How Greyline were able to reduce churn by 35% by automating their customer success workflows

The customers they didn't know were leaving

Greyline's customer success team was good at their jobs. The problem was they were reactive. By the time a customer flagged dissatisfaction, it was usually too late to turn things around. The signals were always there in the data — dropping login frequency, unused features, declining report generation. But with 200+ accounts and a CS team of four, nobody had time to monitor usage dashboards all day.

"We were doing post-mortems on churned accounts and realizing we had all the data to see it coming. We just weren't acting on it in time." — Sam Thornton, VP of Product, Greyline

A customer success process that couldn't scale

With four people covering 200+ accounts, proactive outreach was almost impossible. The team spent most of their time on admin rather than actually talking to customers.

What they built with Violet

Greyline built a churn detection workflow that monitors usage data across all accounts and automatically flags at-risk customers based on a set of predefined signals — then routes them to the right CS rep without anyone having to check a dashboard.

trigger:
  type: analytics.threshold
  source: "internal_db"
  conditions:
    - metric: "logins_last_14_days"
      operator: "<"
      value: 3
    - metric: "reports_generated_last_30_days"
      operator: "<"
      value: 1

actions:
  - type: crm.add_tag
    contact: "{{account.id}}"
    tag: "at-risk"
  - type: crm.assign_task
    owner: "{{account.cs_owner}}"
    title: "Check in with {{account.name}} — low engagement detected"
    due: "tomorrow"
  - type: slack.send_message
    channel: "#customer-success"
    message: "⚠️ {{account.name}} flagged as at-risk. Assigned to {{account.cs_owner}}."
  - type: email.send
    to: "{{account.cs_owner.email}}"
    template: "at-risk-briefing"
    data: "{{account.usage_summary}}"

trigger:
  type: analytics.threshold
  source: "internal_db"
  conditions:
    - metric: "logins_last_14_days"
      operator: "<"
      value: 3
    - metric: "reports_generated_last_30_days"
      operator: "<"
      value: 1

actions:
  - type: crm.add_tag
    contact: "{{account.id}}"
    tag: "at-risk"
  - type: crm.assign_task
    owner: "{{account.cs_owner}}"
    title: "Check in with {{account.name}} — low engagement detected"
    due: "tomorrow"
  - type: slack.send_message
    channel: "#customer-success"
    message: "⚠️ {{account.name}} flagged as at-risk. Assigned to {{account.cs_owner}}."
  - type: email.send
    to: "{{account.cs_owner.email}}"
    template: "at-risk-briefing"
    data: "{{account.usage_summary}}"

trigger:
  type: analytics.threshold
  source: "internal_db"
  conditions:
    - metric: "logins_last_14_days"
      operator: "<"
      value: 3
    - metric: "reports_generated_last_30_days"
      operator: "<"
      value: 1

actions:
  - type: crm.add_tag
    contact: "{{account.id}}"
    tag: "at-risk"
  - type: crm.assign_task
    owner: "{{account.cs_owner}}"
    title: "Check in with {{account.name}} — low engagement detected"
    due: "tomorrow"
  - type: slack.send_message
    channel: "#customer-success"
    message: "⚠️ {{account.name}} flagged as at-risk. Assigned to {{account.cs_owner}}."
  - type: email.send
    to: "{{account.cs_owner.email}}"
    template: "at-risk-briefing"
    data: "{{account.usage_summary}}"

CS reps now start each day with a prioritized list of accounts that need attention — automatically generated, with context already attached.

The results after 90 days

"The churn number speaks for itself. But what I didn't expect was how much better our CS team felt about their jobs. They were finally doing CS work instead of spreadsheet work." — Sam Thornton, VP of Product, Greyline

What they automated next

With churn detection running, Greyline built an expansion workflow — automatically identifying accounts showing high engagement and routing them to their account management team for upsell conversations.

What's next for Greyline

Greyline is building a quarterly business review workflow that automatically compiles an account health summary, pulls key usage metrics, and generates a draft QBR deck for each CS rep — ready two weeks before every review cycle.

"Violet became the connective tissue of our entire CS operation. I genuinely don't know how we managed without it." — Sam Thornton, VP of Product, Greyline