SALES

Sticky-Feature Abandonment Warning for CSMs

Watches for accounts that have stopped using the features most correlated with retention.

CategorySales
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule triggers the abandonment check
  • ActionQuery sticky-feature last-used dates and ARRPostgreSQLPostgres
  • LogicFlag and rank accounts that abandoned a sticky feature
  • ActionDraft per-account re-onboarding playOpenAI
  • ActionCreate prioritized CSM tasks in ClickUpClickUpClickUp
  • OutputPost at-risk digest to customer-success SlackSlack

What it does

It monitors the specific high-value features your data shows are the strongest retention predictors, identifies paying accounts that have abandoned one or more of them, ranks the accounts by contract value and days-since-last-use, and delivers a re-onboarding play to the right CSM.

When to use it

Use this when raw login counts look fine but customers have quietly stopped using the workflows that make them sticky, the silent precursor to a non-renewal.

How it works

  1. 1A weekly schedule triggers the abandonment check.
  2. 2Postgres queries last-used dates for the designated sticky features per account, joined to contract value.
  3. 3A logic step flags accounts that used a sticky feature historically but have zero usage in the last 30 days, ranking them by ARR.
  4. 4An AI step writes a re-onboarding play per account: which feature lapsed, why it matters, and a suggested re-activation offer.
  5. 5A ClickUp task is created per account in the CSM's queue with priority set from the ARR ranking.
  6. 6A digest of the top at-risk accounts posts to the customer-success Slack channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect ClickUpDocs + tasks + chats in one workspace.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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