Solved: What are practical applications of AI for data professionals?

Hi there,

Thanks for reaching out, happy to give you some initial thoughts and guidance on how AI and machine learning (ML) are used in paid advertising platforms, especially beyond just using chat interfaces for simple queries. As a data professional, you'll probably find this aspect pretty interesting as it's all about using data at scale to drive outcomes.

It's spot on that just asking a chatbot for code snippets or copy is really just scratching the surface. The real power of AI/ML in digital advertising is embedded *within* the platforms themselves – think Google Ads, Meta Ads (Facebook/Instagram), LinkedIn Ads, etc. These systems are constantly using sophisticated algorithms to automate, optimise, and improve campaign performance in ways that would be impossible for a human to do manually.

It's all about automating optimisation...

The core idea here is taking vast amounts of data – user behaviour, past conversions, auction dynamics, ad interactions, website visits, and tonnes more – and using ML models to make real-time decisions about your campaigns. Instead of you having to constantly monitor and adjust bids, targetings, and placements, the AI does it for you, seconds before an ad is shown to a potential customer. It's basically predicting the likelihood of someone converting based on everything it knows, and adjusting the bid or showing the ad accordingly.

Think about it from a data perspective: you're feeding the system your goals (like "get me leads" or "get me sales with a 500% ROAS") and it uses predictive models trained on colossal datasets to try and achieve that goal as efficiently as possible. It's not a simple rule-based system; it's dynamic and learns over time based on the actual results it gets.

How AI powers bidding strategies...

One of the most prominent areas where you see AI/ML at play is in bidding strategies. Gone are the days where you'd manually set a maximum cost-per-click (CPC) for every single keyword or audience. While manual bidding still exists, smart bidding strategies powered by AI are often recommended because they can react in real-time to the auction.

For example, strategies like 'Target ROAS' (Return on Ad Spend) or 'Target CPA' (Cost Per Acquisition) on Google Ads or Meta Ads are entirely AI-driven. You tell the system what you want (e.g., "I want £5 back for every £1 I spend" or "I don't want to pay more than £50 per lead"), and it uses historical data and real-time signals to predict the likelihood of a conversion for each individual auction. If it predicts a high likelihood of hitting your target ROAS or staying below your target CPA, it might bid higher to win that auction. If the likelihood is low, it might bid lower or not bid at all. This happens millions or even billions of times a day across accounts.

Other strategies like 'Maximise Conversions' or 'Maximise Conversion Value' work in a similar way – the AI is constantly optimising bids across different users, devices, locations, times of day, etc., to get you the most conversions or conversion value for your budget.

From a data POV, this is about building complex propensity models (predicting conversion likelihood) and executing on those predictions in a live environment. You're not just querying a database; you're working with a system that is constantly analysing and acting on streaming data.

It also helps with targeting and finding new audiences...

Beyond just bidding, AI/ML helps platforms understand and find the right people to show your ads to. Traditional targeting relies on you defining interests, demographics, keywords, etc. While that's still important, AI adds another layer.

Consider 'Lookalike Audiences' on Meta or 'Similar Audiences' on Google (though less prominent now). You give the platform a list of your existing customers or website visitors, and the AI analyses their characteristics and behaviours to find other users across the platform who are statistically similar and therefore likely to be interested in your product or service. This isn't just simple matching; it's using ML to identify complex patterns and affinities across millions of users.

More advanced campaign types, like Google's 'Performance Max' (PMax) or Meta's 'Advantage+ Shopping Campaigns', take this further. You provide the system with your assets (copy, images, videos, product feeds) and your goals, and the AI decides where to show your ads (across search, display, YouTube, Gmail, Discover for PMax; or Facebook, Instagram, Audience Network for Advantage+) and *who* to show them to, often finding conversion opportunities in places you might not have thought to target manually. It's constantly testing and learning which combinations of audience segments, placements, and creative are most likely to result in a conversion, and automatically shifting budget towards the winners.

This means the AI is effectively doing a huge chunk of teh audience research and testing for you in real-time, based on performance data. It requires a data-driven approach to monitor *what* audiences and placements the AI is finding success with, even if you're not manually building every segment yourself.

And even helps with creative and placement...

AI isn't just limited to numbers; it's increasingly involved in the creative side too. Features like Dynamic Creative on Meta or Responsive Search Ads and Responsive Display Ads on Google allow you to upload multiple headlines, descriptions, images, and videos. The AI then automatically combines these assets into different ad variations and tests them in real-time across different placements and audiences to see which combinations perform best. It's essentially running thousands of small A/B tests constantly and optimising towards the highest-performing variants.

Some platforms are even starting to use AI to generate creative variations or suggest improvements based on past performance data.

This is another area where the data comes in – understanding which creative elements are resonating with which audiences, interpreting the performance reports from these dynamic campaigns, and using those insights to create better initial assets or refine your strategy.

Here's a quick overview of some key AI/ML use cases in these platforms:

So, for a data professional, it's not about asking a chat interface "how do I target X?". It's about understanding that when you select a smart bidding strategy or use a campaign type like PMax, you are engaging with a complex, data-driven AI system. Your role shifts from manual execution (setting individual bids or audience segments) to strategic oversight and analysis. You need to: -> set the right goals for the AI (what do you want it to optimise for?), -> provide it with high-quality data (conversion tracking, audience lists), -> understand the data it gives back (how is it performing? where is it finding conversions?), -> identify signals or trends the AI might miss or might need help with (e.g., market shifts, new product launches), and -> provide strategic direction (should we shift budget? test a new offer?).

It’s about leveraging these powerful AI tools effectively, which absolutely requires strong data literacy. It's working *with* the AI, guiding it with your business knowledge and data insights, rather than just asking it questions. It frees up your time from tedious manual tasks to focus on higher-level strategy and interpreting the results teh AI is driving.

Managing these campaigns to get optimal results from the AI can be quite complex, especially when you're dealing with large budgets or challenging performance goals. It requires deep platform knowledge, a strong understanding of tracking and data inputs, and expertise in interpreting the performance signals the AI is giving you.

If you'd like to discuss your specific situation or goals in more detail, we'd be happy to book in a free consultation. We’ve run quite a few campaigns for B2B SaaS clients and others, leveraging these AI features to drive results like £0.96 cost per user or $7 per trial signup, and seeing ROAS figures like 618% or even 1000% in certain niches. It's all about setting up the data and the AI for success.

Regards,
Team @ Lukas Holschuh