Most Customer Success (CS) teams are operating with incomplete information, focusing on direct customer feedback while missing critical signals happening elsewhere within the organization. This blind spot, according to industry observers, is a primary driver of unexpected churn, even among accounts reporting positive experiences.
The problem isn’t a failure of established CS practices like Executive Business Reviews (EBRs) or health scoring, but rather a reliance on them as the sole source of truth. These methods capture what customers are willing to share directly, overlooking conversations and activities occurring in channels the CS team doesn’t routinely monitor.
One recent example involved two enterprise accounts churning simultaneously, both of which appeared healthy according to standard metrics. One account even reported a Net Promoter Score (NPS) of 72. A deeper investigation revealed that the churn wasn’t due to a CS execution failure, but a lack of visibility into crucial signals outside the team’s typical purview.
The core issue is that the real conversation about a product often happens in places a CS team won’t see – Slack channels, procurement reviews, or one-on-one meetings between a customer’s champion and a new manager. Treating the EBR as the only channel for understanding customer sentiment is a dangerous oversimplification.
Beyond the EBR: Uncovering Hidden Signals
EBRs are valuable for building relationships and surfacing issues customers are willing to discuss. However, they are structurally limited to information customers choose to disclose in a formal setting. The most predictive signals often lie outside of these direct interactions.
Consider the case of an account where the engineering team filed 23 support tickets over four months regarding API latency. These weren’t critical outages, but small, specific technical complaints that were individually resolved. Because they didn’t escalate to a critical level, the CS team remained unaware of the growing frustration. Chronologically, however, the pattern clearly indicated a loss of patience.
In another instance, three of five key users at a customer organization updated their LinkedIn profiles within a two-week period, with one beginning to post about a competitor’s product. Simultaneously, the customer’s champion experienced a quiet demotion – a title change from “Head of” to “Senior Manager” – which went unnoticed because the team was focused on product usage dashboards, not organizational charts.
These types of signals, often overlooked, are readily available through publicly accessible sources. Failing to track them isn’t just an oversight; it’s a lack of respect for the customer, who often assumes their vendor is aware of these changes.
The Limitations of Health Scores
Health scores, typically based on NPS, login frequency, support ticket counts, and feature adoption, are not inherently flawed, but they are often treated as a complete picture. These are lagging indicators; by the time login frequency drops, the decision to explore alternatives may already be underway.
One analysis found that support ticket velocity – specifically, the rate of increase in non-critical tickets over a 90-day period – predicted churn with roughly twice the accuracy of a composite health score. The signals that truly predict churn aren’t typically those tracked by standard CS platforms.
A Tiered Signal Coverage Model
High-performing teams don’t abandon existing processes; they augment them with a signal layer. This model breaks down into three tiers:
Tier 1: Support Ticket Patterns. Focus on velocity, sentiment trends, and whether the same team consistently files tickets. A steady stream of resolved tickets from a single engineering team can be a more significant warning sign than a single critical escalation.
Tier 2: People Changes. Track champion turnover, reorganizations, title changes, and new executives with a history at competitor organizations. The individual who initially purchased the product is often different from the one responsible for renewal.
Tier 3: Competitive Exposure. Monitor whether the customer is being actively pitched by competitors, attending competitor events, or engaging with competitor content online.
The biggest challenge isn’t identifying what to track, but the fact that these signals reside in multiple systems, and no single individual is responsible for consolidating them. CSMs see Zendesk, Sales Engineers see Jira, and Account Executives see Salesforce. The complete picture requires manual assembly – a process that doesn’t scale.
From Manual Tracking to Automation
Some teams have successfully implemented a manual version of this approach, with CSMs logging signals from six different sources each week. However, This represents only sustainable for a small number of accounts – around 25. For larger portfolios, automation is essential.
Automated solutions can deliver alerts when changes occur, eliminating the need for constant dashboard monitoring or weekly rituals. The goal isn’t simply detection, but actionable insight. An executive sponsor who hasn’t logged in for 90 days requires a different intervention than an account with a competitor Proof of Concept (POC) in their Salesforce sandbox. The signal informs the response.
One platform, Renewal Fix, aims to address this challenge by automatically pulling signals from support tickets, call recordings, CRM data, and engineering channels, stitching them into a single account view, and flagging potential risks before they impact renewals. The platform offers a free executive brief, providing a snapshot of a company’s blind spots based on its existing data sources.
the message is clear: while green accounts may appear safe, they may be quieter than you realize. Proactive monitoring of a wider range of signals is crucial for preventing unexpected churn and building lasting customer relationships.
