Mova Churn Prediction

Analyze customer behavior signals to predict churn probability and route retention campaign decisions through a human approval gate via MOVA HITL. Trigger wh...

Contract Skill — A ready-to-use MOVA HITL workflow. Requires the openclaw-mova plugin.

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 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":

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