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mova-churn-predictionMova 流失预测

Agent Skill

mova-churn-prediction 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mova-churn-prediction(Mova 流失预测)
来源仓库:https://github.com/mova-compact/mova-churn-prediction
安装命令:
openclaw skills install mova-churn-prediction
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install mova-churn-prediction

简介

预测客户流失概率并制定保留活动策略。

  • 适用于客户生命周期管理与留存优化。
  • 结合行为信号与人工审批制定干预方案。
  • 需输入客户历史数据与交互记录。mova-churn-prediction 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 注意模型偏差对特定群体的影响。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
mova-churn-prediction
description
Analyze customer behavior signals to predict churn probability and route retention campaign decisions through a human approval gate via MOVA HITL. Trigger when the user asks to predict customer churn, requests a retention analysis, or wants to identify at-risk customers. Human sign-off is required before any targeted retention action is launched.
license
MIT-0
metadata
{"openclaw":{"plugin":{"name":"MOVA","installCmd":"openclaw plugins install openclaw-mova"},"dataSentToExternalServices":[{"service":"MOVA API (api.mova-lab.eu)","data":"segment ID, analysis period, customer behavior features, churn probability scores, model version, human decision, audit metadata"},{"service":"Customer events connector (read-only)","data":"customer activity signals (logins, transactions, support tickets, feature usage) for the specified segment and period"},{"service":"Churn model connector (read-only)","data":"customer feature vectors evaluated by churn prediction model"},{"service":"CRM connector (read-only)","data":"customer profile and segment metadata lookup"}]}}
Contract Skill — A ready-to-use MOVA HITL workflow. Requires the openclaw-mova plugin.

MOVA Churn Prediction

Run an AI churn risk assessment on your customer segment — get a ranked at-risk list with contributing factor breakdown, then route the retention campaign decision through a mandatory human approval gate with a full audit trail.

What it does

  1. Behavior ingestion — customer activity signals (logins, transactions, support tickets, feature usage) for the specified segment and period
  2. Churn model — probability score per customer (0.0–1.0) with contributing factor breakdown
  3. High-risk list — ranked list of at-risk customers above threshold with recommended retention actions
  4. Human gate — customer success manager reviews the list and chooses: launch campaign / launch selective / defer / escalate
  5. Audit receipt — input features, model version, prediction scores, and human approval are all logged

Escalation rules enforced by policy:

  • GDPR check required before any customer is targeted — consent and legitimate interest must be confirmed
  • Model version drift (> 90 days) → recommend review before launch
  • Campaigns above budget threshold → escalate to VP required

Requirements

Plugin: MOVA OpenClaw plugin must be installed in your OpenClaw workspace.

Data flows:

  • Segment ID + period + threshold → api.mova-lab.eu (MOVA platform, EU-hosted)
  • Customer activity data → events connector (read-only, no raw data stored by MOVA)
  • Feature vectors → churn model connector (inference only, read-only)
  • Customer profiles → CRM connector (read-only)
  • Audit journal → MOVA R2 storage, signed
  • No data sent to third parties beyond the above

Demo

Step 1 — Segment submitted: SEG-ENTERPRISE, 30 days, threshold 0.70 Step 1

Step 2 — AI analysis: 300 at-risk customers, avg score 0.75, top signals and findings Step 2

Step 3 — Decision recorded: launch_selective top 10 by churn score + audit receipt Step 3

Quick start

Say "run churn analysis for segment SEG-ENTERPRISE over the last 30 days":

segment_id: SEG-ENTERPRISE
period_days: 30
threshold: 0.70
requestor_id: EMP-0441

The agent fetches behavior signals, scores churn probability per customer, shows the ranked at-risk list with top contributing factors, then asks for your retention decision.

Why contract execution matters

  • GDPR compliance built in — policy enforces consent check before any customer is targeted, not left to the agent's discretion
  • Model version tracking — the exact model version used for scoring is locked in the audit trail, enabling reproducibility audits
  • Immutable decision record — when a customer asks "why did I receive this offer?" or an auditor asks "who approved this campaign?" — the answer is in the system
  • EU AI Act / GDPR Article 22 ready — automated profiling for targeted campaigns requires documented human oversight

What the user receives

OutputDescription
Customers analyzedTotal in segment
At-risk countAbove threshold
Avg churn scoreAverage probability for at-risk group
Per-customer score0.0–1.0 churn probability
Top contributing factorsFeature breakdown (e.g. login drop, support volume)
Model versionScoring model identifier and date
Recommended retention actionsPer-customer suggested action
Recommended decisionAI-suggested campaign choice
Decision optionslaunch_campaign / launch_selective / defer / escalate
Audit receipt IDPermanent signed record of the campaign decision
Compact journalFull event log: feature pull → scoring → human decision

When to trigger

Activate when the user:

  • Asks to predict churn, run retention analysis, or identify at-risk customers
  • Provides a segment ID or cohort with a date range
  • Sets up a scheduled churn review (weekly / monthly)

Before starting, confirm: "Run churn analysis for segment [SEG-ID] — last [N] days?"

If segment ID or period is missing — ask once.

Step 1 — Submit customer segment for analysis

Call tool mova_hitl_start_churn with:

  • segment_id: customer segment or cohort identifier
  • period_days: lookback period in days (e.g. 30)
  • threshold: minimum churn probability to include in at-risk list (e.g. 0.70)
  • requestor_id: employee ID of the requestor

Step 2 — Show at-risk list and decision options

If status = "waiting_human" — show the churn summary and ask to choose:

Segment:           SEG-ID
Period:            N days
Customers at risk: COUNT  (above THRESHOLD)
Avg churn score:   AVG

Top at-risk customers:
[ID | Name | Score | Top factor]
Recommended action: ACTION ← RECOMMENDED
OptionDescription
launch_campaignLaunch retention campaign for all high-risk customers
launch_selectiveLaunch for top-N only (specify N in reason)
deferDefer to next review cycle
escalateEscalate to VP of Customer Success

Call tool mova_hitl_decide with:

  • contract_id: from the response above — this is ctr-chn-xxxxxxxx, NOT the segment ID
  • option: chosen decision
  • reason: manager reasoning

Step 3 — Show audit receipt

Call tool mova_hitl_audit with contract_id. Call tool mova_hitl_audit_compact with contract_id for the full signed scoring chain.

Connect your real data systems

By default MOVA uses a sandbox mock. To route analysis against your live infrastructure, call mova_list_connectors with keyword: "churn".

Relevant connectors:

Connector IDWhat it covers
connector.analytics.customer_events_v1Customer activity event stream
connector.ml.churn_model_v1Churn prediction model (inference endpoint)
connector.crm.customer_lookup_v1Customer profile and segment metadata

Call mova_register_connector with connector_id, endpoint, optional auth_header and auth_value.

Rules

  • NEVER make HTTP requests manually
  • NEVER invent or simulate churn scores — if a tool call fails, show the exact error
  • Use MOVA plugin tools directly — do NOT use exec or shell
  • CONTRACT_ID is ctr-chn-xxxxxxxx from the mova_hitl_start_churn response — NOT the segment ID

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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按下载量换算1,895

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安装流程涉及命令执行,可能通过 openclaw skills install mova-churn-prediction 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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