Challenge
Jordan Craig, a streetwear brand specializing in men’s jeans, has been actively investing in revenue growth and exploring AI technology for hyper-personalized marketing. Especially, with the goal of driving more e-commerce sales based on strong customer retention, Jordan has been using and testing various tools to enhance the hyper-personalization of its email marketing efforts.
Solution
Jordan Craig leveraged Retentics, a tool to enable hyper-personalization, in addition to the existing ‘Likely to Buy (Replenishment)' Flow built with Klaviyo.
Result
We’ve helped Jordan Craig, a New York City streetwear staple since 1989, move beyond generalized "Likely to Buy" flows. By transitioning from standard platform averages to our consumption-based predictive model, they’ve turned denim replenishment into a major profit center.
So, how do you actually measure the gap between a "generalized" reminder and a "precise" one? Here is exactly how we structured the test to prove bottom-line results across their reorder flow strategy.
How did we run our A/B test?
Jordan Craig executed a rigorous, long-term study to ensure the data was indisputable:
The Set up: A head-to-head A/B test comparing "Expected Date of Next Order" logic. The only variable was the predictive source: the existing platform's AI vs. Retentics AI.
The Scope: We isolated Jordan Craig's reorder flows. The goal was to see which method best identifies the specific "next order" window for individual customers, addressing the gaps left by previous logic.
Running the test: The test was executed for a full 12 months. This one-year window proved the impact of predictive precision across all seasons, ensuring the results were a reflection of true behavior rather than a temporary spike.
The 3 KPIs That Proved Predictive Precision
These metrics evaluate Retentics specifically against the existing legacy flow setup, not total site-wide sales.
KPI 1: Replenishment Revenue Multiple
What it is: The ratio of revenue generated by the Retentics-powered flows compared to the legacy flows over the same period.
The Jordan Craig Result: 4.3x Multiple. Retentics outperformed the previous setup by more than quadruple in total revenue generated specifically from reorder-triggered messaging.
KPI 2: Revenue Per Recipient (RPR) Lift
What it is: The average dollar value generated by each individual recipient entering the flow.
The Jordan Craig Result: 1.81x Higher RPR. Retentics generated 1.81x more revenue per recipient, proving the AI hits the right people at the right time rather than relying on high send volumes.
KPI 3: Initial Momentum (The "Gap" Metric)
What it is: The speed at which the AI logic begins to capture missed revenue opportunities.
The Jordan Craig Result: 5x Reorder Flow Revenue Surge. Within the first few days of implementation, Jordan Craig saw a fivefold increase in revenue across their reorder flows, proving how many sales were being left on the table by the gaps in their previous timing.
Why "Personal" Beats "Broad" Predictions
The test revealed a fundamental flaw in relying on broad averages to trigger reorder flows.
The "Broad" Approach: Many platforms calculate timing based on a database-wide average purchase cycle. This often ignores what products were ordered or the quantity, leading to generalized campaigns that miss the nuances of individual streetwear buying habits.
The Retentics Approach: Retentics identifies unique customer behaviors and their specific purchasing patterns. By accounting for these individual rhythms, we fill the blind spots where generic timing missed real buying intent—turning "likely to buy" segments into a high-conversion reality.
“ We’ve been investing in exploring AI tech for revenue growth, especially with customer retention. With Retentics, It was incredibly easy to set up triggers into flows, and results showed in just a few days. ”
— Javier G.⎜Retention Marketing Specialist
Final Advice
One year later, Jordan Craig’s experience proves that personalization accuracy directly drives reorder flow revenue. While broad models predict based on general patterns, data-driven precision reveals where the real profit is created. What matters is ensuring your reminder lands when every customer’s unique timing truly counts.
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