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Retention & Save Agent

Detects churn risk and runs the save conversation within approved offer limits.

8–20% higher save rate against a hold-out control

The problem. Churn is detected after the cancellation request arrives, when the only lever left is a discount, and save offers are applied inconsistently across agents and channels.

The agent reads behavioural, billing and interaction signals to identify at-risk accounts, then runs the retention conversation in the customer's preferred channel.

Offers are selected from an approved catalogue with margin limits per segment, so the save is economic rather than reflexive.

Every save, decline and reason code is written back to CRM, giving the business a real churn-reason dataset instead of anecdotes.

Before

Retention is a reactive discount conversation at cancellation, with no consistent offer discipline and no reliable reason data.

After

At-risk customers are reached before they cancel, offers respect margin, and churn reasons are measured.

Reference architecture

Where this agent sits in the stack.

Agents that take action need more than a model — they need tools, policy, approvals and an audit trail wired in from the first workflow.

Where work arrivesSystems of record
  1. 01

    Task intake & channels

    Requests arriving from chat, email, queues, forms, tickets and events — normalised into work items with owner, priority and SLA.

  2. 02

    Agent runtime

    Planner, memory, tool registry and evaluation loop, with deterministic guardrails on what each agent may attempt and when it must stop.

  3. 03

    Tools & actions

    Typed API actions against CRM, ERP, ITSM and core platforms, each with auth scope, rate limits, idempotency and rollback behaviour.

  4. 04

    Human-in-the-loop

    Approval checkpoints for regulated or high-value steps, exception queues and a reviewer console with full reasoning and evidence.

  5. 05

    Systems of record

    The transactional systems the agent updates — records written once, reconciled, and traceable back to the triggering request.

Integration surface

  • CRM and customer data platform
  • Billing or subscription system
  • Offer and pricing catalogue
  • CCaaS and outbound messaging

Guardrails & human oversight

  • Offer authority bounded by margin limits per segment; higher-value saves need human approval.
  • Contact permissions and frequency caps respected on every outreach.
  • Every action outside policy stops at a reviewer queue with the agent's reasoning, evidence and proposed change attached.
  • Complaint, regulatory or vulnerability signals stop the save motion and escalate.

What has to be true first

  • Churn signal data with enough history to build a risk view.
  • An approved offer catalogue with margin limits.
  • Consent and contact-permission data.

Security, data & compliance

  • Runs under a dedicated service identity with least-privilege, per-tool scopes — never a shared admin account.
  • Customer and employee data stays inside your tenancy and region; no training on your data by default.
  • PII is redacted before it reaches a model, and prompts, responses and tool calls are retained under your retention policy.
  • Every tool call, input, decision and system write is logged and replayable for audit and model-risk review.
Rollout

How this agent reaches production.

  1. Weeks 1–3 · Scope

    Risk signal review, segment and offer-limit definition, channel selection.

  2. Weeks 4–7 · Build

    Signal pipeline, offer engine integration, conversation design and approval gates.

  3. Weeks 8–12 · Production pilot

    One segment live against a hold-out control group.

  4. Quarter 2+ · Scale & run

    Expanded segments and channels with monthly save-rate and margin reporting.

Measurement plan

What we agree to be measured on.

Ranges drawn from comparable production engagements. Your baseline is agreed before build starts, and the same numbers are reported after go-live.

MetricExpected range
Save rate on contacted at-risk accounts8–20% higher than control
Average discount given per save10–25% lower
At-risk accounts reached before cancellation60%+
Churn reasons captured as structured data90%+ of conversations

Model the business case: Contact Center AI ROI calculator →

Production pilot

One at-risk segment run against a control group, with offer discipline and measured revenue retained.

Fixed-price scope · milestone billing · price on request.

Scale & run

Portfolio-wide retention motion across channels with margin governance and monthly reporting.

Retained pod · quarterly outcome review · price on request.

Agent specification

The full Retention & Save Agent specification, as a PDF.

A multi-page specification your architecture, security and procurement reviewers can read without a call: what the agent does, the architecture, the integration surface, autonomy and guardrails, security posture, rollout plan, measurement plan and engagement shape.

  • Process before and after, with the decision that stays with a human
  • Layered architecture diagram and named integration surface
  • Guardrails, approval gates, escalation and audit trail
  • Security, data handling and compliance posture
  • Phase-by-phase rollout and the measurement plan
Get the agent spec

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Delivered with this playbook

Contact center modernization: from IVR to conversational AI

A step-by-step migration model for retiring legacy IVR and moving to conversational AI without breaking CX. Covers platform selection, data readiness, integrations, agent experience, QA and cutover — proven across NICE, Genesys, Amazon Connect, Kore.ai and Five9 estates.

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Related use cases
Price on request

Get a written estimate for the Retention & Save Agent.

Tell us the process, the systems it touches and the compliance scope. We come back with a scope, a measurement plan and a written estimate — no published band that would not apply to you.

solutionRetention & Save Agent — routed to this team

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