The 12.20x Revenue Multiple: How Ceremonia Scaled Hair Care Repeat Orders with AI Logic

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 Efficiency 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.