Toward More Effective Advertising: Better AI and ML Approaches in Customer Behavior Understanding

In an era where digital content is omnipresent, understanding customer behavior has never been more crucial for businesses.

As they strive to deliver engaging customer experiences, businesses constantly look for innovative ways to comprehend their audience’s preferences, behaviors, and responses to content. Consequently, this understanding has profound implications for advert targeting and monetization strategies.

Benuraj is the Head of the Applications and Algorithms Technology Unit at MulticoreWare. He is a Machine Learning and Computer Vision specialist with 7+ years of R&D experience in Video Analytics, Surveillance, Retail Analytics & ADAS (Advanced Driver Assistance Systems).

Shivakumar Narayanan heads the Media and Entertainment BU focusing on AI-Enabled Video Codecs, Media Solutions, and Services at MulticoreWare.

Leveraging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) can significantly improve businesses’ ability to parse customer behavior data and create more personalized and efficient advertising strategies. These technologies can assist in understanding and predicting user behavior, automatically identifying advert opportunities, and intelligently placing adverts in a manner most beneficial to both the advertiser and the viewer.

However, while the use of AI and ML in customer behavior analysis and advertising has made significant strides, there remain vast untapped potential and numerous forward-looking ideas to explore. In this discussion, we delve into these cutting-edge concepts, examining how they might be developed and their challenges.

From enhancing metadata handling to identifying the emotional context for ad placement, we’ll explore currently available solutions and discuss ideas that could revolutionize the future of advertising.

AI/ML solution ideas can be divided into currently available solutions and more forward-looking ideas.

Currently Available Solutions:

AI-Driven Metadata Handling and Generation: Machine learning models today can already process video and audio content to identify key moments and assign relevant metadata.

Sentiment Analysis and Emotion Detection: Sentiment analysis using natural language processing is a well-established field and is currently used by many businesses to understand customer opinions.

Predictive Analytics for Ad Opportunities: Machine learning techniques, especially in predictive analytics, are already widely used for optimizing ad placement and timing based on past user behavior.

Real-Time Personalization: Many online platforms already utilize AI to deliver real-time, personalized ad content based on user behavior.

Cross-Platform Behavior Analysis: AI is used today to process and analyze user behavior data across multiple platforms and devices to understand user habits and optimize ad strategies accordingly.

Known Challenges:

Data Privacy and Ethics: With AI and machine learning, especially in potentially sensitive areas like biometric data, ensuring data privacy and ethical usage of data becomes paramount. Techniques like differential privacy and federated learning can help maintain user privacy while benefiting from the data.

Explainability: AI and ML models often act as black boxes, making it difficult to understand why a particular ad placement or selection was made. This could lead to issues with user trust and acceptance. Explainable AI (XAI) is a growing field that aims to make AI decisions more understandable to humans.

Ad Fatigue: AI can help monitor and predict ad fatigue, i.e., users becoming desensitized to ads due to overexposure. Dynamic and responsive ad strategies can be devised to keep user engagement high without annoying.

Data Quality and Bias: AI/ML models are only as good as the data they’re trained on. Ensuring that the data is high quality and free from biases is crucial, which could lead to skewed results and ineffective ad strategies.

By weaving AI/ML into every facet of understanding customer behavior and ad placement, companies can truly optimize their strategies to maximize user engagement and ad effectiveness.

Forward-Looking Solutions:

Interactive AI Adverts:

• Solution Idea:
  • Employ Natural Language Processing (NLP) technologies like transformer models (e.g., GPT-3) to create conversational AI chatbots within ads.
  • Develop Augmented Reality (AR) and Virtual Reality (VR) technologies for immersive ad experiences using libraries like ARCore, ARKit, and Unity.
  • Use real-time analytics and ML algorithms to process user interactivity data, adapting ads based on user responses.
  • Experiment with Reinforcement Learning models to optimize the interaction strategies of the AI in the advertisements.

• Challenges Addressed:

Challenge	Approach
User Engagement	Interactive ads with AR/VR provide an immersive and engaging user experience.
Personalization	Real-time analytics and ML help tailor ads based on user responses, enhancing personalization.
Data Quality	Real-time user feedback provides high-quality, relevant data for refining ML models and improving ad effectiveness.

Biometric Data Analysis:

• Solution Idea:
  • Collaborate with wearable tech companies to access anonymized, consented biometric data.
  • Employ ML algorithms, such as deep learning models, to analyze biometric data (e.g., heart rate variability, skin conductivity) and correlate it with user engagement and ad reactions.
  • Use time-series analysis and signal processing techniques to preprocess the biometric data before feeding it into the ML models.

• Challenges Addressed:

Challenge	Approach
User Privacy & Consent	Strict privacy controls and the use of only anonymized and consented data ensure user privacy is respected.
Precision in User Understanding	Biometric data provides a more direct and precise measure of user engagement and emotional reactions.

Explainable AI (XAI) for Advertising:

• Solution Idea:
  • Develop models that predict ad relevance and explain the choice, using techniques like LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (SHapley Additive exPlanations).
  • Use data visualization tools (e.g., Tableau, PowerBI) and effective UI/UX design to make explanations accessible and understandable to the end-user.

• Challenges Addressed:

Challenge	Approach
Trust and Acceptance of AI	XAI models help improve user trust in AI-driven advertising systems by providing understandable explanations for ad selection.
Data Transparency	Explanations offer insights into what data influences the ads users see, enhancing transparency.

AI Monitoring and Predicting Ad Fatigue:

• Solution Idea:
  • Build deep learning models to track user engagement over time, identifying patterns that suggest decreasing interest or ad fatigue.
  • Leverage predictive analytics to forecast user behavior and anticipate points of ad fatigue before they occur.
  • Integrate these predictive insights into the ad-serving algorithms to dynamically adjust ad frequency and content.
  • Use A/B testing to validate the efficacy of different ad strategies and refine the predictive models.

• Challenges Addressed:

Challenge	Approach
AD Fatigue	Predictive analytics allow for dynamic ad frequency and content adjustment, mitigating ad fatigue.
User Experience	By preemptively identifying and avoiding points of ad fatigue, user experience is significantly improved.

Conclusion

While these technical solutions offer substantial potential for enhancing advertising strategies, their implementation should always consider ethical guidelines, user privacy, and the overall user experience. These factors will ultimately play a crucial role in determining the success of these advanced technologies.