ytpartners transformation story.
Cost-to-serve model and COGS reduction
We converted post-sale operations into decision-grade unit economics. Micro-task decomposition, time-per-task surveys, and tier allocation created a cost-to-serve model that drove workflow redesign and automation priorities to lower COGS without sacrificing outcomes.
Executive summary
The company had strong demand, but unit economics were constrained by post-sale labor. We decomposed the execution workflow into micro-tasks, captured time-per-task by role through interviews and a structured survey, and built an allocation model to quantify cost-to-serve by customer tier. This revealed which segments were subsidizing others and where automation would create the most leverage.
Moment that changed the strategy
Starting point and diagnosis
The constraint was not demand. It was labor attached to throughput.
- Post-sale execution included many small steps that compounded into high cost-to-serve
- Exceptions and rework consumed senior capacity and slowed throughput
- Lower-tier volume created a hidden operational tax
- Economics could not be managed until cost-to-serve was measurable
What we built
Activity-based costing
- Mapped post-sale execution end-to-end and decomposed work into micro-tasks
- Interviewed roles and validated task ownership and handoffs
- Time-per-task survey to quantify labor by role
- Tier-based allocation model translating time into cost-to-serve per campaign
Economics-driven workflow redesign
- Routing rules based on value and complexity rather than habit
- Automation-first for low-yield work with explicit exception queues
- Standard playbooks and QA checkpoints to reduce rework
- Instrumentation: cycle time, exceptions, rework, and unit cost
How the model was built
Cost-to-serve was made measurable by converting workflow complexity into task-level time and labor cost.
- Mapped the end-to-end post-sale workflow from intake to launch to reporting
- Decomposed work into micro-tasks (setup, coordination, trafficking, QA, exceptions)
- Captured time-per-task by role via interviews and a structured survey
- Allocated labor cost by role and volume by tier to compute cost-to-serve per campaign and per tier
- Identified the highest-cost task clusters and the exception drivers creating rework
| Layer | What it contains | Why it matters |
|---|---|---|
| Workflow map | End-to-end steps from intake to closeout | Prevents blind spots and “unowned” work |
| Micro-tasks | Atomic actions grouped into task clusters | Allows accurate time and cost attribution |
| Role cost rates | Loaded labor cost by role | Translates time into true cost |
| Tier volumes | Campaign count and mix by tier | Shows where capacity is actually consumed |
| Cost-to-serve output | Cost per campaign and cost per tier | Enables tier-based routing and service levels |
Cost-to-serve drivers and automation targets
The model made it clear where labor was being consumed and where automation would create the most leverage.
| Task cluster | Typical work | Primary cost driver | Automation opportunity |
|---|---|---|---|
| Intake and setup | Input validation, brief standardization, initial setup | Missing/inconsistent inputs | Validation, templating, rule-based routing |
| Trafficking and scheduling | Coordination, scheduling, launch readiness | Back-and-forth, reschedules | Automated prompts, lock logic, exception routing |
| QA and compliance | Checks, revisions, requirements | Exception and rework rate | Embedded QA and requirements checking |
| Exception handling | Escalations, manual fixes | Edge cases, unclear ownership | Detection + routing + escalation rules |
| Reporting and closeout | Data capture, reporting, post-mortem | Manual compilation | Automated report generation |
What changed
- Cost-to-serve became measurable and managed by tier
- Tier-based routing reduced senior time spent on low-yield execution
- Automation priorities shifted from opinion to ROI-backed targeting
- Exceptions and rework became explicit, instrumented drivers in weekly operations
- COGS reduction was achieved without sacrificing Tier 1 and Tier 2 outcomes
Assets delivered
- Cost-to-serve allocation model by customer tier and campaign type
- Workflow map and micro-task library tied to owners and SLAs
- Tier-based service levels aligned to economics and renewal priorities
- Automation backlog prioritized by measured cost-to-serve drivers
- Operating metrics and cadence: cycle time, exception rate, rework load, on-time launch
Outcomes
- Cost-to-serve became a managed lever rather than an invisible tax
- Tier-based routing reduced human time spent on low-yield work
- Senior capacity was protected and redeployed toward high-value accounts and renewals
- Automation targets were selected by measured impact, not intuition
- Exception and rework reduction became an explicit operating goal
Applied AI in execution systems
- Validation at intake to reduce missing inputs and downstream rework
- Tier-based routing to enforce service levels and protect senior capacity
- Embedded QA checks to catch requirement gaps before launch
- Exception detection and escalation rules to reduce manual firefighting
- Automated reporting outputs to remove recurring compilation work
Testimonial
“Once cost-to-serve was visible by tier, priorities became objective. We could finally decide what to automate, what to standardize, and where human checkpoints were worth it. It changed how we planned capacity and reduced the amount of avoidable rework.”
VP Client Services (anonymous)