Skill: mova-compact/mova-churn-prediction (version 1.0.1)

--- 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](screenshots/01-input.jpg) **Step 2 — AI analysis: 300 at-risk customers, avg score 0.75, top signals and findings** ![Step 2](screenshots/02-analysis.jpg) **Step 3 — Decision recorded: launch_selective top 10 by churn score + audit receipt** ![Step 3](screenshots/03-audit.jpg) ## 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 | Output | Description | |--------|-------------| | Customers analyzed | Total in segment | | At-risk count | Above threshold | | Avg churn score | Average probability for at-risk group | | Per-customer score | 0.0–1.0 churn probability | | Top contributing factors | Feature breakdown (e.g. login drop, support volume) | | Model version | Scoring model identifier and date | | Recommended retention actions | Per-customer suggested action | | Recommended decision | AI-suggested campaign choice | | Decision options | launch_campaign / launch_selective / defer / escalate | | Audit receipt ID | Permanent signed record of the campaign decision | | Compact journal | Full 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 ``` | Option | Description | |---|---| | `launch_campaign` | Launch retention campaign for all high-risk customers | | `launch_selective` | Launch for top-N only (specify N in reason) | | `defer` | Defer to next review cycle | | `escalate` | Escalate 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 ID | What it covers | |---|---| | `connector.analytics.customer_events_v1` | Customer activity event stream | | `connector.ml.churn_model_v1` | Churn prediction model (inference endpoint) | | `connector.crm.customer_lookup_v1` | Customer 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)