Diagnose a sudden drop in repeat usage for loyalty rewards engine

Problem Statement Description

A loyalty rewards engine is used by billing admins to manage and revisit reward-related workflows such as configuring reward rules, reviewing earned rewards, applying credits or discounts, reconciling billing adjustments, and monitoring program spend. Recently, repeat usage by billing admins has dropped suddenly, meaning fewer admins are coming back to use the engine after prior usage.

Your task is to diagnose what may have caused the drop before proposing any fixes. Treat this as a real product incident where the decline could be caused by product changes, billing workflow disruption, seasonality, permissions, data quality, customer mix, reporting issues, or measurement errors. The focus is not to jump to solutions, but to structure the investigation and identify the most likely root cause with evidence.

Assume this product has multiple admin roles, account tiers, reward program types, integrations with billing systems, and operational dependencies such as invoices, credits, approval flows, and audit logs. Billing admins are time-sensitive users who rely on accuracy, trust, and predictable workflows, so any issue affecting visibility, correctness, or completion confidence could influence whether they return.

The diagnosis should consider:

- How to define the repeat usage drop, including time window, baseline, denominator, and whether the metric reflects active admins, accounts, sessions, or completed reward workflows

- Whether the anomaly is real or caused by instrumentation, event tracking, identity mapping, role changes, bot filtering, logging delays, or dashboard changes

- Segmentation by account size, region, billing cycle, admin role, reward program type, integration type, platform, and new versus existing customers

- Funnel and workflow analysis across key admin actions such as login, program selection, reward review, approval, credit application, export, and reconciliation

- Product, policy, pricing, permission, API, or billing-system changes that coincided with the drop

- External or cyclical factors such as billing periods, fiscal close, promotional campaigns ending, customer churn, or support escalations

- Qualitative evidence from support tickets, customer success notes, admin feedback, audit logs, and failed task patterns

- Immediate containment needs, longer-term prevention, and how to communicate findings without overclaiming before evidence is validated

The goal is to present a clear root-cause investigation plan that narrows the problem from a broad usage decline into testable hypotheses, validates or eliminates likely causes, and identifies what evidence would be needed before recommending product, operational, or measurement fixes.