The increasing complexity of the advertising landscape means that marketers need better ways to manage, understand and scale their approach. Brands faced with these challenges must adopt new technologies to help address key risks going forward.
Artificial intelligence (AI) presents a powerful solution to many of the challenges facing marketers today, including the removal of third-party cookies, changes in regulations and the increased demand for privacy. AI can help marketers make better decisions because it can rely on real-time data to determine who is most likely to convert.
In this article, we explain what AI technologies are available to marketers and how brands can use AI to make better decisions and deliver optimized experiences at scale.
What is AI in marketing?
As advertising technologies evolve, marketers are turning to AI to improve their advertising and marketing efforts. According to a Forrester survey commissioned by IBM, 63% of advertising technology leaders in the US are turning to AI solutions to address challenges caused by data deprecation.
AI methods such as advanced data models, powerful algorithms, and machine learning can be leveraged to discover actionable consumer insights. These insights can be used to optimize campaigns, create highly targeted messaging and determine when consumers are ready to make a purchase. By using contextual signals, accurate weather forecasting data, and other important insights, AI can deliver actionable insights to help convert users.
Marketing and AI technologies
AI technology is moving fast. Because of this, many marketers are missing key opportunities presented by newer and more advanced methods, including:
- Advanced machine learning. Machine learning is a type of AI that enables predictive algorithms to become more accurate over time. Machine learning can be used to identify the most relevant audiences and predict creative elements that will resonate most with these audiences.
- Natural language processing. Natural language processing gives computers the ability to interpret and analyze text and speech. This capability allows marketers to extract important insights (like consumer mood and personalities) from social media and other sources. It also enables the integration of automated communication software like chatbots.
- Neural networks. Neural networks are at the heart of advanced deep learning techniques and provide marketers with the ability to discover complex patterns in customer behavior. Neural networks can also be used for bid optimization, allowing marketers to find the perfect balance between cost and ROI.
- Computer vision. Computer vision enables image processing, analysis and pattern recognition to optimize and accelerate the creation of better creatives.
How AI can help marketers make better decisions
The challenges facing marketers today are significant. Customers’ expectations for personalized advertising are on the rise, yet they are highly concerned about their privacy. This is leading to increasingly restrictive regulations surrounding data collection as well as increased privacy-protecting behavior from consumers.
Luckily, AI-powered solutions can be used to provide consumers with highly personalized and engaging experiences while at the same time protecting their privacy. Here’s how.
Develop better creative at scale
While 65% of marketing leaders say that they’re confident in their creatives, an almost equal amount say that they struggle to scale their creative approach. This indicates that while many advertisers can develop high-performing creative, activation is a different story.
Using AI, you can deliver high-performing, personalized creative at scale using real-time consumer engagements and cookieless data signals. AI automates the time-consuming and labor-intensive elements of creative optimization. For example, IBM Watson Advertising Accelerator can learn which creatives are performing and develop dozens of permutations in real-time, serving the most relevant ads to your audiences.
Target the right audience without cookies
The management, analysis and utilization of data remain some of the major challenges facing marketers today. Up to 85% of marketers say they are drowning in data, but aren’t sure how to use it in a privacy-friendly manner to target their audiences.
AI solutions can help by using anonymized data to understand consumers in their individual contexts. For example, IBM Watson Advertising Weather Targeting analyzes location and weather data to anticipate consumer behavior and develop relevant messaging. This helps you discover meaningful insights from anonymous data to predict the best creative at household and user-specific levels while protecting your consumer’s personal information.
Personalize the marketing experience
Personalization is vital if marketers want to develop customer experiences that convert. It helps to provide consistency across marketing channels, increase brand loyalty and ultimately drive revenue. The challenge is that while hyper-specific segmentation can increase the effectiveness of your messaging, cost-effectiveness may be lost. This means it’s harder to deliver unique experiences at scale and still have good returns.
However, recent advancements in AI tools such as natural language processing and machine learning enable brands to have scalable personalized experiences at a user-specific level. For example, with IBM Watson Conversations you can have direct engagement and 1:1 interactions with large audiences, driving engagement and building stronger relationships with your customers.
Mitigate advertising bias
Advertising bias has the potential to impact consumers negatively and reduce the effectiveness of campaigns. For example, it can lead to an inaccurate representation of your target audience, damaging your relationship with the customer and potentially resulting in disadvantaged subgroups.
However, AI can be used to automate the discovery of bias in advertising that would otherwise go unnoticed by the human eye. By scanning for biases, AI advertising can discover hidden insights and use these to create optimal strategies that address each population fairly and effectively.
Take a predictive instead of a reactive advertising approach
Brands not only need to identify which channels, messaging and creative elements will be most effective. They also need to anticipate who to engage and when to engage them.
Predicting consumer actions to deliver personalized experiences means that you will always deliver the right message to the right customer at each touchpoint. This is why 73% of advertising leaders agree that brands need to take a predictive rather than reactive approach.
AI marketing case studies
Dozens of brands have applied IBM Watson AI technology to their advertising approach, including:
Audi leverages dynamic creative to drive awareness
In today’s competitive automotive advertising environment, every dollar counts. Ensuring that the right creative is reaching the right audience, at the right time is imperative to success.
Audi chose to partner with IBM Watson Advertising to drive awareness and interest in its electric vehicles. By using Watson Advertising Accelerator, they were able to increase conversion rates by 118% and improve cost-per-model landing page visits by 271% and cost per inventory search by 320% (compared to their respective benchmarks).
National beverage brand uses AI to increase engagement
A national beverage brand wanted to increase engagement over the holiday season and connected with IBM Watson to achieve this goal. Accelerator was able to increase the conversion rate from the start to the end of the campaign by 143% and deliver valuable creative insights such as which elements resonated with audiences.
L.L.Bean leverages AI marketing to increase conversions for new products
L.L.Bean wanted to drive awareness for its premium athleisure wear collection. By partnering with IBM Watson Advertising Accelerator, they were able to deliver the right creative ad units to the right audience. Through this campaign, they were able to increase orders by 48%, while reducing cost-per-site visit by 76% and cost per order by 68% (compared to their respective benchmarks).
The Weather Channel uses AI-powered conversations to answer questions about COVID
After discovering that its users struggled to find COVID-19 information they could trust, The Weather Channel utilized IBM Watson Advertising Conversations technology to create an AI-powered chatbot that would answer COVID-19-related questions. Once deployed, this chatbot answered more than 2.6 million questions in over 400,000 conversations, providing users with reliable and relevant information in real-time.
Downloads of Storm Radar increase with dynamic creative optimization
The Weather Channel reached out to IBM Watson to drive increased downloads of its Storm Radar app. With this goal in mind, IBM Watson Accelerator AI was used to predict the 1:1 creative elements that would drive more downloads. Over just 23 days, Accelerator generated more than 600 creative permutations resulting in triple the app installs and a 241% lift in ad performance.
Here at IBM Watson Advertising, we have developed a suite of powerful AI tools that address the array of challenges facing marketers today. These tools can help brands create unique experiences, improve targeting, better track results and optimize campaign performance.
The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions.