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Case Study · Customer Success

When the Product Was the Problem

Designing a tiered engagement model for a 15-partner network with no margin for churn.

The Network

For two years I sat inside the backbone of a federally-funded community initiative — a network of fifteen community-based partner organizations bound together by a single shared reporting framework. The framework was the product I owned. Partners did not love it.

Most were small, scaling-phase nonprofits with limited data capacity. They weren't paying customers; they were sub-grantees, which gave the relationship compliance teeth. Reporting was the deliverable the funder paid for. A partner walking didn't open a renewal conversation — it eroded the federal grant funding the network. Churn was revenue loss, denominated in grant dollars instead of ARR.

The network had launched with twenty partners. By the time I arrived, five had dropped. Each lost partner meant lost services to the neighborhood and gaps in the data the funder was paying for.

This was one of roughly twenty-five concurrent projects I managed at any given time. The others ran on city departments, executives, boards, internal staff across seven departments, and our compliance overseer — all of whom also needed something from me. Partner retention had to happen inside that wider load.

15
Partner organizations across health, education, economic mobility, and housing.
5
Partners lost in the years before my tenure (of 20 at launch).
100+
Indicators tracked across the network.

The System

Tiered engagement by risk and capacity

I designed a tiered engagement model that assigned each partner a touch level based on two variables: churn risk and data capacity. The question was simple: How much support does this partner need to stay in the network and continue reporting?

Most customer success orgs segment by revenue first and layer touch model on top. That logic didn't apply here — the partners weren't revenue-differentiated. So the segmentation went straight to the variables that actually predicted retention.

Who lived here

Partners with leadership turnover, no dedicated data staff, or a track record of missed reporting cycles. The orgs most likely to drop.

Touch model

Weekly or biweekly 1:1s. Listening sessions before any new ask. Direct hands-on support during reporting deadlines.

What they got

Annotated templates, walked-through documents, ad-hoc trainings calibrated to wherever the partner was stuck that week.

Why it worked

The partners most likely to leave got the most of my time. The relationship was the retention mechanism — not the framework, not the dashboard.

Who lived here

Partners who knew the framework and occasionally needed translation. Capacity was growing but uneven; most of the network lived here.

Touch model

Monthly check-ins, structured email campaigns, regular office hours attendance, and ad-hoc 1:1 when something surfaced.

What they got

Reporting checklists, deadline calendars, network-wide resources, and shared dashboards. Light personalization on top.

Why it worked

The system did most of the work. I checked in to surface friction before it became risk — and pulled partners up to Tier 1 when something changed.

Who lived here

Partners with mature internal data infrastructure, or orgs that had been in the network long enough to operate independently.

Touch model

Plenary meetings, optional office hours, asynchronous communication. Light contact by design.

What they got

Standard network resources. They pulled what they needed; the system stayed out of their way unless they pulled it in.

Why it worked

Protecting their time protected mine. Treating sophisticated partners as sophisticated kept the relationship clean.

Movement between tiers

Partners moved up when something changed — leadership turnover, capacity loss, a missed reporting cycle. They moved down when they stabilized or matured. The tier was a current read, not a label.

The model was built to scale the relationship work across a load I couldn't otherwise hold. Office hours and plenaries did one-to-many work. Email campaigns and self-serve materials did tech-touch work. That freed up one-to-one time for the partners where one-to-one was the only thing that would keep them in.

What Held

100%
Partner retention across both reporting cycles of my tenure. Zero partners dropped. The network had lost a quarter of its partners before I arrived; it lost none during my tenure.

The infrastructure underneath the retention number — shared metric definitions, partner-facing dashboards, co-built governance documents — mattered. But the cleanest signal is the simplest one. None of the fifteen walked.

Where It Maps

The work was customer success in everything but name.

The vocabulary was different — partners instead of accounts, sub-grantees instead of customers, compliance instead of contracts. The architecture is the same.

What it was in the role What it maps to
Tiered engagement by risk and capacity
Touch-model segmentation
1:1 listening and discovery sessions
Executive engagement & QBR motions
Office hours and plenaries
One-to-many CS motions
Email campaigns and self-serve materials
Scaled CS and at-scale enablement
Movement between tiers as risk shifted
Health-score-driven prioritization
100% partner retention across two cycles
Logo retention & net retention

The book was fifteen accounts. The complexity per account was enterprise-equivalent — each partner had its own executive director, board, program staff, compliance obligations, and political stakes, and partner churn meant grant erosion rather than a missed renewal: the nonprofit equivalent of lost ARR. The system I designed for fifteen was built to scale; the principles hold at seventy or one hundred fifty. What changes is the ratio of one-to-one to one-to-many — and the architecture already optimizes for that.