The setting

Meridian is a B2B software product for field-service scheduling, sold to mid-market companies on annual per-seat contracts. You are the customer success manager who owns a book of these accounts through onboarding, renewals, and expansion.

Every mission on this path happens at the same company, so context carries over the way it does in a real job: the data you cleaned in mission two is the data the finance lead questions in mission four.

The missions

1. health scorestarter

Score the health of Meridian's ten renewal accounts

Ten of your Meridian accounts renew this quarter and you cannot give all of them equal attention. Before you decide where to spend your time, you need an honest health score for each, because a couple of accounts look fine on contract value while quietly going unused.

You deliver: A health scorecard rating each of the ten accounts red, yellow, or green with drivers and a priority order.

Scored on: Consistent scoring, Identifies the at-risk accounts, Driver clarity, Prioritization.

Working from: health_framework.md, account_usage.csv.

2. onboarding planstarter

Build the 30-60-90 onboarding plan for Northwind Plumbing

Northwind Plumbing just signed 25 seats, and how their first 90 days go will set the tone for the renewal. They dispatch across three depots and their busy spring season is coming, so a slow start would land them in the rush half-configured.

You deliver: A 30-60-90 day onboarding plan with milestones, owners, adoption metrics, and account-specific risks.

Scored on: Phased milestones, Adoption metrics, Owners and risks, Tied to value.

Working from: northwind_kickoff.md, onboarding_playbook.md.

3. QBR prepcore

Prep the QBR narrative for Cascade Logistics

You have a quarterly business review with Cascade Logistics next week, and their champion Dana Kirsch is under pressure from finance to justify the Meridian spend. Overall usage looks healthy, but one region has quietly fallen off a cliff, and Dana will not want to be surprised by that in front of finance.

You deliver: A QBR prep doc with the usage and value story, the risks, and the asks and next steps.

Scored on: Value narrative, Surfaces the risk, Tailored to the stakeholder, Clear asks.

Working from: cascade_notes.md, cascade_usage.csv.

4. churn analysiscore

Diagnose why Summit Facilities is trending toward churn

Summit Facilities renews in 90 days and every signal is bad: usage is falling, the buyer has gone quiet, and NPS cratered. Leadership wants a straight churn diagnosis before you spend a save budget, so you can tell them whether this is recoverable and what actually caused it.

You deliver: A churn analysis with risk level, quantified decline, root causes, and a specific save plan.

Scored on: Risk quantified, Root cause, Evidence based, Actionable save plan.

Working from: summit_tickets.md, summit_timeline.md, summit_usage.csv.

5. expansion playstretch

Build the expansion play for Beacon Field Services

Beacon Field Services is one of your healthiest accounts and it is bursting at the seams: every seat is in use, a second team is waiting to get on, and they are doing daily manual work that a Meridian module would replace. This is the moment to build an expansion play, and Beacon renews in five months, a natural time...

You deliver: An expansion play with the opportunity, the evidence, the sized offer, the stakeholders, and the pitch.

Scored on: Opportunity sized, Evidence from usage, Offer and pricing, Stakeholders and pitch.

Working from: beacon_notes.md, beacon_usage.csv, pricing.md.

How the scoring works

Each deliverable is graded against the rubric written for that mission. Separately, every mission on every path is graded on how you used AI, against the same four criteria:

  • Understood the task. The learner framed the goal for the assistant clearly instead of pasting the brief and hoping.
  • Grounded in the material. The learner directed the assistant into the provided files and based the work on them, not on invented facts.
  • Verified the output. The learner checked claims, numbers, or coverage against the source material before submitting.
  • Iterated with judgment. The learner refined weak parts of the draft with specific follow-ups rather than accepting the first answer.

Both scores, with the work behind them, go on your proof profile. That is what makes a claim like "I can use AI for customer success" something an employer can check.