Click prediction algorithms are essential & used extensively for sponsored search and real-time ad bidding. The current advertising scenario often makes the predictions redundant, if they are not near real-time. Here, We'll explain the application of Spark’s machine learning pipeline for predicting Click Through Rates (CTRs). We also cover the many challenges you may face & how to overcome them.
Accurate prediction of CTR (Click-Through Rate) is crucial to determine add performance, bid amount and campaign success. Learn how Apache spark helps to predict the click rate.
Machine Learning applications using Apache Spark help in improving online campaigns by precisely predicting ad Click-Through Rate (CTR).
Machine Learning analyzes data and information of users' online behaviours and predicts new data for online advertising. For instance, Machine Learning could be used in:
- Market segmentation
- Personalized messaging
- Display advertisements and lookalike targeting
- Customer Lifetime Value (CLV)
- Data Management Platforms (DMP) to enhance user data for better decisions
To know more about the benefits and applications of Machine Learning in online marketing, click here: https://www.slideshare.net/Imaginea/apache-spark-machine-learning-for-ctr-prediction
