Agent in a Box

Autonomous SaaS Churn Recovery & Retention Agent

operations
1

Autonomous SaaS Churn Recovery & Retention Agent

Problem Statement

B2B SaaS companies lose between 5% and 15% of their annual recurring revenue (ARR) to "silent churn"—customers who stop using the product months before their subscription actually expires. Current recovery methods are reactive: triggered only after a cancellation request is submitted or a payment fails. By this stage, the customer has usually already integrated a competitor or lost the internal budget for the tool.

The core issue is the "Data-Action Gap." Customer Success Managers (CSMs) are overwhelmed with data from Mixpanel, Stripe, and Zendesk but lack the bandwidth to synthesize this into personalized intervention. Generic automated emails ("We miss you!") are ignored because they don't address the specific friction points. For a startup scaling from 50 to 500 customers, it becomes mathematically impossible for humans to monitor every account's health score, feature adoption rate, and support ticket sentiment simultaneously. Similar to how a Customer Support Triage Agent manages incoming tickets, this agent proactively manages account health.

This agent solves the problem by operating in the "pre-churn" phase. It identifies micro-signals of dissatisfaction—such as a drop in API calls, a decrease in session duration, or a specific tone in a support ticket—and executes a multi-channel recovery play. Instead of a generic discount, it offers a personalized "Success Plan" based on the features the user hasn't tried yet, effectively automating the high-touch CSM experience at scale.

What the Agent Does/Doesn't Do

  • Does: Monitors product usage trends, analyzes support ticket sentiment, predicts churn probability using historical patterns, and sends personalized recovery sequences via email or Slack.
  • Does: Generates "Usage Gap Reports" for the customer, showing them exactly what value they are leaving on the table.
  • Doesn't: Handle billing disputes or credit card failures (this is left to dunning software like ProfitWell or an Automated B2B Invoice Reconciliation & Dispute Agent).
  • Doesn't: Make final decisions on high-value (Enterprise) account cancellations without a human-in-the-loop (HITL) trigger.

Workflow

  1. Signal Aggregation: Agent pulls weekly usage data from Segment/Mixpanel and support history from Zendesk.
  2. Risk Scoring: The agent compares current activity against the "Ideal Customer Profile" (ICP) baseline to flag accounts with a >30% drop in core feature engagement.
  3. Contextual Synthesis: For flagged accounts, the agent reads the last 3 support interactions to identify if the drop is due to a technical bug or a missing feature.
  4. Personalized Outreach: The agent generates a 3-step sequence. Step 1 is a "Value Check-in" providing a custom report; Step 2 is a "Feature Spotlight" based on unused tools; Step 3 is a "Human Hand-off" to a CSM. This mirrors the precision of a Hyper-Personalized Cold Outreach Researcher but for retention.
  5. Feedback Loop: If the user re-engages, the agent logs the "Recovery Reason" in the CRM to refine future prediction models.

Tool Stack

  • Data Orchestration: Census or Hightouch (to sync warehouse data to the agent).
  • LLM: GPT-4o (for sentiment and personalized drafting).
  • Workflow Engine: Make.com or LangChain.
  • Communication: SendGrid (Email) and Slack (Internal alerts).
  • Pricing: Estimated $250 - $600/mo depending on volume.

Prompt Skeletons

### Prompt 1: Churn Signal Analyst
Identify the primary reason for engagement drop-off for the following customer.
Data Input: {{usage_logs}}, {{support_tickets}}
Baseline: {{icp_benchmarks}}

Analyze for:
1. Technical Friction (repeated bugs)
2. Value Gap (low adoption of 'sticky' features)
3. Champion Departure (change in primary user email activity)

Output: A 2-sentence summary of the 'Churn Hypothesis' and a Risk Score (1-10).

### Prompt 2: Personalized Recovery Architect
Draft a recovery email for {{contact_name}} at {{company_name}}.
Context: They have stopped using {{feature_a}} but were previously heavy users of {{feature_b}}.
Goal: Do not sound like a bot. Reference their specific data.
Structure:
- Observation: "I noticed your team hasn't used {{feature_a}} in 14 days."
- Value Bridge: Explain how {{feature_a}} links to their goal of {{customer_goal}}.
- Low-friction CTA: Offer a 10-minute "Optimization Audit" or a pre-recorded video tutorial.
Constraint: No generic "We value your business" language.

Success Metrics

  • Recovery Rate: % of flagged "at-risk" users who return to baseline usage within 30 days.
  • NRR Impact: Monthly increase in Net Revenue Retention.
  • CSM Efficiency: Reduction in hours spent by humans on manual account audits.