Which of the following best explains why brand marketing to AI platforms will be more effective than showing ads to consumers?

Like in so many industries, the use of data and AI is transforming advertising at a rapid pace. Consumers are seeing these changes in the personalized ads on their web browsers, the chatbots that help them make buying decisions. But what exactly is AI-powered advertising?

AI in advertising refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions based on the information that is fed to them. They use historical data to learn from past experiences and use it to make smarter decisions in the future. Advertisers can use AI to create more personalized experiences, target the right audience, and make decisions faster.

What are the different parts of AI in advertising?

Machine learning capabilities

Cognitive advertising is driven by AI, and involves computer algorithms that analyze information - automatically improving experiences. Devices that utilize machine learning can analyze new information based on relevant historical data. This then informs decisions based on what has or has not worked in the past.

Utilizing big data and analytics

Big data has come to the forefront due to the emergence of digital media. It has also provided opportunities for marketers so they can understand how their efforts prove value across different channels. This has however led to many marketers struggling to determine which data sets are valuable to collect. Many marketers also struggle with data accuracy and keeping information up-to-date.

Effective AI platform solutions

Effective AI solutions can provide marketers with a central platform for managing large amounts of data. These platforms can derive insightful marketing intelligence about your target audience making it easier to make data-driven decisions.

The current state of AI

If advertisers are not currently implementing AI tools into their strategy, it’s probably time they start considering it. Recent studies show that AI technology is expected to grow significantly throughout this decade.  

AI in advertising can help organizations better segment audiences and target ads while measuring results. HubSpot* reports that today’s marketers are creating content for multiple audiences, with three audiences being the most popular. It’s not enough anymore just to target one audience and hope for the best. To drive real results, companies are using AI to carefully target niche populations and using contextual advertising and behavioral targeting powered by AI to get the right ads in front of the right people.

Measuring campaign success is also an important component of any advertising strategy. Being able to measure how a campaign performs allows advertisers to put their dollars where it matters most. In Deloitte’s State of AI in the Enterprise, 3rd Edition,* AI success has proven easy to measure, with 26% of all respondents and 45% of seasoned AI adopters saying that AI technologies have enabled them to establish a significant lead over their competitors.

The AI advertising industry will continue to evolve as the world becomes increasingly digitized, and there are a lot of opportunities for advertisers to take advantage of the technology on the market. Let’s explore more in-depth how AI is changing the advertising field and how organizations can leverage these insights to create a more cohesive strategy.

How is AI changing advertising?

AI is rapidly changing the advertising landscape due to its host of benefits and ability to get smarter over time. Here are just some of the ways organizations can leverage AI in advertising.

Personalization leads to better customer experiences

Personalization in advertising refers to the use of data or consumer insights to increase an ad’s relevancy to its intended audience. This can be data related to demographic information, interests, buying intents, or behavior patterns.

Increasing the relevancy of ads and making them personalized is becoming a top priority, with 80%* of those who classify themselves as frequent shoppers saying they only shop with brands who personalize their experience. Forty-seven percent* (47%) of B2C consumers note that brands could do a better job aligning their engagement activities with their preferences and 56% of consumers expect all of their interactions with brands or vendors to be personalized.

A personalized AI solution such as conversational marketing can help advertisers build these personal connections with consumers, improve the relationship they have with their brand, and create a better shopping experience. In fact, 71%* of customers expect companies to communicate with them in real-time, which is why using an AI conversational marketing solution to engage with customers and prospects has become increasingly popular. Personalized experiences help brands improve their ROI while strengthening customer loyalty and relationships.

Audiences can be segmented before ads are served

By leveraging machine learning, a form of AI, advertisers can find patterns based on audience behavior and message resonance. Machine learning does this by considering all information they have about a given individual, such as demographics and online behavior, to make an informed decision on the type of content a niche segment actually wants to see. Understanding an audience is imperative to increase engagement, which is why irrelevant content is the number one reason* consumers don’t engage more often.

Proper segmentation and audience targeting lead to more relevant advertising, which is reflected in a recent survey by McKinsey Analytics that found 14%* of users adopted AI for customer segmentation purposes. By delivering messaging that relates to the end-user, advertisers can expect higher engagement rates and more conversions.

Ads created through AI convert better

By being able to look at past trends and performances, AI can make insights that inform advertising decision-making and ensure that budget isn’t spent on ad copy that doesn’t convert. AI does this by going beyond traditional A/B testing to make data-based predictions about how creative and messaging will resonate with customers. This allows advertisers to move from reactive to proactive in their approach to creative, in order to generate more qualified leads and conversions.

A recent Salesforce Research report, “Enterprise Technology Trends,”* found that 83% of IT leaders say AI and machine learning are transforming customer engagement and 69% say it is transforming their business. Advertisers recognize the importance of targeting the right person with the right ad to drive conversions.

Advertising decisions are more impactful, improving ROI

AI is an analytical approach to advertising. AI-powered tools take in a vast amount of information and data to predict future trends and insights with accuracy. The great thing about AI is that it’s always bettering itself—like a human. It continuously learns and adapts as needed to continue making better decisions.

While advertisers have traditionally struggled with measuring the success of their campaigns, analytics helps companies determine what’s working and what’s not. Therefore, in the future, advertisers can be proactive in taking steps that will positively impact their campaigns. According to research by Deloitte,* 73% of AI adopters believe AI is “very” or “critically” important to their business today and 64% said AI technologies enable them to establish a lead over competitors. Targeting the right audiences, with the right message, every time, helps ensure a reduction in advertising waste and greater ROI.

Performance optimization also impact budget optimization and targeting

Performance optimization is an important use case for AI in advertising. Machine learning algorithms can analyze how your ads perform across different platforms, then offer recommendations for improved performance.

Challenges of AI in advertising

Advertising bias

Advertisers rarely have insight into how algorithms work and how unconcious biases may be coded into them. Advertising bias that is coded into the technologies used to deploy campaigns can have a negative impact on performance and ROI. Although bias can be a challenge for advertisers when dealing with AI, machine learning technologies can also help mitigate bias in campaigns, when deployed correctly.

Training time and data quality

AI tools may not automatically know the actions to take to achieve overall marketing goals. These tools require time to learn your organizational goals, as well as your customer preferences, and historical trends. In addition to time, this also requires data quality assurance. If the artificial intelligence tools have not trained with accurate, timely and representative data, these tools will make less optimal decisions, which can reduce the value of the tool.

Data privacy

Marketing teams must ensure they use consumer data ethically and in compliance with standards (like GDPR). If this is not followed, companies risk heavy penalties and reputation damage. This can be a challenge with AI. Unless tools specifically observe specific legal guidelines, they might overstep acceptable terms.

Advertising bias

As the industry increasingly relies on AI to segment audiences and run campaigns, more decisions are being made by machines. Marketers using AI technology try to remain objective in their decisions, however, CMOs often lack insight into how algorithms work and what biases might be built into these models. Decisions may then be made with unintentional signals such as age, race, or gender - introducing bias into the campaign. 

Getting buy-In

It can be difficult to demonstrate the value of AI to a company’s stakeholders. KPIs like ROI and efficiency can be quantifiable, but showing how AI improves customer experience is less obvious. Due to this, marketing teams will need to ensure they can attribute these qualitative gains to AI investments.

Trends to consider in AI advertising

The advertising industry is constantly changing and it’s important to keep a keen eye on what’s trending in the industry to stay ahead of competitors. Here are some trends to consider regarding AI advertising.

Traditional A/B testing is not the best or only way to create better ads

While A/B testing is the traditional way of testing an ad’s relevancy, leveraging predictive audiences can help your team create better ads and creative, before ads are served and measured for effectiveness. When AI can take in vast amounts of data to make recommendations before the budget is spent, you can save time and money on creating the right ads the first time around.

More businesses are investing in AI

Businesses are increasingly invested in AI and according to Forrester, the adoption of AI* continued to expand throughout 2020 due to the pandemic and the need for smarter, automated solutions. Investing in AI gives businesses a competitive edge, and if you haven’t already considered implementing AI-powered tools, you risk falling behind.

Customer behavior may change

According to Forrester, many companies experienced what they refer to as customer drift, where the patterns of behavior changed rapidly during the pandemic. Therefore, models that previously worked, may have changed. Advertisers must be able to take current information into their campaign models and have algorithms that can adjust to the changes in the market.

Cookies may be going away, but personalization isn’t

With new privacy regulations including the GDPR,* it can be difficult to target audiences without the additional information stored through cookies. Even with the loss of cookies, AI is still able to recognize patterns in a target audience, without needing to resort to identity-based advertising. AI can leverage contextual advertising, location data or weather-based triggers to better serve relevant ads and information to users.

Examples of brands using AI in their marketing

Pivot Bio uses IBM Watson Advertising Conversations

Pivot Bio was looking for a technology-first solution in order to reach, engage, and educate growers and agricultural workers on its new, sustainable crop nutrition solution, Pivot Bio Proven™.

With Conversations, Pivot Bio saw meaningful interactions coming from their target consumers, such as:

• 5% conversation rate
• 1.76 average user inputs per conversation

CVS Sponsorship of Flu Insights with Watson on The Weather Channel

To engage users during the height of flu season, CVS sponsored the Flu Insights with Watson tool on The Weather Channel app. Through IBM’s predictive illness data, CVS drove awareness and engagement while educating on their expertise in flu prevention in the moments that mattered.

CVS was able to reach millions of consumers in critical planning moments:

• 42M+ unique visitors
• 644M ad impressions served
• 120%+ internal benchmark CTR

How Watson Advertising can support your AI advertising strategy

Ready to learn more about how AI can help you make better advertising decisions? Book a demo with Watson Advertising today!

Frequently Asked Questions

How do companies use AI in advertising? 

Advertisers use AI to identify and segment audiences, build ad creative, test out their ads, mitigate advertising bias, improve ad performance, and optimize their overall spend. 

Will AI take over advertising? 

AI can not completely replace digital marketing jobs. Instead it will complement roles and help reduce manual efforts associated with data collection and testing.

How is AI used in digital marketing?

AI is used in digital marketing in the following ways:

• Targeting the right audience
• Delivering better creative
• Collecting data and delivering ads based on these insights
• Conversing with customers
• Creating personalized experiences

How does AI improve marketing?

AI makes it easier for marketers to tailor campaigns to their target audience. Marketers can ensure they are promoting the right creative to the right people at the right time. Additionally, AI makes it easier for marketers to scale their efforts. Through conversational marketing platforms, teams can make personalized recommendations without needing additional staff or increasing wait times for the customer.

Do targeted ads use AI?

Targeted ads are making the shift to AI, since cookies are disappearing. Companies who want to continue to deliver relevant advertisements will need to incorporate machine learning into their workflows and campaigns.

Why is AI useful for personalizing ads?

AI advertising can take contextual signals, such as the content on a page, weather data, and location to determine what ads may be most relevant to a user. Machine learning can also determine which users are most likely to take action, ensuring that you are targeting consumers who are more likely to convert. 

What industries should be using AI in their marketing?

There is an abundance of industries that can use AI to their advantage. These include the automotive, healthcare and retail industries.

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