Boosting ROI with Personalized Ads: A Deep-learning Based Approach

Boosting ROI with Personalized Ads: A Deep-learning Based Approach

The advertising industry is constantly evolving. Personalized ads have now emerged as a powerful tool to attract and engage consumers on a much more individualized level. 

Ad personalization involves tailoring ad content to match user preferences and behavior. It provides a targeted approach that results in higher click-through rates, conversion rates, and customer loyalty. 

However, creating personalized ad campaigns is not easy. It is fraught with several challenges, the biggest one being the need to gather and manage large amounts of customer data responsibly. Businesses must be ethical and transparent about their data collection practices and ensure customer privacy at all times. But this is not possible with conventional methods. 

Conventional methods struggle to cope with the scale and complexity of personalized ad campaigns. Therefore, businesses need innovative technologies such as deep learning to adequately capitalize on the vast amounts of data. By leveraging neural networks, businesses can obtain valuable insights from their data and generate highly targeted and individualized ad campaigns to secure a higher ROI. 

How Deep Learning Works for Personalized Ads?

In today’s age of excess information, personalized ad endorsements have become a vital tool for businesses. They help to engross customers and improve their experience with a business. 


Deep learning, a subset of machine learning, has come up as an invaluable technology to create and deliver accurate and effective personalized ads. The technology focuses on preparing artificial neural networks to execute complex tasks by learning from huge amounts of gathered data. 


The word ‚Äėdeep‚Äô refers to the presence of several layers of intersected nodes in the neural networks. Every layer learns to identify specific patterns in the input data, enabling the network to extract the most accurate representations.¬†


Deep Learning vs. Machine Learning for Advertisements

Ad personalization is also possible with machine learning. In a standard approach, marketers use machine learning to create banners from a combination of some simple segments. This includes products viewed by a customer, similar products in the category, and best-selling products in that category.


However, with deep learning, the personalization process is more sophisticated.  Deep learning algorithms analyze and assess how each product is attractive from the user’s point of view. This eliminates generic clusters and makes the results highly tailored.

Moving on, deep learning also facilitates real-time personalization display. 


Several mechanisms built on the older AI or ML technology usually create and refresh behavioral profiles at fixed time intervals. This means the products showcased in the advertisements are no longer required by the users. 


But with deep learning, a behavioral profile is built in real-time. The algorithm understands each user from the way they react to different ads. This way, the ads they see are not just based on what they did before but also on how they responded to previous ads. 


This level of personalization leads to better results such as increased click-through rates. According to RTB House data, deep learning ad recommendation engines helped them get 41% more users to click on the ad.

How to Use Deep Learning to Boost ROI with Personalized Ads?

Training deep learning models to generate personalized advertisements involves analyzing user behavior, user preferences, and historical data. Here’s how it works. 


  1. Data Collection: Deep learning techniques leverage massive datasets to create personalized ads. The data can be gathered from multiple sources such as mobile apps, websites, and social media platforms. The gathered data is then used to extract information like user demographics, purchase history, browsing behavior, and other relevant data points.

  2. Data Preprocessing: Before providing the collected data to the deep learning model, it needs to be cleaned, organized, and transformed into a consistent format. This step involves tasks such as data normalization, handling missing values, feature engineering, and more. 

  3. Constructing the Data Learning Model: Deep learning models for personalized ads are built using neural networks. The architecture of these models depends on the nature of the recommendation task. For instance, convolutional neural networks (CNNs) are built for image data whereas recurrent neural networks (RNNs) are built for sequential data like user interactions or browsing history.

  4. Training the Model: Next, the constructed architecture model is trained using preprocessed data. During the training process, the model learns the relationships in data and consequently adjusts its internal parameters to make accurate recommendations. 

  5. Generating Recommendations: Once the model is ready and fully trained, you can use it to generate personalized ad recommendations. It takes user data as input and predicts the chances of the user’s interest in different items. All predictions are then ranked from high to low to create a comprehensive list of recommended items for each user.

  6. Monitoring and Evaluation: Post-deployment, the personalized ad campaign’s performance is closely monitored and evaluated. Metrics such as conversion rates, click-through rates, and ROI help to identify the success of the campaign and identify areas for improvement. 

Case Study: Deep Learning-based Personalized Ads Generation

One of our clients wanted to achieve better ROI with personalized ad recommendations. They wanted to improve customer engagement by analyzing the existing ads and identifying new patterns of customer preferences. The client wanted to automate personalized content generation and increase the effectiveness of advertisements.

The solution: To fulfill the client’s requirements, the experts at Algoscale developed a solution that used deep learning techniques like Generative Adversarial Network (GAN). We used the Shapley Additive explanations (SHAP) model and the Random Forest Regressor to analyze each ad and forecast cost metrics for elements like images, videos, headlines, and body text.

The result: The developed solution enabled the client’s company to drive more conversions at a much lower cost. The personalized ads that were tailored to each audience segment resulted in a 3x boost in the Return of Ad Spend (ROAS). This demonstrated the potential of deep learning in the advertisement industry.

Final Words

When used correctly, deep learning in advertising can largely benefit everyone. Customers can get ads that are truly relevant to them while marketers can sell their products. The only thing to keep in mind when working with deep learning and artificial intelligence technologies is your output will only be as good as the quality of your data. 


When working with massive amounts of information, it is easy to let unreliable and low-quality data slip through the cracks. If you are going to implement deep learning techniques, you must seek out accurate training datasets to make the most out of your investment. 


Algoscale is a leading digital transformation company with years of experience in data science services. By combining our unique approach and state-of-the-art tech platforms, we give you the best-quality training data for deep learning models. To know more, book a demo with Algoscale today!

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