Client Overview
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.
Requirements
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.
Solution
As a data consulting company, Algoscale designed a universal ML model that can be optimized to analyze multiple websites independently of each other. The framework had two components:
- 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
Algoscale also built a pipeline built using Python and Jupyter Notebook for generating reports on individual brands. The pipeline connects the client’s database with the server that hosts Notebook and runs for a specific duration for a particular brand. Once the pipeline is executed completely, it presents a concise and insightful graphical report for that brand, which can be used to make informed data-driven decisions.
Highlights:
- 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
