Native advertising is a popular way for affiliates and brands to reach their target audiences in a more engaging and natural manner. It involves integrating ads seamlessly into websites or apps, so they don't disrupt the user experience. These ads are designed to capture attention and deliver strong results.
As we know, artificial intelligence (AI) has become a powerful tool for improving online advertising campaigns. It can provide ideas for new ads, generate ad images, predict their performance, and personalize the ad experience for users. All of this is transforming how affiliate marketers and advertisers approach their ad campaigns including native ads.
This article will explore the different ways AI is used in native advertising campaigns to increase engagement, conversions, and overall success. Whether you're new to native advertising or have experience, it's important to understand how AI fits into the equation. This knowledge will help you stay competitive in today's online marketing space.
Improving ad creatives with AI
AI has made a big impact on improving ads in native advertising. When it comes to creating ads, things like the headline, image, description, and call-to-action are really important in getting people to notice and click on them.
Now, AI-powered tools can analyze data from past ads to figure out what works and what doesn't. They can predict which parts of an ad will get the best response from a specific audience or situation. This means advertisers can rely on data instead of just guessing when they create ads, which helps them make ads that perform better.
Some native ad platforms, like Taboola, even have AI features that can dynamically create different versions of headlines and images for an ad. The system then figures out which versions are performing the best and shows those more often. This saves a lot of time compared to manually A/B testing different versions.
AI can also help in the early stages of creating ads. Tools like Copy.ai use AI to suggest ideas for headlines, product descriptions, and calls-to-action based on a few inputs. Human copywriters still need to polish the ideas, but AI can give them a good starting point and spark new ideas.
Predicting ad performance with AI
Another major benefit of AI in native advertising is the ability to predict key performance metrics like click-through rates, conversion rates and cost per acquisition. Accurate performance forecasting helps advertisers better allocate budgets, set realistic goals and proactively optimize campaigns.
AI systems use machine learning algorithms to analyze large volumes of historical campaign data and build predictive models. These models can uncover patterns and insights that may not be immediately apparent to human analysts. With this knowledge, advertisers can make data-driven choices regarding ad placements, target audiences, bidding strategies, and more, increasing the likelihood of achieving their desired results.
Native ad platforms often integrate AI-powered forecasting tools. For instance, Outbrain's Amplify AI leverages an advertiser's CRM data and employs lookalike modeling to estimate which users are most likely to convert. Advertisers can then use these AI-generated audience segments to enhance their targeting efforts.
In addition to platform-specific tools, there are third-party solutions available for predicting native ad performance. Companies like Adalytics and Pathmatics use AI to estimate ad spend and forecast metrics across various native ad platforms and publishers. Their systems analyze publicly available data as well as aggregated and anonymized data from their customer base, providing benchmarks for ad spend and performance based on industry, ad format, geo, and more.
These tools offer valuable insights for advertisers looking to make informed decisions about their native ad campaigns.
Personalizing ad experiences with AI
With the majority of consumers preferring and responding better to personalized ads, it's important for native advertisers to deliver relevant, valuable ad experiences that blend seamlessly with the surrounding content.
AI makes it possible to provide personalized ads on a large scale. Machine learning algorithms can examine various data points about a user, such as their demographics, interests, purchase history, content consumption habits, device type, location, and time of day. This information helps determine which native ad should be displayed to each individual user.
One AI-powered method for personalization is Dynamic Creative Optimization (DCO). With DCO, a native ad template is created with dynamic fields that can change based on who is viewing the ad. These dynamic elements can include the headline, image, price, and discount. AI models analyze each user's unique profile and select the best combination of assets from the advertiser's library to display in the ad. This approach allows advertisers to create highly relevant ads for individual users without manually creating numerous ad variations.
Once again, Taboola is the native ad network that has this feature fully functioning at the moment.
Predictive modeling is another way AI supports personalization in native advertising. By analyzing data about a user's attributes and past behavior, AI can predict which product or offer a user is most likely to be interested in at any given moment. As more behavioral data is collected, these models become increasingly accurate. Advertisers can leverage these predictions to optimize their campaign targeting and improve overall results.
Simplifying campaign management with AI
Running a successful native advertising campaign requires continuous analysis, testing, learning, and optimization. AI can automate many of the manual, time-consuming tasks involved in campaign management, freeing up human ad managers to focus on higher-level strategy.
Bidding optimization is one area where AI is transforming native ad campaign management. Many native ad platforms now offer AI-powered bid optimization tools. These systems use machine learning to continuously adjust bid prices based on real-time data signals to achieve the advertiser's target KPIs (e.g. CPC, CP, or ROAS). Rather than spending time on granular bid changes, advertisers can set their performance goals and let the AI handle the rest.
- Automating campaign testing and budget allocation:
AI is also being used to automate campaign testing and budget allocation. Taboola has an algorithm that automatically allocates more budget to top-performing ad creatives, publishers, and audiences while throttling down spend on underperforming items. Pacing, cross-channel execution, and even ad purchasing can be automated based on performance trends determined by AI. You can also use 3rd Party tools like TheOptimizer, and Optimizely for campaign management and setting automation rules.
Fraud detection
Fraud detection is another tedious but critical aspect of native ad management where AI is making a big impact. With the rise of bots and other forms of invalid traffic, ensuring ads are being delivered to real humans is an ongoing challenge. AI-powered fraud detection systems can identify patterns and anomalies that signal fraud more quickly and accurately than manual methods. This allows advertisers to minimize wasted ad spending and demand refunds from advertising networks and publishers when fraudulent activity is uncovered.
Insights from a native ads media buyer
To get deeper insights, we are going to get some insider information from Dynh Nguyen, an affiliate marketer and the co-founder of M3tric — a Facebook and Native Ad media buying agency.
How can AI help with coming up with ideas when creating native ad creatives?
AI tools like ChatGPT can assist media buyers in generating multiple angles and styles of ad copy when they are struggling to come up with ideas. This helps overcome writer's block and allows for creating a variety of soft and aggressive ad angles. Our media buying team uses ChatGPT for this exact purpose when they get stuck.
What are some of the key areas where AI can be applied in native advertising?
AI can be leveraged in native ads in several powerful ways:
- Predictive modeling to predict which types of ads will perform well based on past data
- Automated bidding to optimize spend
- Personalization of ads
- Content creation
- Fraud detection
Some uses can be on the affiliate marketer/ media buyer’s side, and others can be implemented by affiliate networks and advertising networks.
Our company uses tools like H2O.ai and DataRobot for predictive modeling, and solutions like Persado and Frase for AI-assisted content creation.
How can AI help make the testing and optimization process more efficient?
AI can be used to cluster and categorize publishers based on performance data for certain verticals and offer types. This allows entering auctions with a dedicated bid for your own AI-generated audience pockets, making testing much more targeted and efficient compared to a broad approach. Our company is building our own models and databases to enable this.
Additionally, we are working on automating the process of using heatmaps and AI to detect where users are dropping off on landing pages. The first step is removing the problematic elements, but the goal is to have an AI-powered landing page optimizer that can pull from a database of proven page sections.
What's important to keep in mind when using AI tools for native advertising?
The experts emphasized that while AI is extremely helpful, it's important to maintain a human element. AI should be treated as a colleague who assists and generates ideas, but a human touch is still needed for refinement, high-quality production, and applying common sense to how far AI outputs can be used.
For example, Outbrain's account managers are using ChatGPT to generate image prompts based on top-performing ads, then feeding those prompts into Midjourney to create similar visual variations - combining AI tools with human oversight.
What advice would you give to someone who wants to start using AI in their native ad campaigns?
Here is some actionable advice:
- Talk to native ad networks directly, as they can provide guidance and benchmarks. The affiliate highlighted that the representatives are very approachable at events like this one.
- Don't just use AI in a basic way — push it to analyze and provide in-depth insights. You can try asking AI probing questions about your offer and summarize the responses to generate better angles.
- Focus on mastering one AI tool at a time rather than getting overwhelmed.
- Have someone on your team dedicated to data safety and privacy as you input data into AI models. This will likely become an important role.
- Review the specific AI tools mentioned for different use cases and start testing them. You can use tools like Dynamic Yield and Optimizely for personalization.
- Go in with intent and take action on what you learn, don't just read this information passively. It’s important to implement these AI tools and insights in your campaigns and workflows and see the difference.
Conclusion
The role of AI in native ads is rapidly expanding. From creative optimization to performance prediction to automated campaign management, AI is powering significant advancements and efficiencies across the native ads ecosystem.
For marketers, AI presents an opportunity to gain a competitive edge by extracting more insights from data, delivering more engaging, personalized ad experiences, and continuously improving performance. As the technology behind AI continues to evolve, its potential to revolutionize native advertising will only grow.
However, while AI offers major benefits, it's important to remember that human expertise and creativity still play a vital role. AI systems need human oversight to ensure brand safety, spot potential issues with generated content or targeting parameters, and incorporate branding and messaging strategies that algorithms can't replicate. The most successful native ad programs will find the right balance between leveraging AI and maintaining a human touch.
If you haven't yet explored how AI can enhance your native advertising efforts, now is the time to start. Get familiar with the AI features offered by your native ad platform partners and consider how third-party AI tools could round out your strategy. With a smart approach to incorporating AI, your native ad campaigns can achieve new levels of efficiency and impact.
