January is a digital-first collections company operating in a regulated, trust-sensitive space. The business works with large fintech partners and debt buyers to help consumers resolve past-due balances through a combination of outreach communications and a self-serve payment platform.
When I joined as Product Design Manager, the company was approximately 20–25 people and entering a rapid growth phase. Revenue performance was closely tied to recovery rate, which in practice led the organization to optimize heavily for early-stage conversion and entry-point metrics.
If this approach worked, it produced small, measurable gains quarter over quarter. If it failed, the risk was less visible: payment plans collapsing months later, realized revenue falling short of expectations, and borrower trust eroding without a clear owner or feedback loop.
January optimized heavily for recovery rate and early-stage conversion, which led the organization to over-invest in acquisition and entry-point optimizations.
While these changes produced small, statistically significant gains, they masked a larger failure mode later in the borrower lifecycle: a high rate of payment plan collapse that materially reduced realized revenue. A majority of borrowers enrolled in payment plans, and a substantial portion of those plans failed after enrollment, often months later, requiring cancellation and restart.
Because retention and completion outcomes were delayed, harder to observe, and slower to reach statistical significance, they were implicitly deprioritized in planning and decision-making. Product investment favored optimizations that were easy to measure quarter-to-quarter rather than interventions that addressed the part of the system that ultimately determined outcomes.
To address the lifecycle imbalance created by a recovery-rate–first optimization model, I pushed for treating borrower communications as a first-class product surface rather than a supporting funnel step.
This required a structural change. I advocated for forming a dedicated product team focused on outreach communications, including new PM and engineering capacity, with a mandate to improve trust, clarity, and follow-through across the borrower lifecycle—not just increase early conversion.
Rather than continuing to pursue marginal improvements at the point of conversion, I argued for reallocating effort toward reducing downstream payment plan failure—a failure mode that surfaced months after enrollment but materially affected realized revenue.
This reframing pushed against the organization’s default planning and measurement cadence, which favored fast, statistically significant wins over delayed but higher-leverage outcomes.
To make this shift viable at scale, I supported investments that reduced the cost and time required to experiment with and deploy borrower communications.
This included redesigning and launching more than 100 new communications, introducing AI-assisted tooling to accelerate production and iteration, and supporting ML-driven targeting to determine which borrowers to contact and when.
The goal was not bespoke artifacts, but durable leverage: enabling the organization to operate at higher velocity without proportional increases in headcount.
These decisions produced leverage beyond individual features or experiments.
A dedicated communications product team enabled sustained focus on trust, clarity, and follow-through across the borrower lifecycle, reframing payment plan failure as a product concern rather than an operational afterthought.
Over time, the team significantly expanded January’s ability to engage borrowers at moments that mattered most for plan completion. AI-assisted tooling reduced design and development overhead, while ML-driven targeting improved relevance and timing of outreach.
While these changes did not immediately alter headline metrics or the core product surface, they materially increased the organization’s capacity to influence realized revenue over time by addressing lifecycle failures that had previously gone unaddressed.
This case demonstrates my ability to diagnose misaligned incentives in complex, regulated systems and intervene at the point of greatest leverage—even when that point was not where the organization was accustomed to investing.
It reflects how I operate at Staff scope: focusing on system behavior rather than surface-level optimization, making explicit tradeoffs under uncertainty, and creating durable leverage through structure, tooling, and cross-functional alignment rather than individual output.
The work also reflects my comfort operating as the sole designer at high altitude, influencing direction across product, engineering, and data without relying on formal authority or a large team.
