Challenge
Ceremonia struggled with a "one-size-fits-all" approach to email marketing that relied on broad historical averages or static 30-to-60-day timers. Because different products like scalp oil and shampoo are consumed at very different rates, these generalized reminders were often mistimed. This led to missed revenue opportunities and irrelevant customer experiences that failed to account for individual usage patterns.
Solution
The brand implemented Retentics AI to move away from guesswork and toward a data-backed consumption model. The AI analyzed customer data to identify seven unique micro-segments and predicted next order dates on a per-customer and per-product basis. This allowed Ceremonia to send automated replenishment reminders at the exact moment a customer’s specific bottle was running low, ensuring the messaging reached them at their peak moment of intent.
Result
The shift to predictive AI logic resulted in a 12.20x revenue multiple compared to the standard replenishment model over a three-month period. Beyond increasing order volume, Ceremonia also saw a 17.25% boost in Average Order Value (AOV). By accurately anticipating needs, the brand encouraged customers to not only restock their "hero" products but also add secondary treatments and accessories to their baskets.
In the world of clean hair care, “one size fits all” emails are a recipe for missed revenue. We worked with Ceremonia — a brand rooted in Latinx heritage — to move away from generalized replenishment guesses and toward a data-backed consumption model. By replacing standard “Expected Date of Next Order” (EDNO) timers with Retentics AI, Ceremonia didn’t just see a slight lift; they enabled a new system that generates over 12x more revenue than their previous replenishment-based flows.
The Effiency Framework
Most brands are forced to choose between two flawed options: manually setting static “averages” — like sending a reorder email every 30 or 60 days — or relying on Klaviyo’s standard Expected Date of Next Order (EDNO) trigger, which calculates timing based on broad historical data.
In the age of AI, “standard” methods simply don’t cut it anymore.
Because a scalp oil typically lasts months longer than a daily-use shampoo, Ceremonia recognized that a generalized approach was missing the mark. From mistimed reminders to irrelevant product recommendations, ignoring individual usage patterns leaves significant revenue on the table.
The Discovery: 7 Unique Micro-Segments
Within 48 hours of our free trial, the AI revealed that the “average” Ceremonia customer didn’t exist. Instead, it discovered they had 7 different micro-segments, each with their own:
Unique purchasing cycle
Product preferences
Predicted next order date
Specific AOV
Logic over Averages: Instead of Klaviyo’s standard EDNO model, which relies on broad historical averages, we implemented an AI model that predicts consumption on a per-customer, per-product basis across these 7 segments.
The Revenue Multiplier: We focused on the direct comparison between the “Default” approach and the “Predictive” approach. The goal was simple: hit the customer exactly when their bottle is running low.
The Ceremonia Result: 12.20x Revenue Multiple
The Impact: Over a three-month testing window, the Retentics Replenishment Flow generated 12 times more revenue than the standard model. This level of efficiency proves that timing and product recommendations are the two of the most valuable variables in replenishment-based flows.
Beyond Volume: Boosting the Average Order Value (AOV)
It wasn’t just about getting more orders; it was about getting better orders. By reaching customers at the peak of their intent — right when they actually needed the product — Ceremonia saw an increase in basket size.
AOV Growth: 17.25% Increase
The Result: The average order size saw a double-digit jump compared to the previous flow. When the timing is right, customers don’t just restock their hero product; they are statistically more likely to add a secondary treatment or accessory to their cart because the brand has correctly anticipated their needs through micro-segmentation.
Final Advice: Stop Guessing, Start Predicting
The Ceremonia data proves that the “average” customer doesn’t exist. If you treat a customer who uses a product daily the same as a customer who uses it weekly, you will either annoy them or miss the window of opportunity. By switching to a consumption-based AI model and moving away from legacy historical averages, Ceremonia effectively turned their replenishment flow into a predictable revenue engine that pays for itself many times over.
Case Studies
Copyright © 2026 Retentics. All Rights Reserved