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

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

Quick start

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

CODEBLOCK0

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 | launchcampaign / launchselective / 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
• INLINECODE4: lookback period in days (e.g. 30)
• INLINECODE5: minimum churn probability to include in at-risk list (e.g. 0.70)
• INLINECODE6: 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:

CODEBLOCK1

Option	Description
INLINECODE8	Launch retention campaign for all high-risk customers
INLINECODE9

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
• INLINECODE15: chosen decision
• INLINECODE16: 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
INLINECODE23	Customer activity event stream
INLINECODE24

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
• CONTRACTID is ctr-chn-xxxxxxxx from the movahitlstartchurn response — NOT the segment ID