A DTC wellness brand's data was fragmented across Shopify, Klaviyo, GA4, Meta Ads, Google Ads, and Amazon Ads.
Marketing spend optimized on the wrong signal
8 companies have fixed this with AI, most of them in Retail & E-Commerce, Healthcare & Life Sciences and Consumer Goods & CPG. 4 of the 5 that disclosed a timeline were live inside two months; the longest took 2–4 months. 3 disclosed cost: from under $25K to Under $25K, most often Under $25K. Built mostly as data synthesis & reporting, decision support & scoring and conversational AI, on Google Ads / Meta Ads, Salesforce and HubSpot. 2 builders in the Pluris network have done it.
Solutions
8 on fileLacoste's data was fragmented across multiple online channels and platforms.
Engine's data was fragmented across multiple systems.
The brand relied on a generic recommendation engine that ignored customer intent, seasonality, and browsing behavior, producing low conversion and high cart abandonment.
A high-growth, multi-location medical aesthetics practice ran a surgical case-based model and a recurring medspa model at once across multiple locations.
A nine-figure, national direct-to-consumer outdoor brand ran a multi-region marketing program across paid search, paid social, Amazon, affiliate, and TV in the US, EU.
One of the fastest-growing weight-loss clinic franchises in the US was scaling paid media to acquire new patients but couldn't tell how much it was really spending per.
Advanced MedAesthetic Partners (AMP), a PE-backed platform that acquires equity in med spas, cosmetic surgery, and dermatology practices, relied heavily on digital.