One of many customer-growth use cases. This example turns a churn score into a governed save decision that reasons about why this customer, and respects margin and network reality before it acts.
Proves these Lumina features
Telecom churn runs around 21.5% globally and higher in B2B. Acquisition costs roughly five times retention, so every avoidable departure is expensive twice.
Models can score who will leave with high accuracy. They cannot tell you why this customer, or coordinate a save that protects margin and reflects network reality. The strongest churn signals, like care-call volume and the actual quality of the cell a customer sits on, live in different systems than the CRM, so the save offer is made blind.
Traditional approaches were not built for the scale and complexity of modern operations.
A churn model returns a probability, not a reason. Without the why, the retention team makes a generic offer that may not address what is actually driving the customer away.
Offers go out without reasoning about the customer’s value or the cost of the incentive. You retain accounts at a loss and discount ones that would have stayed.
If a high-value customer is on a degraded cell, a price discount is the wrong save. But CRM-only tools cannot see the network reality behind the complaint.
Which save worked for which segment is institutional knowledge that lives in a retention manager’s head, not in any system that compounds.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Agents unify usage, billing, care, and network-quality data to explain why this specific account is at risk, in plain language, with the query behind every driver. The score becomes a reason you can act on.
Every calculation is SQL you can verify. No black box.
Account ACME-Corp (value: $14,200/mo, 240 lines) Churn probability: 0.81 Top drivers (ranked): 1. 6 care calls in 30 days re: dropped calls (+0.34) 2. 41% of lines on cell SITE-22B, which shows 12% drop rate (+0.29) 3. Contract renewal in 45 days (+0.11) → This is a network-quality risk, not a price risk
RISK FORMING: 17 high-value accounts share concentration on degraded cells (drop rate > 10%) Care-contact rate rising across the cohort (+40% MoM) → Network-driven churn cohort. Fixing SITE-22B may save more than any offer.
The Radar watches for the leading signature of churn across the whole base: a spike in care contacts, a degraded-cell concentration, a usage drop. It flags the account weeks before the cancel request.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Retention, finance, and network agents debate the save. The retention agent proposes an offer, the finance agent checks margin, the network agent asks whether the real fix is engineering, not pricing. The output is one governed recommendation with human veto.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Retention Agent: offer 15% loyalty credit to ACME-Corp Finance Agent: at $14,200/mo this stays margin-positive, but a credit does not fix the cause Network Agent: 41% of their lines are on degraded SITE-22B, schedule the fix that is already in the backlog → Recommendation: prioritize SITE-22B remediation + a service-credit gesture, not a permanent discount. Retention lead approves.
Lumina addresses the four strategic problems that hold operators back.
Reason about every at-risk account with full context, not just the top of a leaderboard, without growing the retention team.
Stop discounting accounts that would have stayed and stop losing ones a network fix would have saved. Margin-aware saves protect both retention and ARPU.
Cortex remembers which save tactics worked for which segment, so retention IP compounds instead of leaving with the manager who knew it.
Every churn driver and save recommendation is backed by a verifiable query. The action is a recommendation your team approves, never an automated change to a customer record.
Specialized AI agents that power this workflow.
Retention Strategist
Explains per-account churn drivers and proposes saves grounded in usage, billing, and care signals.
Finance Reasoner
Tests every proposed save against customer value and incentive cost before it is recommended.
Network Quality Analyst
Connects churn risk to the real network experience on the cells a customer depends on.
This is just one of many use cases
Churn prediction & prevention is one example of how Lumina reasons on operational data. Across Telecom, every domain has use cases where AI agents can add value.
Not ready to commit? Stay up to date as we release new capabilities and industry-specific agents.
View all Telecom use cases