Advanced Machine Learning Implementation to Enhance Audience

Unilever faced a significant challenge in reducing its reliance on third-party digital audience data, such as those provided by major platforms like Facebook and Google. The goal was to create its own robust set of audience data (first-party data) across 19 of its brands in Latin America to enable more precise and effective marketing strategies

Unilever, a global consumer goods giant, operates across multiple regions with a diverse portfolio of over 400 brands, including many household names in beauty, personal care, and food sectors. In Latin America, Unilever faced challenges related to the efficiency of its marketing strategies, primarily due to a heavy reliance on third-party digital audience data from platforms like Facebook and Google. This reliance often led to generalized and less effective marketing campaigns. Unilever LATAM's objective was to gain more control over its marketing processes by developing a robust system to capture and analyze first-party consumer data, thereby enabling more targeted and cost-effective marketing approaches.

PulseUp founder and a team of highly skilled Data scientists led this project in his previous company EPICA, in collaboration with the Unilever team. They provided a solution that involved capturing first-party data from consumers interacting with Unilever’s brands across LATAM. This initiative allowed Unilever to begin analyzing its own data to understand audience behaviors and preferences better.

The solution utilized advanced machine learning algorithms developed by EPICA to identify patterns and behaviors that were not previously evident. This data-driven approach enabled Unilever to personalize offers and content for each consumer, enhancing the relevance and effectiveness of their marketing efforts.

This use case highlights the transformative impact of leveraging in-house machine learning solutions to generate and analyze first-party data. Unilever LATAM’s ability to internalize audience data insights led to enhanced marketing strategies, significant cost savings, and deeper consumer connections, proving the value of investment in advanced data analytics technology.

The project led to the identification of more than 534 new consumer segments. Additionally, by shifting to a strategy based on first-party data, Unilever achieved a significant reduction in marketing costs, with some brands seeing reductions up to 30%. This not only optimized marketing spend but also increased the effectiveness of their campaigns by targeting more precisely defined audience segments.

Previously, Unilever lacked the technology to capture and analyze real-time data from consumers engaging with its brands. Marketing budgets were perceived as inefficient, heavily reliant on third-party audience data, which often did not provide the granularity needed for effective segmentation and targeting. By implementing PulseUp’s machine learning solution, Unilever could achieve a granular understanding of its consumers. This included detailed segmentation by categories such as demographic attributes, engagement levels, and specific consumer behaviors.