Benefits & Results

Marketing campaigns conversion increased by 30%.
Propensity analysis helped Marketing teams channelize their efforts towards users with a higher propensity to subscribe enhancing overall ROAS (Return on Acquisition spends) and significantly optimizing CAC (customer acquisition cost).

Background

La Voz del Interior is one of the main newspapers in Argentina. Founded in 1904 and based in Córdoba it is known for its comprehensive local and national news coverage. The paper is going through a digitalization process to continue positioning itself as a leading generator of journalistic content in the digital era focusing on the monetization of its digital platforms. Their objectives include developing a robust online presence improving user experience through innovative web and mobile platforms utilizing data analytics for personalized content and integrating multimedia elements for comprehensive and interactive news coverage.

Challenges

Understanding and engaging readers through personalized content and accurately predicting subscription propensity to optimize marketing campaigns
Balancing free and premium content to drive subscriptions while maximizing ad revenue.
Technological integration required transforming legacy systems and ensuring a seamless user experience across platforms.

Solution

The solution required development of a Machine Learning model that analyzes historical web browsing data of readers who transitioned from free users to digital subscribers. We employed advanced analytics tools to uncover significant behavioral patterns and preferences developing a data science model to predict which unsubscribed readers were likely to become subscribers.

Implementation

Historical Data Analysis: Collected historical web browsing data of readers who transitioned from free users to digital subscribers including content usage metrics such as page views number of sessions time spent on different sections and interaction with various types of content.
Data Integration: Integrated all collected data into a centralized data warehouse ensuring it was clean consistent and ready for analysis using ETL (Extract Transform Load) processes to consolidate data from multiple sources.
Behavioral Analysis: Conducted detailed analysis of the collected data using advanced analytics tools to identify significant behavior patterns and preferences among users who became subscribers.
Model Development: Developed a sophisticated data science model using machine learning algorithms to predict the propensity of unsubscribed readers to convert to subscribers based on content usage patterns and interactions across different sections.
Pattern Recognition: The model helped in recognizing patterns that indicated a higher likelihood of subscription such as users who frequently read premium articles or spent more time on the website.
Propensity Scoring: Assigned each user a propensity score based on their likelihood to subscribe which was used to segment the audience and prioritize marketing efforts.