Ripley Leads the Use of AI in Campaign Optimization — and Proves It with an A/B Experiment
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Ripley is one of the leading retail companies in Chile and Peru. Its three operating segments include the retail business, which focuses on the sale of apparel, accessories, and home products through department stores and online channels; the banking business, operated by Banco Ripley and primarily focused on consumer finance; and the real estate business, with Ripley participating in the ownership of shopping centers in Chile and Peru.
The Challenge
Ripley faced the challenge of scaling its digital campaigns in an increasingly complex environment: more channels, more campaigns, and increasingly volatile market signals. The team needed to grow sales without compromising efficiency, avoiding static budgeting decisions that failed to respond to daily changes in CPC, conversion rates, or demand. At the same time, there was a strong need to validate any new approach with rigorous evidence before committing to higher levels of investment.
The Solution
To address this challenge, Ripley implemented Mixilo, our Paid Media Optimizer, through a controlled A/B test. The experiment compared traditional budget optimization with an AI-driven approach. The solution enabled dynamic budget reallocation based on marginal ROAS, responding to market signals in real time. Thanks to a robust experimental design and close collaboration between teams, Ripley was able to measure incremental impact, validate the effectiveness of AI, and lay the groundwork to scale a more agile and efficient optimization model.
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Want to learn more about the implementation of Mixilo for retail? Read the full case study
