information is included.
- Monitoring performance and getting data-backed insights into changes that should be made
As a result, they were able to publish over 150 articles and grow their non-branded, organic traffic by 85% over the course of 12 months.
What can we learn from ClickUp?
Thorough content optimization allowed ClickUp’s blog to start generating more traffic to their website and in turn, increased conversions of their product.
When creating a blog for your website, keep this in mind. Quality content that ranks high on the SERP can bring shoppers who are looking for the specific solution you provide, right to you. Even established companies need to work hard to stay competitive. Generative AI can help you output a large amount of articles even if you have a small team.
6. BMW uses generative AI to create new advertising campaign
Image courtesy of BMW
Following in the direction of displaying art on their car models, BMW teamed up with an advertising agency named Goodby, Silverstein & Partners. Together they created a new campaign in 2021 for the 8 Series Gran Coupé in which they projected AI generated art onto the cars.
When advertising luxury cars, the manufacturers need to connect with their customers emotionally to convince them that they need this particular car over one that is perfectly functional, but cheaper.
By imposing AI art onto the car, BMW was able to speak directly to their target audience without even using words.
What can we learn from BMW?
Find a way to connect with your customers and target audience that goes beyond listing the benefits of your product or service. You can attract and retain customers through what you support as a brand, like BMW embracing art throughout its history.
7. Starbucks creates their own AI platform ‘Deep Brew’
Image courtesy of Starbucks
Starbucks has created their own AI and machine learning program called Deep Brew. Their aim with this suite of tools is to stay true to offering a human experience to their customers while also adapting to the technology of modern times.
They first started implementing AI into their mobile application in order to provide personalized recommendations to their customers using the app to order drinks. But over the years, AI has now also made its way into their physical stores.
Deep Brew has been able to collect and analyze large amounts of data such as modifications made to drink orders and peak service times at specific locations. Starbucks has used this data in order to:
- Find the perfect location for new stores
- Saving time with manual tasks like inventory management
- Performing maintenance on their espresso machines
- Provide personalized order recommendations at drive-thru windows
What can we learn from Starbucks?
Using AI can help you remain aligned with the original goals and ideals your brand started with. Starbucks has used AI to help free their employees from manual tasks, therefore giving them more time to connect with customers in store. When customers know they can rely on the same service every time they have an interaction with your brand, they are more likely to return.
8. Farfetch uses AI in email marketing and increases open rate by 7%
Image courtesy of Farfetch
Farfetch is a luxury online marketplace for fashion and beauty products. Their goal was to increase the open and click rates of their emails through the use of AI, all while remaining true to their brand’s tone of voice.
In order to do this, they used Phrasee, which is a generative AI tool geared towards enterprises. Some of the ways Farfetch used this tool are:
- Testing different phrases and writing styles to find what worked best with their audience.
- Optimized email subject lines for different email categories (like abandoned cart messages, or contacting users about the items they put on their wishlists).
- Personalizing the body of the emails to fit their wide clientele and variety of brands they sell.
Through this combination of optimization and double checking the content generated to make sure it adhered to the brand’s voice, Farfetch was able to see good results.
The open rate of their emails increased by 7% for promotional emails and by 31% for emails triggered by events (like abandoned carts). The click rate for these emails also increased by 25% and 38%.
What can we learn from Farfetch?
AI shouldn’t come into your current strategies and upend core parts of your brand like the tone of voice you use. It should enhance what already makes your brand unique and make it easier to achieve your company goals.
9. JPMorgan Chase increases CTR by 450% with AI
Image courtesy of JPMorgan
This is an example of a very early adopter of AI in marketing, relatively speaking. All the way back in 2016, Chase started using Persado and in 2019 they signed a 5 year deal with them.
In this time, they’ve used Persado’s generative AI to create ad copy and saw up to a 450% increase in clicks.
They also used the tool to rewrite existing marketing copy to make it more appealing to customers. When talking about the benefits of AI for the company, CMO of JPMorgan Kristin Lemkau said, “It rewrote copy and headlines that a marketer, using subjective judgment and their experience, likely wouldn’t have. And they worked.”.
Another way they planned to use Persado’s vast data was to create personalized marketing messages for specific audiences.
What can we learn from JPMorgan Chase?
When trained with the right data, AI proves to be a reliable and unbiased resource. That, coupled with the sheer amounts of data it can process, makes it useful for interpreting human behavior. Having access to this kind of information helps advertisers and marketers speak more effectively to their target audiences.
10. Netflix shows hyper-personalized recommendations with AI
Image courtesy of Netflix
Netflix has incorporated AI algorithms and machine learning into many aspects of their company. The most well known however is the personalized film and show recommendations it provides to their users.
The company has even taken personalization to the next level by changing the thumbnail of a film or show that they display on a user’s Home tab based on what they’ve watched before. This is why you might be at a friend’s house and see different images than on your own account.
The thumbnails themselves have also been created using Aesthetic Visual Analysis (AVA) to select the right scenes out of thousands available that will most likely catch the attention of viewers.
What can we learn from Netflix?
With people’s ever-shortening attention span, you only have mere seconds to catch someone’s attention and convince them to choose your product or service. Through automated A/B testing you can discover what images or messaging will make the most conversions with your target audiences.
The same concept with dynamic ads, like Google's Dynamic Search Ads, is at play here, the use of AI allows customers to see the images and ad copy that will be most effective with them.
11. Nutella sells 7 million unique jars with AI generated labels
Image courtesy of Nutella
To end on a short and sweet note, let’s take a look at how Nutella has used AI in their advertising strategies.
Nutella created an advertising campaign where they enlisted AI to generate 7 million unique labels for jars of Nutella. No two special edition jars were the same.
Every single jar sold.
That is some powerful advertising!
What can we learn from Nutella?
People love to feel like they’re taking part in a one-of-a-kind product. Play to this need by creating unique experiences for your customers through the use of AI.
14 key takeaways for AI advertising
Let’s summarize everything we’ve learned from the companies above about how to successfully use AI in advertising.
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Use AI to generate image creatives for ads,
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Leave space for innovating your advertising processes with AI.
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Make wise AI decisions.
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Keep a finger on the pulse of emerging AI trends.
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Use AI in an interactive way with your customers.
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Simplify user experiences with AI.
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Use your company’s historical data.
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Conduct thorough and high quality content optimization.
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Use AI to advertise in a way that connects with your customers beyond communicating your selling points.
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Keep your company’s original goals and ideals at the forefront of using AI in advertising.
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Keep a strong brand voice when using generative AI.
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Use quality data so AI can offer an unbiased voice of reason.
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Test, test, test! Speed up results with automated A/B testing.
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Use AI to create one-of-a-kind experiences for your customers.
5 actionable tips for using AI in advertising
Use these tips to elevate your PPC campaigns with AI.
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Prepare for Search Generative Experience
While still in testing, Google’s new Search Generative Experience will change how users get information from the search engine. It will also change how people shop online, since Shopping results may appear more integrated with search results.
Preview of Search Generative Experience
Stay on top of these updates so you can optimize your ads for them. From the examples Google provided, it looks like product descriptions could play an even more important role to Shopping results.
Tip: Optimize your product descriptions with DataFeedWatch by pulling information from your whole product feed and arranging it in an enhanced way.
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Use Smart Bidding and broad match
Use broad match with your Google Search ads to go beyond just targeting keywords. Broad match uses AI to target searches related to your target phrases and exact matches to reach more customers.
Broad match also pairs well with Smart Bidding, an AI-driven bidding strategy that sets the most beneficial bid depending on the ROI goal you chose. This relieves you from the manual task of tweaking your keywords and ad groups.
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Target new audiences
As 3rd party website cookies are due to become a thing of the past, marketers need another reliable way to target and retarget extremely specific groups of online shoppers. This is where AI comes in.
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Performance Max
Benefit from Google’s AI by creating a Performance Max campaign. This allows your products to be advertised across the entirety of Google’s channels.
First you choose your preferred settings when creating your campaigns, like:
- Campaign objectives
- Conversion goals
- Audience signals
Then Google’s AI takes all of the assets you’ve uploaded to create personalized ads that are shown to new, niche audiences resulting in increased conversions.
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Lookalike audiences
Meta made a machine learning algorithm for Facebook and Instagram ads that creates audience lookalike groups without having to collect data from specific users.
By using this targeting option you can show your products to users that have similar interests to your existing customers.
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Create new image and video ads
The use of generative AI is creating big changes in the PPC world.
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Use AI to create product images
Facebook and Instagram
Experiment with creating different styles of images generated with AI for your ads on Facebook and Instagram. Start with a small batch so you can easily evaluate the results. If you don’t have access to testing Meta’s Sandbox yet, you can use another application in the meantime.
For example, Figma has a plugin called Text2Image you can use to create images for Facebook ads.
Tip: Use DataFeedWatch to easily upload and optimize images within your product feeds.Once you have all of your new images you can add them to a spreadsheet. The first column should be a unique key and the second the image URL. Then you can use the DataFeedWatch functionality called lookup table to supplement your product feed with the new images.
Google Shopping Ads
Google has also announced the creation of Product Studio that has the same concept as Meta’s Sandbox. With it, you’ll be able to use generative AI to make new ad creatives using isolated images of your products. This will be integrated directly into Merchant Center Next.
Preview of Merchant Center Next
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New AI-powered Google campaigns
Keep an eye out for the release of two new campaigns that Google has announced. The first, called Demand Gen, will pull your best performing video and image assets and show them on ads across Youtube, Gmail, and Discover.
The second, Video View, will show your video ads while users are browsing YouTube and while watching videos, all from a singular campaign.
Preview of Video View campaign
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Use predictive analytics
Take advantage of Google’s predictive analytics. With it, you’re able to track:
- How likely a user is to make a purchase based on if they’ve been active on your app or webpage in the last 28 days,
- If a user who was active in the last 7 days will stop being active in the next 7 days.
How much a user who was active in the last 28 days is likely to spend in the next 28 days.
Use optimized feeds to automate your PPC strategy
Optimizing your product feeds means taking the raw product data you already have and enhancing all of the attributes (titles, descriptions, images, etc.) in order for your ads to have optimal performance.
By using integration software to upload your feed to Google Shopping channels, you will be able to further automate your PPC strategy and also:
- Have perfectly optimized product feeds: Engage rule-based feed optimizations across all your campaigns in order to have the best title, descriptions, images, and more.
- Avoid gaps in product info: Fill in any missing data without having to go into your feed and manually add information.
- Reduce wasted ad spend: Remove unprofitable or out-of-stock products automatically.
- Avoid errors altogether: A feed review will automatically run before your products are submitted to the channels you’re selling on. This is especially useful for Google Ads because errors can penalize your account.