The client is a media-tech company that helps online publishers convert website visitors to magazine subscribers. The company uses artificial intelligence and machine learning to integrate magazine articles into their websites using customizable, personalized widgets and generate user-preferred content.
The client was looking for an intelligent solution to analyze thousands of online magazines and smartly segment the site visitors into the right categories based on their behavior. The audience segmentation will help them recommend widgets to the publishers to increase their click-through rate (CTR) and convert visitors to subscribers.
The media-tech firm also wanted a consolidated graphical report for an individual brand with details like nature, volume, and sources of web traffic, engagement trends, time on page, bounce rate, exit rate, top pages, CTR of widgets on the website, etc.
- User-behavior segmentation based on website engagement
- Widget recommendation based on user-behavior
The framework had four distinct modules to address the client’s requirement:
- Data extraction and aggregation
- Behavioral segmentation using a K-means clustering algorithm
- Widget sequence prediction using Hidden Markovnikov Models (HMM) and Finite
State Automata (FSA)
- Deployment on AWS SageMaker
- Built an intelligent, algorithm-friendly, scalable, and optimizable universal ML model
using distributed computing and parallel processing
- Reduced customer acquisition cost by 9%
- Increased CTR by 7%
- Customized content recommendation based on website engagement data