How does this skill differ from simply asking an LLM to summarize my survey results?

This skill uses a multi-layered evidence framework to map unstructured text to specific churn themes, identifying not just what customers say, but the underlying product or pricing issues driving the decision.

What data formats do I need to provide for the analysis to work?

The skill accepts raw data exports in CSV, JSON, or plain text formats from major platforms like Stripe, ProfitWell, Typeform, and Zendesk.

What exactly is included in the purchase of this skill?

You will receive the core logic for the feedback categorization, the prioritization framework, and structured templates for generating retention experiment reports.

Can I use this skill if I only have churn numbers but no written customer feedback?

While it excels at analyzing qualitative feedback, it requires text-based responses to function; it cannot perform financial forecasting or statistical modeling on purely numerical datasets.

Which AI agents or platforms is this skill compatible with?

The skill is designed to be compatible with leading LLM-powered agents and research tools that support structured data processing and text analysis.

Can I analyze different subscription tiers separately within the same report?

Yes, the analysis framework is built to distinguish between different tiers, allowing you to isolate why high-value enterprise accounts are churning compared to self-serve users.

Subscription Cancellation Feedback Analyzer Skill

Example session with this skill installed

Analyze these 3 exit responses:

'Too expensive for a small team' (Pro plan, 2 months).
'Missing the Slack integration' (Pro plan, 6 months).
'Hard to set up' (Free plan, 1 day).

Read your context and instructions
Compiled the subscription cancellation feedback

I have analyzed the 3 cancellation responses provided. The feedback shows a split between price sensitivity in early-stage Pro users and technical friction in the onboarding phase for Free users.

Subscription Cancellation Feedback Analysis

1. Cancellation Reason Categories

Category	Response Count	Share	Example Evidence	Confidence
Pricing/Value	1	33%	"Too expensive for a small team"	High
Missing Features	1	33%	"Missing the Slack integration"	High
Usability/Onboarding	1	33%	"Hard to set up"	High

2. Primary Churn Themes

Theme	Frequency	Customer Segment	Evidence	Interpretation
Price-Value Gap	1	Pro / Short Tenure	"Too expensive"	Potential mismatch between Pro pricing and small team budgets.
Integration Gaps	1	Pro / Mid Tenure	"Slack integration"	Functional blocker preventing long-term stickiness.
Onboarding Friction	1	Free / New	"Hard to set up"	Immediate bounce due to high time-to-value.

3. Root-Cause Analysis

Stated Reason: Hard to set up.
Supporting Evidence: Free plan user leaving after 1 day.
Possible Underlying Cause: Onboarding flow lacks guidance or automated configuration.
Confidence: Medium (single data point).

4. Retention Opportunities

Opportunity	Related Churn Pattern	Target Segment	Recommended Action	Priority
Simplify Onboarding	Initial Friction	Free Plan	Implement a setup wizard or template library.	High
Slack Integration	Feature Gap	Pro Plan	Prioritize Slack in the product roadmap.	Medium
Team-Based Pricing	Pricing	Small Teams	Explore a 'Starter' tier between Free and Pro.	Low

Next steps

Review the onboarding logs for the 'Hard to set up' user to find the specific drop-off screen.
Survey existing Pro users to validate if a Slack integration is a top-voted feature request.
Validate if 'small team' churn is a recurring trend across the Pro segment.

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

About this skill

Customer churn data is often a messy pile of qualitative feedback that product teams struggle to prioritize. This skill transforms raw cancellation responses into a structured analysis of root causes, segment patterns, and prioritized retention experiments.

What it does

Categorize feedback by mapping raw customer responses to primary and secondary themes with confidence scores.
Identify churn patterns by correlating exit reasons with customer segments, subscription tiers, and tenure.
Analyze root causes to distinguish between stated customer excuses and underlying product or pricing issues.
Prioritize opportunities by ranking retention actions based on evidence frequency and implementation effort.
Detect journey signals to pinpoint where friction occurs, from onboarding gaps to competitor switching.

How it works

Input raw data by providing cancellation survey responses, customer segments, and plan details.
Normalize and validate the feedback to remove duplicates and flag ambiguous or low-quality entries.
Execute theme analysis to generate structured tables covering sentiment, frequency, and evidence-backed interpretations.
Review recommendations including specific retention experiments and data collection improvements.

Frameworks & tools

Works with any CSV, JSON, or text export from tools like Stripe, ProfitWell, Typeform, Zendesk, or internal SQL databases.

Why this beats prompting it yourself

Generic prompts often hallucinate customer motivations or fail to distinguish between frequency and severity. This skill uses a strict evidence-led framework that preserves nuance in multi-reason feedback and flags data gaps rather than making assumptions.

Use cases

Analyze monthly exit surveys to identify missing product features.
Compare churn drivers between high-value enterprise accounts and self-serve plans.
Prepare a churn report for product roadmapping sessions.
Design targeted retention experiments for specific customer segments.

Known limitations

Requires qualitative text feedback to function. It cannot perform statistical significance testing or financial forecasting without external quantitative data.

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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Subscription Cancellation Feedback Analyzer Skill