Response SLA Calibration (Delivery)

Response SLA Calibration (Delivery)

1. 01

   Pull 30 days of raw first-response timestamps from the community platform before promising any SLA

   Export thread creation times and first staff reply times from the platform's analytics or API. Mighty Networks and Circle both expose this; if the client runs Discourse, query the admin dashboard instead of asking the community manager to estimate.

2. 02

   Segment threads by intent: member question, support ticket, engagement prompt, or noise

   A single blended average hides the real problem. Support tickets usually need a 4-hour clock while engagement prompts can wait 24 hours, and mixing them makes every SLA target look unachievable.

3. 03

   Set three tiers tied to client revenue exposure, not to internal comfort

   Tier 1 covers billing, access, and outage threads (2 to 4 hours). Tier 2 covers how-to and product questions (same business day). Tier 3 covers social and off-topic posts (48 hours). Write the tiers into the retainer scope so the boundary is contractual.

4. 04

   Assign a named owner per tier per shift, including a weekend fallback

   Unassigned queues decay within two weeks. Rotate ownership across the delivery pod and record the rotation in the client-facing channel so escalations have a destination.

5. 05

   Configure routing and auto-tagging so intent is captured at thread creation

   Respond.io and Wati both support keyword or AI-based routing into shared inboxes; Bettermode and Hivebrite rely on native tagging. Whatever the stack, the tag must exist before the first human sees the thread.

6. 06

   Run a two-week shadow period and compare actual against target before publishing numbers

   Report the gap internally first. Publishing an SLA the team misses in week one damages the retainer more than a conservative target ever would.

7. 07

   Publish the calibrated SLA in the client's monthly report with breach counts and causes

   Track breaches by cause (volume spike, staffing gap, platform outage) so the next quarter's staffing conversation is grounded in data rather than anecdote.