Scott is Chief Revenue Officer at Adswerve. A long-time ad tech executive with previous experience at Microsoft, AOL, Google and more.
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The marketing universe is abuzz with talk about generative AI. How are other marketers using it? What are the best practices? What’s next on the horizon?
The use of AI in marketing isn’t new. Marketers have relied on AI for several years for keyword suggestions in Google Ads, assisting customers via chatbots, personalizing social media ads and more. But generative AI tools like ChatGPT, Google’s Bard and Adobe Firefly have suddenly enabled us to get up close and personal with AI. Seemingly overnight, we’re now able to imagine endless new marketing applications. However, platform engineers are always a step ahead of our imaginations, and they’re already enhancing their marketing tools with this technology. So there’s a lot for marketers to unpack.
I want to use this space to discuss some of those new use cases, particularly in areas where generative AI can work alongside the latest advancements in machine learning (ML) and automation within core marketing platforms. I’ll explore how each of these technologies works, how they intersect, where they’re headed and the opportunities they present for small and midsize agencies.
Artificial intelligence, machine learning and automation: What’s the difference?
It’s easy to conflate AI, ML and automation because marketing tools often incorporate more than one of these technologies. However, it’s important to understand the differences because each technology serves a unique purpose and requires a different approach to harness its power. Let’s look at the nuances between AI, ML and automation:
Artificial Intelligence
Artificial intelligence (AI) is the ability of machines to emulate human intelligence. AI tools use complex algorithms to analyze data and autonomously make decisions or perform tasks based on that data.
• Where you’ve seen it: AI-supported digital marketing efforts long before ChatGPT. Social listening tools, for example, monitor social media platforms for mentions of a brand and use AI to analyze whether the context surrounding a mention has a positive or negative sentiment. Another example: Ad delivery platforms, such as Google’s Display & Video 360, use AI to optimize ad placements across channels for targeted audiences.
Machine Learning
Machine learning (ML) often feeds into AI products, which is why there’s often confusion between the two terms. ML deals with the development of statistical models and algorithms that enable computers to analyze data and improve performance for specific tasks. Without machine learning, AI couldn’t adapt when presented with new information. In many ways, AI is often the product of all the hard work ML has done with data. Some marketing tools tap ML to quickly analyze data and deliver insights for human decision-makers—not machines—to act on.
• Where you’ve seen it: BigQuery, Google’s cloud-based data warehousing service, includes built-in ML capabilities with the ability to analyze vast amounts of consumer data and surface fresh insights. You’ve also likely encountered ML if you’ve used lead scoring and predictive analytics tools to predict customer behavior.
Automation
Automation refers to the use of machines to automate routine tasks that would otherwise be executed by humans. It involves configuring a set of rules and triggers to initiate actions. While automation is often complex, it lacks AI’s ability to learn from data to optimize performance.
• Where you’ve seen it: Automation is a key component of many digital marketing solutions, from email and SMS flows to report generation. Ad bidding capabilities within Google’s Search Ads 360 (SA360) and other ad placement platforms also use automation to improve performance based on specific factors, such as the likelihood of a click or conversion.
Advanced technology lowers the barrier to sophisticated ad buying and data analytics.
Much of the conversation around generative AI has focused on creative output. But generative AI also unlocks possibilities when integrated within data, analytics and ad-buying platforms.
For example, Google recently announced a generative AI integration within Google Ads that automatically creates new headlines and descriptions for search ads to enhance performance. The AI-created assets are based on unique inputs like the landing page and search query.
Elsewhere in the Google Marketing Platform, SA360 now includes powerful automation capabilities that make it easier than ever for small and midsize marketers to improve bid performance, streamline campaign management and simplify reporting without the need for a dedicated data scientist or engineer. While AI isn’t a factor here (yet), I can imagine a future where marketers will use a generative AI assistant to surface insights and manage campaigns.
Or take BigQuery. My team has worked with Alaska Airlines to use Google Cloud’s autoML (a tool for more easily creating ML models) to surface unique segments of high-value audiences within BigQuery. The airline then fed these previously untargeted audience parameters into SA360 and deployed an automated value-based bidding strategy, resulting in a 30% increase in return on ad spend.
Previously, small and midtier agencies—and their clients—were unable to take advantage of many of these advanced capabilities. It was simply too expensive to tap an engineer to execute the complex coding and setup required. But now with advancements in AI and ML, the barrier to entry is considerably lower.
What does the future hold, and how can your agency prepare?
It’s never been easier to take advantage of the latest marketing tools, within Google and beyond—and it’s time for your agency to start experimenting. Smaller agencies, in particular, will benefit from creating internal task forces responsible for keeping abreast of the latest developments and disseminating knowledge across teams. Engaging a platform expert can also be helpful in pointing your agency in the right direction and supporting the execution of foundational work.
New tools and features are always a little intimidating, especially those built on sophisticated algorithms. But as we’ve experienced with ChatGPT and Bard, new advancements only make these technologies more accessible. And failing to leverage them will put your agency and your clients at a disadvantage.
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