How and Why You Should Build an AI Advertising Strategy

The advertising industry is an industry of creatives. Despite perceptions, advertisers have still found ample use cases for AI. They have adopted AI advertising to segment audiences, build ad creatives, test them to improve ad performance and optimize spending.

As an advertiser, it is important to learn the newest industry trends. Read on to learn how advertisers are making use of AI for advertising to spend less time on rote tasks and more time working strategically.

Why do you need AI for advertising?

AI has become an active part of the advertising industry and for good reasons. Brands have benefited from the adoption of AI in their advertising strategies. Below are a few reasons why you should also consider leveraging AI in your advertising campaigns.

• To identify new advertising audiences and conversion opportunities, as well as build richer audience profiles : AI uses complex algorithms to analyze large amounts of data like interests, behavior and demographics from various sources to build more comprehensive audience profiles.

These provide better insights into what kind of advertisements would resonate better with the target audience. The ability to constantly monitor social media and the activities of visitors on the websites also allows for identifying new audiences.

• Gain insights into competitors' ad spend, creatives, and strategies : AI can analyze data available publicly on the internet like ad libraries of social media platforms, financial reports and statements and even private sources like data management platforms to identify what ad formats and messaging competitors are using and what keywords they are focussing on. This information can greatly help advertisers adjust their advertising campaigns to better compete with other advertisers.

• Predict ad performance before launching campaigns to make data-driven decisions that improve ROI : AI can help advertisers predict the performance of their ad campaigns using predictive analytics. It can also analyze historical data and patterns and trends to determine how different variables affect their campaign. This allows advertisers to make more informed and data-driven decisions.

How to build an AI advertising strategy

AI advertising is better than your regular advertising campaigns in so many different ways. Let’s take a look at how AI has helped advertisers by doing the repetitive parts involved in day-to-day marketing tasks:

1. Identify and segment the audience

Analyzing large databases of consumer data and recognizing patterns is very difficult and time-consuming for humans. On the other hand, AI can do this more efficiently and in a much shorter period of time. AI can then target specific groups of people based on characteristics or preferences to deliver ads. This drives up sales as well as the engagement ratio.

Take a look at how AI does this in more detail.

Text analysis: Natural Language Processing (NLP) is used to analyze text data, such as social media posts, search queries, and website content, to identify keywords, sentiment, and other relevant information. This analysis helps advertisers understand consumer interests and behaviors, allowing them to tailor their messaging and ad targeting accordingly.

Image analysis: AI can also analyze images to identify objects, scenes, and emotions. This analysis helps advertisers understand visual content that resonates with consumers and optimize ad creative accordingly.

Clustering: AI uses clustering algorithms to group similar consumers based on their interests and behaviors. This analysis helps advertisers identify consumer segments and optimize ad targeting to reach the most relevant audiences.

Predictive modeling: AI uses predictive modeling algorithms to forecast consumer behavior, such as purchasing decisions or engagement with ad content. This analysis helps advertisers optimize ad targeting to reach consumers who are most likely to take desired actions.

2. Build an ad creative

While creating content for advertisements like scripts, jingles and copies is a creative task and ideally done by humans, several AI tools like Jasper AI, ChatGPT, copy.ai and others have popped up with time that create them for you. The creation time of such content with the rise of these AI tools has decreased considerably.

Likewise, AI tools like synthesia.io considerably reduce, if not eliminate the need for long hours of animating and recording to create videos for advertisements. Similar AI tools also allow you to edit your videos using the AI easily. This can help you immensly in Youtube advertising.

3. Personalize the ad campaign

While humans normally focussed on creating ads that would appeal to the masses, it was later understood that the key to a successful and effective campaign was the specificity of ads. Propagating ads to an audience who may or may not be interested in your services is inefficient and expensive.

AI has the capacity to understand each customer on a deeper level by understanding their preferences and demographics as well as analyzing their past browsing history and behavior. It uses this understanding to deliver highly personalized ads to customers based on their preferences.

4. Automate ad placement and bidding

AI algorithms can analyze large data sets in real-time to determine the likelihood of a user clicking on an ad and converting. This real-time data can allow advertisers to make more informed decisions about which ads to bid on to increase conversions and improve their ROI and ad performance.

AI can also take it a step further by automating the bidding. It can make decisions based on the same principle as before and promote ads to customers with higher chances of conversion. For example, if the AI algorithm determines that a user is highly likely to convert, AI can increase the bid in real-time to ensure that the advertiser wins the impression. This can also lead to lower costs per acquisition.

One of the most popular use cases of AI in this context is in programmatic advertising. Let’s take a look at what they are and how they use AI for ad placement and bidding.

Programmatic ads and display ads: Display ads are a type of digital advertisement that usually include images, graphics or texts and can appear on websites, social media or mobile apps.

Programmatic ads, on the other hand, is an automated process of buying and selling of digital ad space across a wide range of digital platforms without human intervention.

Take a look at how programmatic ads a different from display ads:

Display Ads	Programmatic Ads
Manual process of trading	Automated process with real time trading
The process is time consuming	The process occurs in milliseconds
Predetermined price of ad spaces	Price is based on supply and demand with real-time bidding
Analysis and optimization occurs after the campaign is over	Real-time analysis and optimization throughout the campaign for best results
Has the potential to be more expensive as compared to programmatic ads	Reduced cost of advertisement and better return on investments (ROI)

The whole process of programmatic ads happens in milliseconds:

• When you visit a website, the website sends a request to an ad exchange for ads to fill up the ad space on the website.

• The ad exchange then sends this request to the supply-side platform (SSP), which in turn sends this request to an ad server.

• This ad server decides which ads to show on the basis of user data, ad formats, etc. and forwards the request to the demand-side platform (DSP), the system that allows advertisers to buy desired ad spaces.

• The DSP decides if they want to bid on the available ad space and the advertiser with the highest bid gets to display its ad on the website.

5. Optimize the ad performance and spending

AI uses user data to analyze and understand audiences' preferences. It is able to understand which kind of ads would perform better with which sets of audiences. These can then be categorized based on age, gender and location, among other more subjective filters like preferences and interests.

This helps in optimizing the performance of the ads. What this means is that the ratio of success as well as the ROI dramatically increases when the costs involved and the attempts to reach people who are not interested are skipped altogether.

AI can also test and analyze different versions of ads. AI algorithms use real-time data to identify the ads with the most engagement, clicks and conversions. It then automatically adjusts the budget to prioritize the most effective campaigns while reducing the budget on the underperforming ones. This is specifically helpful for brands that have a large number of ad campaigns running at the same time, which may be too difficult to manage manually.

Top 5 AI advertising tools

While there are a plethora of options available today for various tasks. Below you can find 5 AI tools that can help you in your advertising campaigns for better results.

Phrasee

Phrasee's AI algorithms analyze the company’s brand voice, target audience and performance of previous campaigns to generate high-performing marketing copies that drive engagement and conversion rates. It can be integrated with email marketing for email advertising, social media marketing or any other marketing channels.

Smartly.io

Smartly.io is a social media advertising platform. It uses AI and machine learning to automate and optimize ad campaigns. The AI-powered tools on smartly.io can also generate thousands of ad variations and identify the most effective ones.

AdCreative.ai

AdCreative.ai is an AI-powered creative platform that generates high-performing digital ads for advertisers. You can simply tell the AI your target audience and the platform you’re creating your ads on and it will generate a digital ad focusing on your target audience’s pain points.

Google Ads

Google Ads is one of the leading advertising platforms. It uses its search engine to display highly optimized and personalized ads with the help of AI and machine learning. It also allows you to track and analyze the performance of your ad campaigns. It also uses AI to help optimize ad performance by automatically adjusting bids and targeting the most relevant audiences.

Adext AI

Adext AI uses AI to analyze data from ad campaigns and identify the best-performing ad variations and targeting strategies. It even optimizes advertising campaigns by making data-driven decisions. It can even connect with other major advertising platforms like Google Ads and Facebook Ads to optimize campaigns across multiple channels.

6 real-world examples of AI advertising

We have learned how AI has helped the advertising industry and have impactful it has been. Let’s take a look below at how some brands have used AI in their own marketing strategy and to what effect.

Pepperoni Hug Spot

The Pepperoni Hug Spot Ad is an ad that was created completely only using AI. The ad was created using machine learning algorithms that analyzed thousands of ads and social media posts. Here’s how the process went:

1. The script was written by GPT4

2. The images were generated by Midjourney

3. Video clips were generated by Runway Gen 2

4. Music was generated by Soundraw AI Music

5. Graphics and editing were done in Adobe After Effects.

You can check out the finished product below.

Tomorrow Sleep

A mattress company called Tomorrow Sleep used an AI tool called MarketMuse to build content strategies. The company was able to learn about high-value topics and also about the topics that no other competitor was talking about to talk about the vital gaps.

Tomorrow Sleep was able to increase its traffic by 10,000%, with 400,000 monthly visitors. You can read the case study here.

Amazon

Amazon uses AI to study data in real-time and analyze the shopping habits of its customers to automatically adjust the prices of ads based on factors such as demand and competition. The prices are set such that they are more likely to result in a purchase.

This causes the average product to change cost every 10 minutes. Dynamic pricing boosted their revenue by 25% and the technique is still extensively used on Amazon.

McDonald’s

McDonald wanted to generate visibility around McCafe's limited-time specialty coffee offerings. It used IBM Watson Advertising’s location data and store locators to drive the results.

The results were:

1. Close to 5,000,000 visits

2. 168% more efficient cost per visit than category benchmarks

3. 79% of exposed users visited McDonald’s restaurants within 3 days

You can read the case study here.

Walgreens

Knowing that weather conditions play an important role in medical conditions like allergies, flu, cold, etc. Walgreens used IBM Watson Advertising Weather Targeting to reach a consumer when and where symptoms were most likely to be filled.

It was able to identify the right consumers at the right time, which resulted in a rise in overall store traffic. To be precise, there was:

1. 379.98% lift in store traffic with Native Ads

2. 11.29% lift in store traffic with Rx Total Flu Messaging

3. 24.39% lift in store traffic with Cough & Cold Symptom Messaging

You can read the full case study here.

Google

Google is one of the leading ad service providing platforms. It holds a huge amount of customer data which it uses AI to analyze. It even uses AI to manage ads like targeting the desired audience based on various factors like region, age, gender, etc.

Google uses AI for real-time analysis of the performance of various ads and tweaks them as and when required in real-time. This helps advertisers to generate better ROI with less costs involved.

Ethical challenges of using AI in advertising

While AI has provided the advertising industry with numerous positives, it cannot be said that there have not been any challenges on the way. There have been instances of mishaps where the outcome wasn’t as expected.

Potential for bias

Pepsi’s Kendall Jenner ad of 2017, which was created using the information gathered from social media received a lot of backlash. The AI system analyzed popular cultural themes and created a concept but failed to realize the seriousness of the issues. Trivial issues like black lives matter, police brutality, etc. were shown to have been resolved using a can of Pepsi.

This was just one of the examples to show how human intelligence is a necessity to understand matters that involve sentiments and social and political opinions because AI fails to do so.

There is also a high potential for bias based on the data that has been fed to the AI. Biasness may arise towards a specific race or gender etc.

Another example is when Amazon developed an AI algorithm in 2018 to help with its hiring process. It was found to be biased against women and people of color.

Transparency and accountability

While AI can analyze and understand huge amounts of data, it becomes very difficult for consumers to understand how and what kind of data is being collected and utilized.

Also, the algorithms used by AI are too complex for humans to understand and hence make it very difficult to understand how things are working.

Likewise, when there are complications and backlashes due to the processes involved or any mishap, it becomes very difficult to assign responsibility for such ethical and legal violations.

Training data quality

While AI can do tasks more efficiently and cost-effectively than humans, it fails to differentiate between right and wrong. At the end of the day, it is a machine that lacks judgment.

AI relies on information available to it to perform the tasks given to it. So if you want information on a topic, AI can only produce it for you if it is already available on the internet. In the absence of information, AI fails to give you what you ask for. Also, it doesn’t have the ability to check for the legitimacy of the information.

On the other hand, more repetitive tasks are performed as a result of the information fed to AI. It is able to analyze that data and make sense of it; but if the data itself is flawed, AI still works on the same data and the outcome may not be as appealing. In short, the data that is used to train AI to do a task can be substandard or untrue, but AI will still work with it to produce results.

Data privacy

In this digital age, consumer data is being sold to brands to be used for targeting and personalizing their advertisements. AI now has access to such consumer data. Brands today even use AI to collect data.

Due to the complex algorithms that AI uses, it becomes very difficult to know what data is being collected and in what ways. It is also rather scary to realize that we don’t know what data on us is being used for what purpose. So it creates a rather uncomfortable situation where our information isn’t private.

Wrap up

The advertising industry is benefiting from the use of AI in various ways, such as targeting the right audience, personalization, optimizing ads and placement of bids.

Many brands have already leveraged the use of AI into their advertising campaigns and other brands are already following suit. Thus, the demand for AI is only going to rise and brands must leverage the power of it to improve their advertising campaigns and accelerate business growth.