CRM

Renewal-Risk Score from Declining Product Usage

Runs nightly against your BigQuery usage warehouse, flags accounts whose 30-day active usage dropped sharply versus the prior period.

CategoryCRM
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionQuery 30-day vs prior usage from BigQueryGoogle BigQueryBigQuery
  • LogicCompute risk score weighted by ARR and days-to-renewal
  • LogicKeep only accounts newly in medium/high risk
  • ActionWrite risk score and tier to Salesforce AccountSalesforce
  • OutputPost ranked high-risk digest to CS Slack channelSlack

What it does

This workflow detects accounts that are quietly disengaging before their renewal date. Each night it compares recent product usage against the prior window, computes a renewal-risk score, syncs that score onto the Salesforce Account, and surfaces the worst offenders to the Customer Success team.

When to use it

Use it when your renewals slip because nobody noticed usage falling until the QBR. It works best if product telemetry already lands in BigQuery and renewal/ARR data lives in Salesforce.

How it works

  1. 1A nightly schedule fires the run.
  2. 2BigQuery returns per-account active-seat and event counts for the last 30 days versus the prior 30.
  3. 3A scoring step computes percent decline, weights it by days-to-renewal and ARR, and buckets each account into low/medium/high risk.
  4. 4A filter keeps only accounts that moved into medium or high risk since the last run.
  5. 5Each surviving account gets its `Renewal_Risk_Score__c` and risk tier written to Salesforce.
  6. 6A digest of high-risk accounts, sorted by ARR at risk, is posted to the CS Slack channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect SalesforceAccounts, opportunities, cases.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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