How a $1B+ Omni-Retailer Cut CAC and Boosted ROAS with Predictive C-API | Case Study

How a $1B+ Omni-Retailer Cut CAC and Boosted ROAS with Predictive C-API

A leading $1B+ omni-retailer partnered with Angler AI to tackle rising acquisition costs and limited campaign visibility. Using predictive modeling and real-time audience targeting via Angler’s Conversions API, the brand optimized Meta ad spend and unlocked efficient, profitable growth across digital and physical channels.

63%

Reduction in new customer acquisition cost (CAC)

77%

Increase in incremental return on ad spend (ROAS)

93%

Higher 7-day click-to-conversion rates

About the Brand

Being a $1B+ omni-retailer operating across digital and physical channels, the brand struggled with rising customer acquisition costs, limited visibility into incremental performance due to privacy restrictions, and campaign reporting that highlighted problems after the fact rather than guiding spend in real time. By partnering with Angler AI, they tapped into predictive modeling to target the Movable Middle—the segment most likely to convert—making Meta investments more efficient and unlocking profitable growth.

Goal

The client aimed to:

Solution

Angler AI deployed its predictive Conversions API (P-CAPI), which:

Outcome

Over an 8-week pilot (May 6 – July 8, 2025), across ASC+ and Audience campaigns, Angler delivered substantial improvements: