Identifying and Engaging Audience through Content Analytics


The client, a leading content marketer in Finance industry wanted insights for their clients (advertisers) to target users based on interest (visits) and engagement (clicks). To provide these insights we built a solution that would predict trending topics in the future and help client’s content writers publish relevant content

Our Approach
  • Scraping content from the web for over a million URLs from the client’s publishing platform
  • Classifying content into 45 topics (Stocks, Bonds and Cryptocurrency) using LDA (Latent Dirichlet Allocation) algorithm
  • Assigning topic to the article based on the weightage of keywords in the content
  • Predicting user interest by forecasting users, visits and clicks using advanced predictive models (S-ARIMA, LSTM RNN) by topic to help
  • Topic modelling strategy showed better accuracy than the client's existing topic classification model and also a 3rd party vendor Diffbot
  • Our predictive model results helped client’s content writers to publish relevant content on trending topics and take effective data informed decisions