Ad Click Optimization using machine learning

The project focuses on the optimization of ad clicks through the use of an artificial intelligence (AI) model. The main objective is to analyze user click patterns on different ads to identify those types of ads that generate the most interaction. Based on this data, the AI model will personalize the selection of ads for each user, seeking to maximize the click-through rate and, therefore, optimize the company' s advertising costs with an expected improvement of up to 20%.




Innovation/Artificial Intelligence


Mar. 2022 - Dec. 2023


Specific Objectives

Increase ad click-through rate (CTR): Improve ad effectiveness through personalization based on user preferences.

Cost optimization: Reduce unnecessary spending on less effective ads, focusing the budget on those that generate more interactions.

Improve user experience: Provide users with more relevant and interesting ads, which can improve their perception and overall satisfaction with the platform.

Expected Impact

The project is expected not only to improve click-through rates by 20%, but also to optimize advertising costs by concentrating spending on ads that actually generate interactions. In addition, ad personalization will promote a better user experience, which can translate into increased customer loyalty and satisfaction.


Data Collection: Collect historical data on user interactions with ads, including clicks, time spent viewing, and relevant user demographics.

Ensure data quality and relevance for AI model training. for AI model training.

Data Analysis and Preprocessing: Analyze data to identify trends, patterns and correlations between ad attributes and click-through rates.

Preprocess the data to remove noise and outliers, and transform the data for use in machine learning models.

AI Model Development: Use machine learning techniques, such as logistic regression models, decision trees, or neural networks, to develop a predictive system that can estimate the probability of a click based on ad and user characteristics.

Adjust and validate the model to maximize accuracy and minimize overfitting.

Implementation and A/B Testing: Implement the model in a test environment to compare the effectiveness of ads selected by the AI model versus a random or traditional selection.

Perform A/B testing to measure the actual impact of the model on click-through rate and user experience.

Continuous OptimizationUse the results of the A/B tests to make iterative adjustments to the model.

Integrate continuous feedback to improve the model and adapt it to changing user preferences and behaviors.