Cost Reductions & Revenue Maximization with ML-Powered Bidding Engine
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
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.
Data Flow
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%.
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.
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.
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.
