What specific data do I need to provide for a complete churn report?

The agent requires your subscription records and cancellation data. For a more detailed diagnosis, you can also provide usage logs, support tickets, and survey responses like NPS or CSAT scores.

How does this agent differ from a standard churn dashboard?

Unlike basic dashboards, this agent connects feedback to behavior, distinguishing between what customers say and what they actually do. It also identifies 'evidence gaps' where you lack enough data to make a confident decision.

How do I set up the SaaS Churn Diagnosis Agent after purchase?

This skill is designed for use within your AI agent platform; simply import the skill, and you can begin by uploading your data files or pasting your customer records directly into the chat.

Can I ask the agent to focus on a specific customer segment, like high-value accounts?

Yes, you can request a specific focus, such as investigating why high-tier plan members are leaving or comparing churn rates between customers with different tenure lengths.

What kind of actionable output should I expect at the end of the analysis?

The report provides a prioritized list of retention experiments and follow-up actions, categorized by how much evidence supports the need for that specific intervention.

Does the agent guarantee it will find the exact cause of every cancellation?

The agent identifies correlations and patterns, such as a drop in usage before cancellation, but it will clearly flag when a pattern needs further human investigation to confirm a definitive cause.

SaaS Churn Diagnosis Agent

Example session with this skill installed

April: started with 40 Starter and 30 Team accounts. 8 Starter canceled (6 cited setup); 2 Team canceled (both cited price). No usage data. Give a brief diagnosis and one retention test.

Read your context and instructions
Compiled the saas churn diagnosis

Starter had the higher April cancellation rate. Setup difficulty is the leading stated reason in the supplied data, but it is not a proven cause of all Starter churn.

April Churn Diagnosis

Rates below use cancellations divided by accounts active at the start of April, assuming the cancellations came from those accounts.

Plan	Canceled / starting accounts	Rate	Stated reasons
Starter	8 / 40	20%	6 cited setup; 2 reasons unknown
Team	2 / 30	6.7%	Both cited price
Total	10 / 70	14.3%	Setup was cited by 6 of 10 cancellations

Interpretation: Setup is a well-supported feedback theme among Starter cancellations. Whether poor onboarding explains the difference between plans remains a hypothesis. Team pricing concerns warrant follow-up, not an immediate price change based on two cancellations.

Evidence gaps: No usage, tenure, support history, or setup experience for retained accounts was supplied. One month and small groups limit broader conclusions.

Priority Retention Test

Offer guided setup to a test group of new Starter accounts and compare with a comparable group receiving current onboarding.

Hypothesis: Setup help reduces early cancellations.
Primary metric: Cancellation rate over the same follow-up period.
Guardrail: Support time per account.
Required data: Group assignment, setup completion, signup and cancellation dates.

Next steps

Ask the six customers where setup broke down.
Define the test period and success threshold before launch.

Connects securely to your tools. The creator never sees your data.

About this skill

Customers are leaving, but cancellation notes alone may not explain where to focus. Get an evidence-based churn report that identifies patterns, separates facts from possible explanations, and prioritizes retention actions.

What it does

Finds churn patterns: Compares cancellations across plans, tenure, usage, and customer groups when those details are supplied.
Explains likely drivers: Connects customer feedback and account activity to possible reasons for leaving, with clear evidence limits.
Prioritizes retention work: Suggests targeted follow-ups and experiments tied to the findings.
Flags missing evidence: Highlights incomplete records, conflicting signals, and questions that need investigation.

How it works

Share your analysis period, churn definition, and available customer or subscription records.
Add cancellation reasons, usage data, support history, or survey responses where available.
Ask for a diagnosis in your preferred format.
Review the findings, retention priorities, and measurement checklist.

Works with

Supplied SaaS customer records, subscription details, cancellation feedback, product-usage data, support history, and NPS or CSAT responses.

Why use this skill

It brings scattered retention evidence into one decision-focused report. You can see which actions have support and which need more investigation before committing time or budget.

Use cases

Investigate cancellations among new customers.
Compare churn patterns across subscription plans.
Turn recurring complaints into retention experiments.
Identify evidence gaps before a retention review.

Known limitations

Findings depend on the evidence you supply. Missing data may allow only a partial diagnosis, and observed patterns do not by themselves establish causes.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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SaaS Churn Diagnosis Agent