TLTR: Darkroom rebuilt Public Goods’ retention engine - redesigning welcome, post-purchase and cross-sell journeys across email and SMS, refreshing creative and cadence, and executing through Klaviyo so AI recommendations translated directly into revenue. Monocle supplied customer-level AI that identified at-risk members, timed winback enrollments for maximum engagement, and selected the right message and cadence for each person. The collaboration produced clear commercial lift: +5% membership retention, 7× winback conversion, 10× ROI, +34.3% QoQ retention-attributed revenue and +20.7% campaign lift.

The brief: elevate membership with minimal friction

Public Goods arrived with three narrow, measurable goals: reduce churn among 100k+ auto-renewing members, reactivate pandemic-era dormant signups, and improve second-purchase retention and cross-sell. They wanted enterprise-grade personalization but without a heavy engineering program or long vendor onboarding. 

Two partners, one workflow

This engagement succeeded because each partner did one thing exceptionally well and the teams tightly synchronized.

Monocle: the decisioning layer
Monocle’s AI modeled behavioral and intent signals to more accurately identify churn risk, choose the optimal moment for winback enrollment, and select message and cadence at the individual level. That avoided “leaking” offers to members who weren’t at risk and enabled surgical reactivation efforts.

Darkroom: the lifecycle engine
Darkroom rebuilt welcome, post-purchase and cross-sell flows, refreshed creative and campaign cadence, mapped Monocle outputs into Klaviyo, and ran holdouts and cohort measurement to prove incrementality. Operationally, the teams used shared Figma files and direct channels for fast iteration so strategy and execution stayed aligned.

What Darkroom built: pragmatic, revenue-first engineering

Darkroom’s remit was simple: make Monocle’s decisioning repeatable, testable and measurable across email and SMS.

Retention ecosystem redesign

• Post-purchase journeys engineered for timing and creative relevance to nudge profitable second purchases.

• Cross-sell journeys that use benefit-led storytelling and membership value to preserve lifetime value.

Executional rigor

• Klaviyo mappings for enrollment timing, suppression windows and channel orchestration.

• Templates and creatives adapted for lifecycle use (not one-off campaigns).

• Holdouts and cohort measurement to prove attribution and incremental revenue.

Lean delivery
The program prioritized testable hypotheses and a light engineering footprint so Public Goods could see material lift without a heavy internal project. The work was scoped and tracked in Darkroom’s retention roadmap and execution slate.

How Monocle’s AI decisioning added precision

Monocle’s contribution was decisions at scale - delivered at the customer level:

Churn and intent signals
Monocle assessed behavioral signals that more accurately predict cancellation risk, which let the team avoid blanket offers and instead focus resources on true at-risk members.

Optimal enrollment timing for winbacks
Rather than enrolling everybody into a static flow the moment they became “at risk,” Monocle optimized when to reach each customer so winback messages landed when the recipient was most likely to engage.

Message and cadence selection
For cross-sell journeys Monocle chose not just timing but the right offer and frequency per customer, preserving LTV while increasing conversion.

That precision removed much of the waste common to retention programs and made each experiment more likely to show clean, attributable lift.

Outcomes: elevation, not vanity metrics

The combined program delivered material commercial results:

• +5% membership retention (Monocle + Public Goods)

• 7× winback email conversion lift

• 10× return on investment

• +34.3% QoQ growth in retention-attributed revenue (Darkroom execution)

• +20.7% YoY campaign revenue lift vs. BFCM 2024 (Darkroom campaign performance)

Those results came from three compound effects: targeted decisioning (avoid wasted offers), optimal enrollment timing (winbacks that land), and disciplined execution (creative, cadence and suppression rules that protected LTV).

Looking ahead

Public Goods is expanding personalization to first-time buyer segmentation and B2B wholesale - use cases where higher AOVs and distinct intents benefit from individualized journeys. The pattern is repeatable because it separates decisioning (how to decide) from execution (the system that converts decisions into revenue).