Predict customer churn with AI analysis of HubSpot and Google Sheets data

Predict customer churn with AI analysis of HubSpot and Google Sheets data

Team Pick
Lifecycle & Email
n8n
PollupAI's template builds a health score out of three inputs nobody looks at together: deal age, ticket sentiment, and usage trend.
Source
n8n
Tools
HubSpot, Google Sheets, AI model
Runs
Scheduled
Cost
Free template, API costs
Last updated
August 7, 2026

What it is.

It pulls deals from HubSpot, collects linked support tickets and feature usage from a Google Sheet, runs sentiment analysis on the tickets to produce a customer health score, then has an AI step weigh deal age, sentiment, and usage trend against thresholds you set.

When risk crosses the line it emails the person who owns the account, with the data and a suggested next step attached. Each of those three signals is weak on its own and useful together.

What you get.

  • A customer health score built from CRM, support, and usage data in one pass.
  • Sentiment analysis over the ticket text, which carries more than a ticket count.
  • Risk evaluation against thresholds you define for deal age, sentiment, and usage.
  • An email to the responsible owner carrying the data and a suggested next step.
HOW TO USE IT

How to set it up.

1

Connect HubSpot and confirm which deal pipeline and stages you want watched.

2

Build the Google Sheet with support tickets and feature usage in the columns the template expects.

3

Check the sentiment step against tickets you have already read, because sentiment scoring is the piece most likely to be wrong.

4

Set your own thresholds for deal age, sentiment, and usage decline; the defaults are the author's.

5

Point the alert at the named account owner, so somebody owns each one.

6

Run it against last quarter's churned accounts first, and see whether it would have caught them.

Use cases

Health score from what you already have

Build one score per account from the data you already collect.

Read the ticket sentiment

Let the analysis pass over the ticket text nobody has time to read.

Give CS a call list

Sort the scores and hand over the accounts worth a conversation this week.

Best for

Accounts you only hear from at renewal

Silence reads as health until it does not, and this watches the signals that move first.

Teams without a customer success platform

A health score from HubSpot plus a sheet covers most of what a dedicated tool would show.

Testing a churn hypothesis

Backtesting against accounts that already left tells you whether your thresholds mean anything.

Open the original.

Hosted on n8n, free to open.
Open the Tool

Questions about Predict customer churn with AI analysis of HubSpot and Google Sheets data

What do I have to build first?
Can I trust the sentiment scores?
Are the thresholds usable?

Questions about Lifecycle & Email

What predicts churn earliest?
What should a churn alert trigger?
Strengths
  • It joins three signals in one score, so a quiet account with rising ticket negativity gets caught.
  • Sentiment runs over the ticket text, which carries more than a ticket count does.
  • The alert goes to the account owner with a next step attached, so it lands as work.
Limitations
  • Usage data has to arrive in a Google Sheet, so somebody has to build that export first.
  • Sentiment analysis on short support tickets is noisy, so read the first month of scores before you trust them.
  • Thresholds arrive as the author's guesses, and a churn model on somebody else's numbers predicts nothing.
  • It alerts and takes no action, so the save is still a human conversation.
Skip this if
  • Skip it if you already have a health score your CS team works from.
Ideal for
  • Customer success leads
  • RevOps leads
  • Stage: Series A onward

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