AI Workflow for Personalized Discount and Plan-Change Recommendations

Manual retention relies on generic offers and agent intuition, leading to missed opportunities and margin erosion. This workflow uses ML to score churn probability and an offer engine to simulate the optimal plan or discount for each customer's usage and payment history. By personalizing the intervention, you convert more at-risk customers at a lower effective discount, directly preserving monthly recurring revenue (MRR).

Agents spend significant time navigating billing systems, checking plan eligibility, and calculating discount impacts. This workflow automates offer construction by integrating with your product catalog, billing engine (e.g., Amdocs, Oracle BRM), and CRM. The AI agent generates compliant, margin-aware recommendations in real-time, slashing handle time and allowing agents to focus on negotiation and empathy.

Reactive discounts often lock customers into suboptimal plans, hurting long-term value. This workflow evaluates the full economic trade-off, often recommending a plan change over a straight discount. By migrating customers to a better-fitting plan (e.g., more data for a heavy user), you improve satisfaction, reduce future churn risk, and frequently increase ARPU, boosting LTV beyond the immediate save.

Blanket retention promotions are expensive and train customers to wait for discounts. This workflow enables surgical precision, applying discounts only where predictive models indicate high churn risk and high lifetime value. This shifts the operating model from costly mass promotions to targeted, economically justified interventions, protecting overall service margin.

The gap between a churn signal and an agent's action is where revenue is lost. This workflow operates in real-time, analyzing live signals (e.g., support call sentiment, usage drop) and pushing a recommendation to the agent desktop or self-service portal during the same interaction. This immediacy dramatically increases conversion likelihood by addressing the customer's pain point in the moment.

Manual offers lack consistency and audit trails, creating regulatory and fairness risks. A custom workflow embeds business rules and approval gates (e.g., max discount tiers by segment) directly into the orchestration logic (using frameworks like LangGraph). Every recommendation is logged with its rationale, customer context, and economic simulation, creating a transparent, governable system for retention spending.

This foundational agent continuously ingests and unifies real-time signals from billing systems (e.g., Oracle BRM, SAP), network OSS (call drop rates, data usage anomalies), CRM (support ticket sentiment, NPS), and product usage logs. It creates a dynamic, 360-degree customer profile that serves as the single source of truth for all downstream scoring and recommendation logic, replacing manual data compilation from fragmented dashboards.

A specialized ML model evaluates the unified customer profile against historical churn patterns. It outputs two key scores: a churn risk probability (likelihood to leave) and a migration propensity score (receptiveness to a plan change vs. a discount). This engine runs continuously, triggering the recommendation workflow only when risk exceeds a configurable threshold, ensuring interventions are timely and targeted.

The core business logic agent. For each at-risk customer, it simulates the financial impact of multiple intervention options. It queries the product catalog for eligible plans, models the net present value (NPV) of a migration versus the cost of a retention discount, and factors in competitor pricing scraped from external feeds. It selects the 1-2 offers that best balance retention probability with lifetime value protection.

Not all offers can be automated. This control layer applies business rules: any discount above a pre-set percentage or plan change with complex contractual implications is routed to a human retention agent for approval via a queue in Salesforce or ServiceNow. All automated offers are logged with a complete rationale (risk score, simulated ROI) for auditability and model refinement.

This agent executes the approved recommendation. It determines the optimal channel (in-app message, SMS, agent desktop alert during a live call) based on customer channel preference and context. It renders the personalized offer, handles the customer's response, and if accepted, triggers the downstream Fulfillment Coordinator to execute the plan change in the billing system (e.g., Amdocs, Netcracker) and update the CRM.

The closed-loop learning system. It tracks offer acceptance rates, subsequent customer behavior (e.g., churn within 90 days), and actual revenue impact. This performance data is fed back to tune the propensity scoring and offer simulation models. Implemented using MLOps pipelines, this component ensures the workflow's recommendations become more accurate and profitable over time, moving beyond a static deployment.