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
Considering the websites of each brand is unique, Algoscale built a universal ML model that
can be optimized to analyze multiple websites independent of each other. The framework
had two components:

  1. User-behavior segmentation based on website engagement
  2. Widget recommendation based on user-behavior

The framework had four distinct modules to address the client requirement:

    1. Data extraction and aggregation
    2. Behavioral segmentation using a K-means clustering algorithm
    3. Widget sequence prediction using Hidden Markovnikov Models (HMM) and Finite
      State Automata (FSA)
    4. 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 XX%
  • Increased CTR by XX%
  • Customized content recommendation based on website engagement data