- Updated all packages from ai@6.0.23 to ai@6.0.49 - Added pnpm override to ensure consistent version - Created changeset for publishing affected packages
80 lines
2.3 KiB
JSON
80 lines
2.3 KiB
JSON
{
|
|
"name": "@tpmjs/churn-risk-score",
|
|
"version": "0.1.0",
|
|
"description": "Scores customer churn risk based on usage, engagement, and support signals",
|
|
"type": "module",
|
|
"keywords": [
|
|
"tpmjs",
|
|
"cx",
|
|
"churn",
|
|
"retention",
|
|
"customer-success",
|
|
"analytics"
|
|
],
|
|
"exports": {
|
|
".": {
|
|
"types": "./dist/index.d.ts",
|
|
"default": "./dist/index.js"
|
|
}
|
|
},
|
|
"files": [
|
|
"dist"
|
|
],
|
|
"scripts": {
|
|
"build": "tsup",
|
|
"dev": "tsup --watch",
|
|
"type-check": "tsc --noEmit",
|
|
"clean": "rm -rf dist .turbo"
|
|
},
|
|
"devDependencies": {
|
|
"@tpmjs/tsconfig": "workspace:*",
|
|
"tsup": "^8.5.1",
|
|
"typescript": "^5.9.3"
|
|
},
|
|
"publishConfig": {
|
|
"access": "public"
|
|
},
|
|
"repository": {
|
|
"type": "git",
|
|
"url": "https://github.com/ajaxdavis/tpmjs.git",
|
|
"directory": "packages/tools/official/churn-risk-score"
|
|
},
|
|
"homepage": "https://tpmjs.com",
|
|
"license": "MIT",
|
|
"tpmjs": {
|
|
"category": "cx",
|
|
"frameworks": [
|
|
"vercel-ai"
|
|
],
|
|
"tools": [
|
|
{
|
|
"name": "churnRiskScoreTool",
|
|
"description": "Scores customer churn risk based on usage, engagement, and support signals. Provides risk score (0-100) with detailed contributing factors and recommendations.",
|
|
"parameters": [
|
|
{
|
|
"name": "customer",
|
|
"type": "object",
|
|
"description": "Customer data with activity metrics including usage, engagement, and support interactions",
|
|
"required": true
|
|
}
|
|
],
|
|
"returns": {
|
|
"type": "ChurnRiskScore",
|
|
"description": "Risk score with contributing factors, risk level, and retention recommendations"
|
|
},
|
|
"aiAgent": {
|
|
"useCase": "Use this tool to identify at-risk customers, prioritize retention efforts, and proactively reduce churn. Ideal for customer success teams and account managers.",
|
|
"limitations": "Requires comprehensive customer data. Risk scoring is heuristic-based and should be combined with human judgment for critical decisions.",
|
|
"examples": [
|
|
"Identify customers at high risk of churning",
|
|
"Prioritize outreach for retention campaigns",
|
|
"Monitor customer health scores over time"
|
|
]
|
|
}
|
|
}
|
|
]
|
|
},
|
|
"dependencies": {
|
|
"ai": "6.0.49"
|
|
}
|
|
}
|