Machine Learning in AdTech: 6 Ways You Can Improve Digital Advertising

Many AdTech companies use artificial intelligence for ad monetization, but only a handful seem to leverage their data to the fullest. However, the recent advances in machine learning in digital advertising have changed how companies deliver ads, plan their campaigns, and tailor user experience.

We’ll describe existing machine learning technologies that enable publishers to effectively manage their demand partners, organize ad inventory, and target users. Meanwhile, advertisers will learn how algorithms can help produce dynamic ads, personalize campaigns, and prevent ad fraud. But first, let’s familiarize ourselves with the basic terms.

Artificial intelligence and machine learning in AdTech

Artificial intelligence (AI) in AdTech means technologies that collect, analyze, and apply data for marketing and ad monetization with the help of smart algorithms. Machine learning in digital advertising, in its turn, refers to the algorithms that let the AI learn how to use data more efficiently. 

The current technologies aren’t entirely autonomous, but they are advanced enough to improve your digital marketing and ad delivery efforts. Here are the benefits you can expect from implementing them into your platform.

Enhanced data processing and analytics

Companies deal with a vast amount of data, but most systems lack the means to use it. Take the information you regularly receive about users, like location, search queries, social media activity, and purchases. Then, combine it with trends, weather forecasts, or seasonal fluctuations. The result is more information than you can chew on, and that’s if you manage to capture it.

The 2020 Rethink Data report by Seagate Technology found that companies capture about 56% of data. Moreover, only 32% of them can successfully leverage it for their business goals.

AI-enabled systems can provide you with a means to capture, structurize, and analyze advertising data. Then machine learning algorithms can then use it to identify audiences, understand how it interacts with your ads, and predict what they want to click on.

Personalized user experience

Machine learning models can help you personalize your marketing. They use first-party data and third-party cookies from ad partners to extrapolate the audience into groups based on their previous behavior. It enables you to tailor content for different audiences, making them more likely to buy products.

Here’re a few examples. A system might put a person visiting a car dealership website into a group of people interested in cars, whereas purchasing a Chanel bag identifies the user as a woman’s product shopper. Algorithms can then spot that both audiences also like to buy household items. Thus, a person gets additionally placed into a group for household shopping.

Machine learning for targeted display advertising improves the chances of a customer to click on the ad and buy the product. McKinsey’s 2020 state of AI survey suggests that over 14% of marketing companies use machine learning to segment their customers, and 17% rely on it for customer analysis.

Productivity boost

AI-powered tools like robotic process automation can help AdTech companies augment and automate about 45% of repetitive tasks. On top of that, 84% of senior executives believe algorithm-based technology is necessary for their company to grow.

Platforms powered by machine learning algorithms can help you set up your programmatic auctions. They can automatically organize the ad inventory, manage demand partners, and serve the optimal ads to the audience. 

Improved ROAS

Machine learning models can optimize your ad performance to achieve a higher return on advertising spend (ROAS). This means serving users with relevant ads in the preferable format to engage or convert them. According to Facebook’s 2019 research, using technologies to combine ad formats and creatives results in a 34% increase in incremental ROAS and a 6% lower cost per purchase.

The AI tools can also help you analyze ad campaign outcomes based on historical data. AI-empowered platforms can build testing modules to predict the effectiveness of ad campaigns by looking for patterns in the volumes of gathered data. Additionally, some solutions can detect irregular activities and click spamming to detect ad fraud.

Don’t forget that machine learning systems evolve over time. So, you can expect your AI-based solution to improve its effectiveness as it learns to distinguish between essential and irrelevant data.

Now that we know what you can expect from algorithms, it’s time to see how AdTech companies apply them.

6 ways to use machine learning models for advertising

AdTech companies have been using machine learning algorithms for years. So, let’s explore popular applications you can try for your company right now.

Programmatic auctions optimization

Ad networks can hurt your programmatic auctions due to high latency, poor inventory, or incorrect setup. Likewise, some impressions aren’t worth what you’re willing to bid on them. Thankfully, machine learning tools can improve your auctions in both instances while saving your time.

Google Smart Bidding uses machine learning algorithms to optimize your auction based on your goals, which may prioritize costs-per-auction, conversions value, or ROAS. You can also set device-specific performance targets (desktop, mobile, and tablet). The technology will then adjust networks, bids, and impressions to reach these objectives. 

The META’s Campaign Budget Optimisation lets advertisers manage their ad campaign budgets. The system automatically distributes your central budget throughout the campaign, simplifying the setup and management. This also helps you shift your costs to the best-performing ad networks.

Context-relevant ads

Machine learning technologies can determine visual content that has the biggest influence on certain audience types. This helps match ad content with inventories and deliver winning creatives.

Let’s take Vodafone telecommunications company as an example. They couldn’t advertise their carrier support for the iPhone X due to Apple’s marketing guidelines. The company turned to GumGum’s machine learning tool, which identified iPhone X ads on websites and inserted Vodafone ads into them. This helped consumers understand that Vodafone was a carrier for an upcoming smartphone by association, increasing the company’s market share of voice by 67%.

Luna (formerly Bidalgo) used AI-based algorithms to determine winning ad creatives. The algorithm was trained with images and videos to identify overperforming assets and the reasons for their effectiveness. Technology like this can suggest colors, concepts, pictures, and dozens of other variables for your marketing campaigns.

Dynamic content

Algorithms can help you modify elements of your ads. For instance, Meta’s dynamic ads show different visual media and ad formats (carousel or image collection) depending on the user’s preferences. In addition, Google’s AdSense Auto ML system analyzes pages to pick ads with higher CPM that are also likely to improve the user experience.

Another example includes META’s text optimization system. The machine learning models determine the user’s devices to optimize text variations, fonts, and size. Plus, the system can automatically translate the ads into different languages to reach international customers.

Targeted campaigns

Companies use machine learning for targeted display advertising to personalize their campaigns around customer groups. To narrow your target audiences down, you can feed your AI systems with data from Google, LinkedIn, and META.

Abreva, a cold sore medicine company, taught its machine learning algorithms with Google’s user data. It led them to identify the interests of their target audiences (predominantly millennials and Gen Zs). The company then created over a hundred ads that varied based on the content the person was watching on YouTube. This contextualized approach helped them reach a 342% search interest lift across YouTube and Google.

Inventory optimization

Managing your advertising inventory is critical for a successful monetization strategy. A reliable solution like Google Ad Manager applies machine learning and forecasting to help you make the most of your ads and, consequently, maximize your sell-through rates.

The platform compares the forecasting line items with competing booked line items using up to two years of historical traffic to predict traffic volumes. The machine learning models also monitor seasonal trends and apply them to your forecasts to help you optimize your inventory.

Fraud detection

The costs of digital ad fraud are projected to reach over $100 billion by 2023 (up from $35 billion in 2018). Scammers can deceive you by generating fake activity with bot farms, SSAI attacks, SDK spoofing, and click spamming. However, machine learning algorithms let you process huge amounts of data to detect such nefarious and downright naughty endeavors.

A mobile monetization company, Cookies Digital, was losing a chunk of its profits due to fake traffic. Switching to the fraud detection software allowed the company to track activities across multiple ad campaigns. This helped the company prevent incent traffic, block redirects, and comply with brand protocols for cooking. As a result, Cookies Digital could achieve 1,200% of annual returns on investment (ROI).

Despite all these advantages, machine learning algorithms are not without their problems.

Challenges of implementing machine learning models

Any company that implements machine learning in digital advertising should be ready to address some common challenges and limitations:

• Black-box algorithms. Many machine learning models are black-box systems, which means you can’t interpret how the system arrived at certain conclusions. This also means you don’t know what features were used to make predictions, leaving the accuracy of results in question.
• System biases. The data sets can contain patterns of inequality based on the user’s gender, race, or socioeconomic status. There is always a risk your system captures these patterns, and your results will contain the same biases.
• Dependency on structured data. AI models need enough data to provide accurate results. You shouldn’t make any critical business decisions if you have less than a month worth of activities and conversion samples. Plus, you require solid processes for collecting, storing, and filtering your data.
• High initial costs. You need a substantial budget to implement advanced machine learning technologies into your ad platform. More importantly, you need a reliable provider and technical skills to implement it.

Luckily, Postindustria has the knowledge and technical stack to leverage new monetization opportunities. 

Tap into machine learning for digital advertising

AI tools enhanced by machine learning technologies make a huge difference to your ad monetization and marketing campaign. We saw how companies use them to manage ad networks, forecast trends, target users, and prevent fraud. So, what’s stopping you?

If that’s the experience — we’d be more than happy to share ours. Our vetted engineers and project managers can extend your in-house team to help you implement the latest AI-based tools. Better yet, we can enhance your platform with machine learning algorithms, robotic process automation, predictive analytics, and language processing tools.

Sounds interesting? Make sure to contact us if you want to learn more.