HelpDeskCo: A 96% SLA That Nobody Believes
HelpDeskCo provides service request fulfillment for a retail client: 1,200 requests a month across 310 catalog items. Its dashboard shows 96% SLA attainment, average handling time of 7.8 minutes against an 8-minute target, and first-contact resolution of 71%. The client's user satisfaction score is 61%, the reopen rate is 18%, and 27% of requests are closed as 'fulfilled' within 10 minutes of submission, mostly by a Friday bulk-close script. Agents say they skip the knowledge base to protect AHT; 82% of volume comes from 12 catalog items. Three major incidents last quarter were traced to changes made while fulfilling requests. The contract renews in six months and the client wants 'world-class' reporting. The account manager is pressuring the service desk to keep the 96% figure in the renewal pack.
1. Diagnose what the current metrics are driving. Identify the specific gaming mechanisms and the operational risks they create.
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The dashboard is measuring activity at the wrong points. SLA attainment at 96% is probably calculated from submission to closure, so closing a ticket is the fastest way to hit the target. The Friday bulk-close script closes 27% of requests within 10 minutes without evidence of fulfillment; those tickets may be reopened later or never fulfilled at all. Reopens at 18% are a direct symptom. If reopens are excluded from the SLA calculation, the metric is even more distorted.
AHT of 7.8 minutes against an 8-minute target rewards speed per interaction, not resolution. Agents skip the knowledge base to protect the target, which raises rework and inconsistency. First-contact resolution of 71% is suspect if requests are closed before the user confirms the outcome; it measures closure, not resolution.
The operational risks are serious: three major incidents were traced to changes made during request fulfillment. That suggests fulfillment is bypassing change control and knowledge checks, likely because speed is rewarded. Users are dissatisfied (61% CSAT) despite excellent-looking SLA numbers, which means the service is failing the outcome while passing the metric.
The root cause is a measurement system that treats closure as fulfillment. It is not an agent motivation problem; it is a definition and governance problem.
2. Design a balanced measurement framework for service request management. Define the metrics, formulas, and how you would prevent the new measures from being gamed.
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Use a small balanced set across four perspectives, with explicit definitions.
Customer outcome: CSAT by request type; percentage of requesters confirming the outcome was achieved; reopen rate within 30 days (reopened requests ÷ closed requests); first-time-right (closed without reopen or rework ÷ closed).
Process performance: end-to-end cycle time by request type (median and 90th percentile); approval wait time; percentage auto-fulfilled or self-service completed; backlog age; percentage of requests fulfilled within the published target, with clock-stop rules stated. Keep AHT only as a diagnostic for specific transaction types, never as the primary target.
Compliance and risk: requests that resulted in an unauthorized change (target zero); access or licence exceptions per 100 requests; audit findings closed on time; percentage of fulfillment changes with required change records.
People and cost: cost per request by type; knowledge article usage and accuracy; agent workload and attrition; competency certification rates.
Prevent gaming by requiring evidence of fulfillment before closure: user confirmation, automated provisioning record, or fulfillment evidence. Reopens should count against the original ticket, not disappear. Publish definitions and audit a sample monthly. Avoid single-number targets; use a scorecard reviewed together. If a measure improves while CSAT and reopens worsen, investigate rather than celebrate.
3. How should the service desk handle the renewal pack and use continual improvement to fix the underlying problems?
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Do not present the 96% as a valid attainment figure without qualification. The account manager should receive a corrected view: the current SLA definition counts bulk-closed tickets, so it is not comparable to a fulfillment-based measure. Present the old and new definitions side by side, explain the change, and provide a remediation plan. Concealing the issue risks the renewal and the relationship.
Use a structured continual improvement approach. Vision: reliable request fulfillment measured by outcomes. Current state: validate the data - sample bulk-closed tickets, quantify unfulfilled requests, reopens, and incidents linked to fulfillment. Target state: define what 'fulfilled' means, set a realistic baseline, and select the top 12 request types for redesign. Plan: fix closure rules, remove the bulk-close script, introduce evidence requirements, restore change control for fulfillment changes, and update knowledge articles. Handle: pilot with one request type and one team. Check: compare CSAT, reopen, first-time-right, and cycle time before and after. Sustain: embed the definitions in governance and monthly service reviews.
Create an improvement register with owners and dates for the bulk-close practice, AHT target, knowledge usage, and change-control breaches. Report honestly to the client: a lower but trustworthy number with a credible improvement trajectory is stronger than a 96% figure that collapses under audit.