From chatbots to self-driving cars, it seems like everyone is talking about artificial intelligence (AI). This technology is not only changing the way humans interact with the world but unlocking new efficiencies in ad tech. Following the last few years of data privacy enhancements and industry consolidation, could artificial intelligence in marketing be the answer to ad tech’s longevity? In short — yes.

Below, we explore three ways AI will the future will enhance the future of ad tech.

1. ID-less lookalike modeling 

Since the deprecation of the IDFA, app marketing has gotten more complex. Marketers no longer have easy access to user-level details and data to serve hyper-relevant advertising to a targeted audience. This, in turn, restricts marketers’ ability to create robust audience profiles to build lookalike audiences.

Lookalike audiences are used by marketers to reach potential customers who are similar (in demographic, mobile browsing behavior, and preferences) to their current customers. By identifying lookalike audiences as a method of utilizing AI in ad tech, marketers can increase the probability of generating high-quality leads by appealing to users that are similar to audiences who have already engaged with their product or service.

In the initial years following the release of iOS 14.5, mobile publishers have begun building innovative ways to target high-quality users while still abiding by privacy regulations and improving AI in ad tech. These artificial intelligence in marketing builds  include creating solutions like universal IDs and first-party publisher data. AI will take this work even further, by giving brands the ability to create lookalike models based on smaller sets of known, high-engaging users.

How this will work: 

• Brands collect privacy-compliant signals such as mobile website or app type, geography, device type, time of day, local weather, dominant political or other attributes of the region, keywords on the page, sentiment of the page and time on page.
• AI models learn from privacy-compliant non user-level signals.
• These AI in ad tech models extrapolate their learnings based on non-user level signals to find and bid on larger target segments programmatically.
• Example: If your brand’s ads perform best with urban-based users mid-week and mid-day who are browsing content related to fashion and health, AI will use this information to automatically adjust your programmatic buying filters to focus spend on those users. 

2. Granular supply path optimization

DSPs optimize their ad buying through a manual and algorithmic process that analyzes hundreds of quantitative and qualitative data points from SSPs and publishers. Generally, ad buying strategies are determined at the exchange level. In other words, ad buyers select a large and well-regarded exchange to buy the best ad for their purposes.

However, with AI in ad tech, ad buyers will have the ability to go beyond the exchange level to forge the most efficient supply path for their ads. Without AI, this would be too complex of a process to do as it involves reviewing a large volume of complex data signals.

In the future of artificial intelligence in marketing, marketers will be able to use a mix of manual and AI optimization to select exchanges and then rely on AI to optimize a more detailed supply path level to hit their goals for price, performance, fraud and more.

How this will work: 

• Without AI in ad tech enhancements, an ad buyer bids on mobile ad space and then selects the exchange with the most efficient CPM.
• With AI, an ad buyer will be able to know that the same mobile ad might have a lower CPM on exchange A but if they go with a slightly higher CPM on exchange B, the result will be a lower CPA overall due to improved performance.

3. Better dynamic creative optimization 

Dynamic creative optimization (DCO), or dynamically optimizing your ad creatives to fit the context and preferences of a specific user, can be a powerful tool for engaging and re-engaging customers and a large part of AI in ad tech. At YouAppi, we often work with m-commerce apps to incentivize shoppers to come back through retargeted ads that use DCO to show items a shopper left behind. This lowers shopping cart abandonment, and increases retention and LTV. Brands can also use DCO to show products to users personalized to fit their interests and browsing behavior. While DCO requires a detailed set of data points and functionality, dynamically optimized creatives can be customized and served as part of the real-time bidding process in a matter of seconds.

In a future powered by artificial intelligence in marketing, DCO as we know it will become even more robust. AI in ad tech will enhance the sophistication of this process to optimize along even more parameters and creative options to automatically generate an ad for any given impression. Furthermore, elements of DCO will be automatically included in any app marketing campaign. When setting up a campaign, advertisers will be able to upload creative components into a DSP’s platform like images, headlines, etc. and the DSP will algorithmically choose the right creative for each impression based on predicted performance.market

Furthermore, with recent strides in chatbots and generative AI for creative assets, the DSP will likely be able to generate much of the text and imagery rather than needing brands to manually upload it themselves and aid in the future of AI in ad tech.

How this will work: 

• A brand uploads a variety of images, color palettes, slogans and headlines to their campaign.
• The AI model predicts which creative combination will perform best based on the parameters available at each impression call.
• The AI model continually learns, adapts and improves the performance of a campaign.

Takeaways

AI is changing the way humans interact with the world but unlocking new efficiencies in ad tech.

• • ID-less lookalike modeling: AI will power brands’ ability to create lookalike models based on smaller sets of known, high-engaging users without the need for user-level IDs like the IDFA.
  • Granular supply path optimization: In the future, marketers will be able to use a mix of manual and AI optimization to select exchanges and create a detailed supply path to hit their goals for price, performance, fraud and more.
• Better dynamic creative optimization: In a future powered by AI in ad tech, DCO as we know it will become even more robust. AI will enhance the sophistication of this process to optimize along even more parameters and creative options to automatically generate an ad for any given impression.

Want to learn more about AI?

For more information about technology in the app marketing space like artificial intelligence in marketing and machine learning, read our blog here. To get in touch with our team of retargeting and growth experts to harness the power of machine learning and automation to drive ROI in your mobile advertising campaigns, schedule a meeting with us.