There’s arguably no topic more buzzworthy today than artificial intelligence (AI) and machine learning (ML)—be that positive or, more commonly, negative buzz.
Sure, you may hear fearful chatter about the rise of AI and its impact on human innovation. But hear us out: it’s essential to recognize that these technologies are not here to replace human ingenuity, but rather to enhance it.
Just as AI and ML are finding their place in healthcare, education, and space exploration, they’ve also begun to revolutionize the world of marketing and advertising.
Let’s talk about why that’s not a scary thing.
AI and ML are ushering in a new era of search advertising. They’re delivering everything from hyper-personalized content for individual users to enhanced fraud detection for businesses, ultimately helping brands better understand what makes consumers tick while saving valuable time and money.
Let’s explore five ways these technologies are driving more meaningful search experiences and empowering brands to know their market.
1) Hyper-Personalization: A Better Understanding of User Behavior
At some point, we’ve all experienced the frustration of being fed generic ads that seemed miles away from our actual interests. But the days of one-size-fits-all ads are rapidly moving behind us thanks to hyper-personalization.
Powered by AI and ML, hyper-personalization allows search ads to be tailored to each user's unique interests, demographics, behavior patterns, and intent signals. Algorithms fully analyze historical and real-time search habits to build detailed customer profiles that enable hyper–personalization.
Advertisers can then precisely target ads to align with an individual user's needs and preferences. For example, searchers with past interest in hybrid vehicles may be served ads for the newest electric cars, while parents searching for strollers see ads for baby products.
The result? Consumers feel like they have a digital shopping assistant that understands their tastes and needs, leading to higher engagement, time efficiency, and conversion rates.
2) Look into the Future with Predictive Analysis
AI and ML don’t just react to user behavior; they can also anticipate it.
Much like how meteorologists use data and models to forecast the weather, predictive analysis, driven by ML algorithms, looks at historical user patterns to anticipate behavior. This allows advertisers to understand user intent even before the search occurs, helping brands deliver the perfect ad to the right user exactly when they’re ready to convert.
You may notice that hyper-personalization and predictive analysis seem to be closely related concepts—and you’d be right. However, each serves a distinct purpose:
Hyper-Personalization vs. Predictive Analysis: What’s the difference?
3) Chatbots: An Even More Personalized Experience
For any business, hiring a 24/7 team of experts to provide personalized recommendations and guidance for every single consumer would be impossible. Yet consumers expect personalized, conversational experiences. How do you fill the gap?
The answer is found on just about any website that’s embraced the AI/ML revolution: AI-powered chatbots.
When consumers search for products, these chatbots use natural language processing (NLP), a branch of artificial intelligence that enables chatbots to understand, interpret, and engage in human language.
Drawing insights from past purchases, browsing history, intent, and context, chatbots suggest items catered to a user’s needs. It’s the same inputs an old friend would fall back on to buy you a birthday gift.
For brands, the value of adding a chatbot to their website is immense. According to business leaders, chatbots increase sales by 67% on average.
Not only do they enhance consumer engagement and boost conversions, but they also relieve the burden of manually handling common consumer queries at scale.
Plus, every chatbot interaction allows brands to gain data-driven insights that enable them to continually refine and optimize the consumer journey.
4) Automated Bidding and Budget Management
One of the biggest headaches in managing search campaigns is constantly tweaking bids and budgets for keywords, ad groups, and campaigns. It’s meticulous, often monotonous, and eats up valuable time.
Advertisers have to manually ensure every keyword has just the right bid, and every ad group gets just the right budget. The work, by the way, is never done—as soon as you finish, it's time to do it all over again.
Luckily, there’s an AI and ML fix for manual bidding.
Machine learning algorithms can incorporate performance data, seasonality trends, and bidding strategy goals to automatically adjust bids to the optimal price point. AI will learn to dynamically shift budget between better-performing areas, while scaling back poor performers.
This hands-off approach to bid and budget management not only frees up advertisers’ time, but also improves performance.
5) Enhanced Fraud Detection
Search advertising fraud costs businesses billions yearly. In fact, AdAge reports that for every $3 spent on digital ads, $1 is lost to ad fraud. But AI and ML technologies are helping put an end to fraud-induced financial hemorrhages.
These technologies apply advanced pattern recognition to identify signals of fraudulent activity across ad clicks, conversions, and website traffic. By analyzing large datasets, AI can catch signs of bots, fake clicks, and other malicious behavior that evade human detection.
Without having to waste ad spend on invalid traffic, companies can free up their budget and optimize campaigns for real prospects.
AI and ML are at the forefront of a transformative shift in the world of search advertising. The synergy between technology and advertising marks a new era where precision and personalization have come together in unprecedented ways. It’s up to businesses to decide when, not if, they should take part.
At adMarketplace, we don’t shy away from new technology. We’ve devoted significant efforts to developing AI and ML solutions that not only benefit our organization, but also our clients.
One of our primary focuses is on prompt shaping, where machine learning offers prompts to searchers to better tailor the product results they're served so they see more relevant results aligned with to their purchase intent.
Additionally, we recently launched ELME, which stands for Event Likelihood and Metrics Estimator. This proprietary machine learning model is trained to predict the potential value of each potential consumer for our advertisers. ELME serves as our Pricing Team's crystal ball to help our clients maximize their ROI.
We'll be keeping a finger on the pulse of the fast-paced world of AI and ML to explore how these intricate, yet dynamic technologies can help us shape the future of intelligent search advertising. In the meantime, explore our solutions more in-depth here.