Driving Cost Reductions and Revenue Maximization with an ML-Powered Bidding Engine for US-based Retail Client

Success Metrics: Real Results Happen With GetOnData

45%

Increased Click-through Rates (CTR)

20%

Improved Revenue Generation

40%

Enhanced Customer Engagement

Objective

The objective was to address the challenges a US-based retail giant faced in accurately predicting user behavior amidst diverse product offerings and customer demographics. The aim was to develop an advanced machine learning-based click prediction algorithm customized for the retail industry. This algorithm aimed to optimize content delivery and engagement strategies for an e-commerce platform.

The Challenges

  • Data Complexity: The client grappled with managing a vast array of variables in their data, ranging from product descriptions to user demographics, necessitating sophisticated preprocessing and feature engineering techniques.
  • Real-time Processing: Dealing with streaming data from real-time bidding (RTB) platforms efficiently while maintaining low latency for model predictions proved quite daunting for the client.
  • Model Performance: Ensuring the accuracy and efficacy of the click prediction model in capturing user behavior patterns and accurately predicting click probabilities presented a formidable obstacle for the client to overcome.
  • Integration and Monitoring: The complex task of integrating the ML model into the existing infrastructure and continuously monitoring its performance in a production environment was a significant hurdle.
  • Data Complexity: The client grappled with managing a vast array of variables in their data, ranging from product descriptions to user demographics, necessitating sophisticated preprocessing and feature engineering techniques.
  • Real-time Processing: Dealing with streaming data from real-time bidding (RTB) platforms efficiently while maintaining low latency for model predictions proved quite daunting for the client.
  • Model Performance: Ensuring the accuracy and efficacy of the click prediction model in capturing user behavior patterns and accurately predicting click probabilities presented a formidable obstacle for the client to overcome.
  • Integration and Monitoring: The complex task of integrating the ML model into the existing infrastructure and continuously monitoring its performance in a production environment was a significant hurdle.

The Solutions

Data Preprocessing

Utilized advanced techniques to preprocess the retail dataset, including tokenization of text inputs, encoding features such as product categories and user demographics, and incorporating additional contextual information like user history and time of day.

Feature Engineering

Improved input data using various features such as content categories, user demographics, and historical click activity, enhancing prediction accuracy and context capture for the BERT model.

Model Training

Fine-tuned a pre-trained BERT model on GPU using CuDF for faster preprocessing, optimizing the model to predict the likelihood of user clicks within target categories.

Model Evaluation

Evaluated the performance of the fine-tuned BERT model using metrics such as accuracy, precision, recall, and F1-score on a validation dataset to ensure its effectiveness in predicting user behavior.

Deployment and Monitoring

Integrated the trained BERT model into the bidding ML engine, deploying it in the production environment and establishing continuous monitoring mechanisms to track its performance and optimize content distribution strategies.

The Business Impact

  • Increased Click-through Rates: The bidding ML engine increased CTR by 45% by accurately predicting user clicks, resulting in more effective content distribution and engagement tactics.
  • Revenue creation: Improved marketing efforts resulted in more excellent conversion rates and revenue for the retailer, accounting for a 20% increase in revenue creation.
  • Enhanced Customer Engagement: Personalized recommendations and customized content improved user engagement by 40%, leading to greater satisfaction and loyalty.
  • Cost Reduction: Targeting advertisements more effectively minimized wasteful spending on ineffective marketing efforts, optimizing the advertising budget.
  • Improved User Experience: Anticipating user preferences and needs enabled the retail business to provide a more customized and seamless experience, elevating customer satisfaction levels.
  • Increased Click-through Rates: The bidding ML engine increased CTR by 45% by accurately predicting user clicks, resulting in more effective content distribution and engagement tactics.
  • Revenue creation: Improved marketing efforts resulted in more excellent conversion rates and revenue for the retailer, accounting for a 20% increase in revenue creation.
  • Enhanced Customer Engagement: Personalized recommendations and customized content improved user engagement by 40%.
  • Cost Reduction: Targeting advertisements more effectively minimized wasteful spending on ineffective marketing efforts, optimizing the advertising budget.
  • Improved User Experience: Anticipating user preferences and needs enabled the retail business to provide a more customized and seamless experience, elevating customer satisfaction levels.

Data Flow

Client’s Quote

Our partnership with GetOnData and the implementation of their bidding ML engine have changed our marketing efforts. With increased CTR, revenue generation, and customer engagement, we've optimized our advertising budget, and enhanced the overall shopping experience for our customers.