AI |MARKETING | ADVERTISING FIELD | FLYINGMUM

Impact of AI in Advertising Field

AI models learn ad relevance through a process that involves several key steps and components, leveraging data and sophisticated algorithms to optimize which ads are shown to users. Here’s a breakdown of how these models typically learn ad relevance:

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A. Data Collection and Labeling

1. User Interaction Data: AI models are trained on vast amounts of data collected from users’ interactions with ads. This includes clicks, conversions (e.g., purchases after clicking), time spent on the ad, and user behavior post-click.
2. Contextual Data: Information about the context in which the ad is shown, such as the user’s search query, the webpage’s content, or the user’s demographic data, is also collected.
3. Labeling: The data is often labeled to indicate whether the interaction was positive (e.g., click or conversion) or negative (e.g., no interaction or quick bounce). This helps the model learn what constitutes a relevant ad.

B. Feature Extraction

1. User Features: These include user-specific data like past search history, browsing behavior, location, device type, and more.

2. Ad Features: Attributes of the ad itself, such as the content, format (text, image, video), keywords used, and historical performance data.

3. Contextual Features: This includes the time of day, the type of webpage or app where the ad is displayed, and other situational factors.

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C. Model Training

1. Supervised Learning: AI models often use supervised learning, where the model is trained on labeled data (e.g., ads that were clicked vs. those that were ignored). The model learns patterns that correlate with higher relevance.
2. Feature Engineering: Engineers might create or select features that help the model distinguish between relevant and irrelevant ads more effectively. This could involve creating complex features from raw data, like the frequency of certain keywords in a user’s past interactions.

3. Algorithm Selection: Common algorithms used include logistic regression, decision trees, gradient boosting machines (GBMs), and deep learning models like neural networks. These algorithms are capable of handling the high dimensionality and complexity of the data.

D. Optimization and Feedback Loop

1.Click-Through Rate (CTR) Prediction: Models are often trained to predict the probability that a user will click on a given ad. This is one of the primary metrics used to determine ad relevance.

2.Conversion Rate Optimization: Beyond clicks, models also aim to predict conversions, which are more valuable. Conversion rate optimization focuses on showing ads that not only get clicks but also lead to actions like purchases.

3. Continuous Learning: These models often include mechanisms for continuous learning, where they update and refine their predictions as new data comes in. This can involve techniques like online learning, where the model is updated in real-time with each new interaction.

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E. Ad Auction and Ranking

1.Bid and Relevance Score: In systems like Google Ads, each ad is assigned a relevance score based on the model’s prediction. This score is combined with the advertiser’s bid to determine which ads are shown and in what order.

2.Ranking Algorithms: Ads are ranked based on a combination of their relevance score, the bid amount, and sometimes the expected impact on user experience (e.g., predicted bounce rate).

F. Experimentation and A/B Testing

1. A/B Testing: Platforms like Google and Facebook continuously run A/B tests to experiment with different ad placements and formats to see which versions perform better.

2. Multivariate Testing. This is a more complex version of A/B testing where multiple variables (e.g., ad copy, image, target audience) are tested simultaneously to understand their combined effect on relevance and performance.

G. Personalization

1.User Segmentation: AI models often segment users into different categories based on behavior, demographics, and preferences. This allows for personalized ad experiences, where different users might see different ads based on what the model predicts they will find most relevant.

2. Dynamic Creative Optimization (DCO):This technique dynamically changes parts of the ad (like images, text, or call-to-action) based on user data and predicted relevance, ensuring the ad is more likely to resonate with the viewer.

H. Ethical Considerations and Bias

1.Bias Mitigation:

As with any AI system, it’s crucial to ensure that the models do not reinforce negative biases (e.g., showing certain types of ads more frequently to specific demographic groups). Techniques like fairness-aware algorithms and regular audits are used to mitigate such risks.

What is Algorithm Fairness?

AI models learn ad relevance by continuously analyzing large datasets of user interactions, extracting features, training predictive models, and optimizing for key performance indicators like CTR and conversion rates. Through a feedback loop of real-world data, these models improve over time, ensuring that ads shown to users are increasingly relevant and effective.

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