customer-success-manager
Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/alirezarezvani/customer-success-managerWhat This Skill Does
The Customer Success Manager skill provides production-grade analytics for SaaS account management without the complexity of external dependencies. It leverages multi-dimensional weighted scoring models to evaluate customer health, calculate churn risk, and identify expansion opportunities. By processing standard JSON customer datasets, it offers deterministic, repeatable insights that help CSM teams prioritize their book of business. The toolset analyzes usage telemetry, engagement metrics, support activity, and relationship data to classify accounts into Red/Yellow/Green categories while providing trend analysis for period-over-period performance tracking.
Installation
To integrate this skill into your OpenClaw environment, use the following CLI command:
clawhub install openclaw/skills/skills/alirezarezvani/customer-success-manager
Ensure you have the required Python environment configured, as this skill utilizes standard library scripts to ensure security and portability. Once installed, ensure your JSON input files follow the structure defined in assets/sample_customer_data.json to leverage the full suite of scoring modules.
Use Cases
- Proactive Churn Prevention: Automatically flag "at-risk" accounts based on behavioral shifts (e.g., declining login frequency or high escalation rates) before the renewal date approaches.
- Expansion Identification: Analyze adoption depth and whitespace mapping to recommend high-impact upsell or cross-sell opportunities for your healthiest accounts.
- Executive Reporting: Generate data-backed insights for Quarterly Business Reviews (QBRs) and Success Plans by leveraging trend analysis to demonstrate value realization to stakeholders.
- Segment-Based Management: Apply distinct scoring thresholds for different tiers (Enterprise vs. SMB) to ensure your time is allocated where the potential impact is highest.
Example Prompts
- "Run the health score calculator on my Q3 customer dataset and list all enterprise accounts that dropped below a 60% health score."
- "Analyze the churn risk for my mid-market segment; which accounts show the most significant decline in engagement compared to the previous period?"
- "Identify the top 5 expansion opportunities from this dataset based on high usage, high CSAT, and low multi-threading depth."
Tips & Limitations
- Tip: Keep your input JSON consistently formatted. Accuracy is heavily dependent on the quality of your telemetry data.
- Tip: Use the trend analysis feature to visualize trajectory; a stagnant account with declining trends is often more dangerous than a low-scoring account that is trending upward.
- Limitation: This skill does not perform real-time API polling; it is a static analysis tool designed for batch processing.
- Limitation: It is strictly logic-based. It does not utilize machine learning models or probabilistic forecasting, meaning it cannot 'learn' from unstructured communication logs without prior conversion into numeric scores.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-alirezarezvani-customer-success-manager": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read
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