Artificial intelligence (AI) isn’t just about robots.

Its influence across all industries is undeniable, with changes being felt in medicine, education, transportation, and even advertising. Whether it’s through personalized ads or recommendations, AI-powered advertising is helping companies in their decision-making processes.

According to Deloitte, 45% of seasonal AI adopters claim that AI technologies helped them to establish a significant lead over their competitors. And with the advertising industry becoming increasingly digitized, advertisers have a lot of opportunities to take advantage of AI tools to create a more cohesive strategy.

In this article, we’ll cover a few things you need to know about AI in advertising, including:

1. Advantages of AI in advertising

Personalization

AI can easily collect user information to offer personalized recommendations, which are key in modern marketing. Persado, for example, is a tool that makes use of natural language algorithms to generate personalized wording in sales copy.

Better audience segmentation

Machine learning helps advertisers detect patterns in customer behavior. AgilOne is a platform that allows marketers to enhance both website and email communication, and it switches approach according to user behavior.

Ad creation

Some AI platforms are working towards the complete automation of advertising systems, including detecting audiences, executing ad bidding, targeting markets, and more. AI is also beneficial for tracking sales and monitoring ad spending, with social media platforms using the technology to assess their ads.

2. Disadvantages of AI in advertising

Getting buy-in

Showing the value of AI to company stakeholders can be difficult. Demonstrating how AI helps to improve customer experience isn’t as easy as quantifying efficiency and ROI, so it’s vital for teams to show that qualitative gains can be attributed to AI investments.

Data quality and training time

AI tools need time for training so they can learn an organization’s goals, historical trends, and customer preferences. These tools need to be trained with timely, accurate, and representative data so they can make optimal decisions.

Data privacy

Using consumer data ethically and in compliance with standards, like GDPR, is essential. AI tools need to observe these legal guidelines, otherwise companies can risk heavy penalties and damage to reputation.

3. How AI is used in advertising

Recommendations

The streaming platform Netflix, for example, uses predictive analytics to give customers a more accurate list of recommendations.

Social media

Facebook uses a text understanding engine, DeepText, to analyze and interpret content within its platform to find meaning. Thanks to machine learning, it can then offer relevant ads to users based on that content.

Search engine analysis

Google’s RankBrain, a machine learning-based search engine algorithm, analyzes and organizes search queries, which helps Google to offer more accurate results.

User data

Looking for ways to create user-friendly ads? AI can be extremely useful in producing relevant content, conducting research, and analyzing user data.

4. Different parts of AI in advertising

a. Effective AI platform solutions

Marketers can benefit from AI platforms that manage large amounts of data, offering essential marketing insights about the target audience and allowing for data-driven decisions.

b. Machine learning capabilities

Computer algorithms analyze data to automatically improve experiences, and having machine learning-enabled devices helps to analyze new information based on historical data. This leads to informed decision-making.

c. Big data and analytics

Digital media has brought big data to the forefront, offering opportunities to marketers and helping them understand how their efforts provide value across different channels. But it’s important to know which data sets are valuable to collect, and to keep information up-to-date and data accurate.

Changing customer behavior

Companies can experience customer drift, with behavior patterns changing very quickly. Models that used to work may not work anymore, and advertisers need to be able to take current information and feed it through algorithms that can easily adjust to market changes.

Traditional A/B testing - enough for better ads?

Companies can create better ads by leveraging predictive audiences, instead of using traditional A/B testing. AI can take in large amounts of data, making better recommendations before budgets are spent. This means you can save both time and money by creating the right ads the first time.

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What are predictive audiences?

Audiences that have at least one condition that is based on a predictive metric. Predictive audiences use AI to learn about customers, so you can know about their spending habits and product preferences.

Increased AI adoption

AI allows for a competitive edge in businesses, with the global AI adoption rate having grown to 35%. Artificial intelligence is helping companies address a variety of issues, such as talent and skill shortages, and meeting sustainability goals.

Increased customer personalization

Privacy regulations like GDPR make it more difficult to target audiences without having the additional information that cookies store. AI can, however, still recognize patterns in audiences, without identity-based advertising.