REF / AUTOMATION

Austin B2B SaaS - Customer Onboarding and Support Triage Automation

Built customer onboarding flows, support ticket triage, and renewal forecasting for an Austin B2B SaaS - onboarding NPS jumped 31 points.

RoleAutomation Lead
Year2025
OutcomeOnboarding NPS +31, churn −44%
DomainAutomation
00
STACK

Tech used.

Claude Sonnet 4.6LinearSlackHubSpotStripeSegmentn8n

The Problem

A 32-person B2B SaaS company had recently raised a Series A and was acquiring customers faster than its 4-person customer success team could onboard them. New customers were waiting 2-3 weeks for a kickoff call. Support tickets were piling up in Linear with no prioritisation; complex customer questions sat for days while simple ones got answered fast (loud-customer bias). Renewal conversations were happening 30 days before contract end. too late to influence outcomes.

The CFO had modelled out hiring three more CSMs. The CEO asked: can we do this differently?

What I Built

A four-part stack:

1. Self-serve onboarding sequence. New customers receive an interactive onboarding flow tailored to their declared use case (extracted from sign-up data via Claude). Key steps are tracked; if a customer stalls, a CSM is alerted with the exact step + suggested talking points.

2. Support ticket triage. Every inbound ticket is classified by Claude:

  • Severity (blocker / standard / nice-to-have)
  • Category (auth, integration, billing, feature request, bug)
  • Customer tier (enterprise / SMB)
  • Suggested first response (where the model has high confidence)

Tickets are routed to the right engineer/CSM with context attached. Triage time per ticket: 4 minutes → 20 seconds.

3. Customer health scoring. A nightly job analyses product usage, support volume, payment history, and contract terms; produces a per-account health score with a one-paragraph reasoning. CSMs start their morning by reviewing the bottom 10%. not chasing the loudest customers.

4. Renewal forecasting + early intervention. 90 days before each renewal, the system flags accounts at risk and drafts a tailored CSM intervention plan (specific value moments to highlight, expansion opportunities, decision-makers to engage).

Customer Lifecycle Automationsignup → renewal
  1. 01
    TriggerNew customer signs up

    Sign-up data flows into Claude, which extracts the declared use case and primary success metric.

  2. 02
    StepTailored onboarding sequence

    Interactive onboarding adapted to use case. Stalled steps page the assigned CSM with talking points.

  3. 03
    StepInbound tickets triaged

    Every ticket auto-classified by severity, category, tier and routed to the right engineer or CSM with context.

    4 min → 20s
  4. 04
    StepNightly health scoring

    Usage, support volume, payments and contract terms feed a per-account health score with reasoning.

  5. 05
    DecisionRenewal risk flagged at 90d

    Bottom-decile accounts surface with a drafted CSM intervention plan: value moments, expansion plays, key contacts.

  6. 06
    OutputCSM acts before churn

    Human review + sign-off on every customer-facing message. AI proposes, CSM approves.

    +$340K renewal saves
Support Ticket Triage
InputInbound support ticket (Linear / Slack / email)
ClassifierSeverity × category × customer tier
  • Blocker · enterprise12%
    Senior engineer paged2h SLA
  • Standard · integration34%
    Integration specialist8h SLA
  • Standard · billing18%
    Finance + CS rep4h SLA
  • Bug report22%
    Engineering backlog with repro context
  • Feature request14%
    Product backlog (monthly review)
+31
Onboarding NPS
42 → 73
−44%
Annual churn rate
8d → 36h
Time to first value
+$340K
Renewal revenue saved
early intervention
Customer Success Operations
MetricBeforeAfterΔ
Time to first kickoff call14 days2 days−86%
Avg ticket triage time4 min20s−92%
Tickets resolved within SLA62%91%+29 pts
At-risk renewals identified ≥60d out23%94%+71 pts
Annual churn rate18%10%−8 pts

Outcome

The company avoided three CSM hires (~$390K annualised). Onboarding became a competitive advantage. they started referencing the 36-hour time-to-first-value in sales pitches. Annual churn dropped from 18% to 10%. for a SaaS company at their scale, that's roughly $1.4M of recurring revenue retained.