Diagnose a sudden drop in collaboration speed for expense approval tool

Problem Statement Description

You are investigating a sudden decline in collaboration speed for customer success teams using an internal expense approval tool. These teams rely on the tool to submit, review, discuss, approve, and resolve expenses related to customer meetings, travel, renewals, escalations, and account activities. The issue is time-sensitive because slower approvals can delay customer-facing work, create reimbursement frustration, and increase operational overhead.

Your task is to diagnose what changed before recommending any fixes. Focus on framing the anomaly clearly, understanding where in the expense workflow collaboration is slowing down, and separating true user behavior changes from measurement, instrumentation, or reporting issues. Consider that “collaboration speed” may involve multiple actions, such as comment response time, approval handoffs, manager review latency, policy exception resolution, or time from submission to final approval.

The investigation should account for team structure, approval chains, expense categories, geographies, policy rules, integrations, and recent product or operational changes. You should identify the most likely causes through a structured RCA approach, not jump directly to product changes.

The experience should consider:

- How “collaboration speed” is defined, measured, and whether the metric denominator or event tracking changed

- Which segments are affected, such as customer success roles, regions, team sizes, expense types, approval levels, or account tiers

- Where the workflow slowed down: submission, reviewer assignment, comments, manager approval, finance review, exception handling, or reimbursement handoff

- Recent changes in product UI, notification systems, permissions, policy rules, approval routing, integrations, or backend performance

- Instrumentation checks for missing events, delayed event ingestion, duplicate records, timezone issues, or dashboard logic changes

- Behavioral hypotheses such as approver workload, unclear policy requirements, notification fatigue, vacation cycles, or new compliance steps

- Evidence needed to validate or reject hypotheses, including funnel cuts, latency distributions, logs, user sessions, support tickets, and qualitative feedback

- Immediate mitigation, stakeholder communication, and prevention mechanisms once the root cause is confirmed

The goal is to demonstrate a rigorous RCA approach that narrows the problem from a broad metric drop to a specific cause or set of causes, while protecting customer success productivity, employee trust, and approval process reliability.