AdTech Scenario Examples
- Inefficient ad targeting and personalization
Problem
Current ad targeting methods fail to reach the right audience with relevant ads, leading to wasted ad spend and low engagement rates.
Potential Solutions
- AI-driven algorithms can analyse vast datasets in real-time to predict user preferences and deliver more relevant ads. Machine learning models continuously improve targeting accuracy by learning from user behaviours and adjusting targeting parameters dynamically, enhancing personalization and engagement.
Example
AdRoll leverages machine learning to predict user preferences by analysing behavioural data, delivering personalized ads across platforms to improve engagement and ROI.
- Ad fraud
Problem
Fraudulent clicks, bot traffic, and fake impressions cost the industry billions each year, undermining ad campaign effectiveness.
Potential Solutions
- AI-powered fraud detection tools can recognize patterns associated with fraudulent clicks, bot activity, and fake impressions. These systems can analyse traffic in real-time, block suspicious activity, and adapt to evolving fraud tactics, helping to protect ad budgets and campaign integrity
Example
White Ops (now known as Human) uses AI-based fraud detection to analyse traffic in real-time, blocking bot-driven clicks and fake impressions to protect advertiser budgets from fraudulent activity.
- Low ad viewability and engagement:
Problem
Many ads go unnoticed or are ignored, diminishing campaign impact and weakening advertiser ROI.
Potential solutions
- AI-powered landing page personalisation tools can match the landing page content with the user’s ad interaction or profile data, ensuring that visitors are directed to relevant, customized pages that increase conversion chances.
Example
Unbounce offers AI-driven tools for landing page personalization, matching page content with user ad interactions, thus increasing conversion chances by creating a seamless user experience.
- Content mismatch in contextual advertising
Problem
Poor matching of ad content with webpage context creates negative user experiences and harms brand perception
Potential Solutions
NLP (natural language processing) and sentiment analysis can analyse webpage content in real-time to ensure that ad placements match the surrounding content context. This technology can help ensure that ads appear in relevant and brand-safe environments, improving user experience and brand perception.
Example
GumGum utilises natural language processing (NLP) and computer vision to analyse on-page content and place ads in relevant and brand-safe environments, ensuring a positive user experience.
- Creative fatigue and limited creative variations
Problem
Repetitive ads lead to audience fatigue, while marketers struggle to produce enough creative variety to keep users engaged.
Potential Solutions
AI tools for creative optimization can generate multiple ad variations and automatically test them across audiences, learning which visuals, text, and formats perform best. By rotating effective creatives, AI reduces ad fatigue and sustains audience interest.
Example
Phrasee applies AI to generate and test multiple ad variations, adjusting messaging, imagery, and tone to maintain audience interest and prevent ad fatigue over time.