Data as of Aug 25, 2026 · Based on 318 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
To proactively reduce churn, focus on tracking indicators like declining product usage, negative support experiences, and changes in key stakeholders. Tools like Customer Success Platforms (such as 
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Named in 75% of answers
ChurnZero is best for real-time engagement monitoring and automated alerting. It is a primary choice for consolidating customer data to manage health scores and trigger timely, proactive intervention for at-risk accounts.
Named in 67% of answers
Named in 37% of answers
Named in 35% of answers
Source | Citation share |
|---|---|
Proactive At-Risk Customer Strategy - ChurnZerohttps://churnzero.com/blog/guide-to-proactive-at-risk-customer-strategy/ | 6.0% |
 Understanding churn risk: How to identify and retain at-risk customershttps://www.moxo.com/blog/churn-risk-identify-at-risk-customers | 4.3% |
 Churn Risk and Mitigation Strategies for CS Teamshttps://www.velaris.io/articles/churn-risk | 4.1% |
 How To Reduce Churn with Proactive Customer Success Strategieshttps://nalpeiron.com/blog/how-proactive-customer-success-can-curb-churn | 2.8% |
The Essential Guide to Customer Churn | Gainsight Softwarehttps://www.gainsight.com/essential-guide/churn/ | 2.3% |
 How to identify churn risk (and save at-risk customers)https://www.dock.us/library/at-risk-customers | 2.1% |
Predicting and Preventing Churn with AI | Gainsight Softwarehttps://www.gainsight.com/blog/predicting-and-preventing-churn-with-ai/ | 2.1% |
 What's your go-to tool for proactively identifying customer risk?https://www.reddit.com/r/CustomerSuccess/comments/1iyxqto/whats_your_goto_tool_for_proactively_identifying/ | 1.9% |
 6 Ways to Identify At-Risk Key Accounts to Prevent Churn - Kaptahttps://kapta.com/resources/key-account-management-blog/6-ways-to-identify-at-risk-key-accounts-to-prevent-churn | 1.8% |
 The 8-step customer success process for reducing churn - Gonghttps://www.gong.io/blog/customer-success-process | 1.8% |
Proactively identifying at-risk accounts is the cornerstone of effective Customer Success. Instead of waiting for a cancellation request, CSMs can spot early warning signs by monitoring key health metrics and behavioral shifts. Early Warning Signs & Indicators - **Product Usage Decline:** A sudden or steady drop in…
Proactively identifying at-risk accounts is the cornerstone of effective Customer Success. Instead of waiting for a cancellation request, CSMs can spot early warning signs by monitoring key health metrics and behavioral shifts.
Early Warning Signs & Indicators
Essential Tools for Churn Prediction
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I can help you build a custom customer health score framework.
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A Customer Success Manager (CSM) should treat churn risk as something to **predict from changes in customer behavior**, rather than waiting for a customer to say they’re unhappy or for a renewal to approach. ### 1. Build a customer health score Create a health score that combines several signals rather than relying on…
A Customer Success Manager (CSM) should treat churn risk as something to predict from changes in customer behavior, rather than waiting for a customer to say they’re unhappy or for a renewal to approach.
Create a health score that combines several signals rather than relying on a single metric. Common inputs include:
| Signal | What to watch for |
|---|---|
| Product usage | Declining logins, fewer active users, reduced feature adoption |
| Adoption | Customers stop using key/core features |
| Engagement | Fewer meetings, unanswered emails, declining stakeholder participation |
| Support | Repeated issues, unresolved escalations, worsening response patterns |
| Sentiment | Falling NPS/CSAT, negative feedback, frustrated comments |
| Business outcomes | Customer isn't achieving the outcomes/ROI they purchased for |
| Relationship | Champion leaves, executive sponsor disengages, stakeholder changes |
| Commercial | Budget pressure, downsizing, delayed renewal discussions |
| Renewal timing | Risk signals appearing 3–6 months before renewal |
Health scores are most useful when they're tailored to your customer segment and based on signals that actually correlate with renewal at your company.
A customer with a 70/100 health score isn't necessarily healthy. What's more informative is:
70 → 62 → 51 over three months
That deterioration should trigger investigation.
Similarly, declining usage isn't automatically churn risk. A customer might be using the product less because they've successfully automated a process. The CSM needs to combine quantitative signals with customer context.
An interesting example: Gainsight's analysis found that breadth of feature usage and percentage of daily active users were strong renewal indicators, while simply looking at the number of support tickets could be misleading.
Instead of asking CSMs to manually inspect hundreds of accounts, establish triggers such as:
The important part is connecting the alert to an action/playbook, not simply putting another red indicator on a dashboard.
One of the most established dedicated Customer Success platforms. It combines health scores, product usage, support history, renewal information, stakeholder data and engagement signals. Its current platform also uses AI to surface risk and recommend workflows.
Best for: Enterprise CS organizations with sophisticated health scoring, playbooks and renewal processes.
Particularly focused on identifying and acting on churn risk. Its ChurnScores can combine product usage, engagement, support history, satisfaction and account characteristics, while its predictive analytics looks for patterns associated with churn.
Best for: Teams wanting dedicated churn-risk monitoring and automated customer-success workflows.
Useful when CRM data is already central to the organization. Salesforce's Customer Health Score incorporates adoption, support and relationship signals, while its Revenue Health Score can identify revenue and churn risks.
Best for: Organizations already heavily invested in Salesforce.
HubSpot can be used to build customer-success dashboards around health, product adoption, engagement, support, NPS/CSAT and renewal metrics.
Best for: SMB/mid-market teams that want CS signals integrated with their CRM and broader customer platform.
A sophisticated CS platform won't help much if it has poor data.
A useful architecture looks something like:
Product analytics → usage/adoption
CRM → contacts, opportunities, account information
Support system → tickets/escalations
Billing/subscription system → ARR, contract, renewal
Survey tool → NPS/CSAT/sentiment
CS platform → combines these into health + risk + actions
For product-led businesses, product analytics can be especially valuable. For example, Gainsight PX's account-level tooling can examine feature usage, active users, stickiness and health over time.
I'd structure the CSM process as:
Detect → Diagnose → Act → Measure
The biggest mistake is treating a health score as the answer. It's really a prioritization mechanism. The goal is to identify the accounts where the CSM has enough time and information to intervene before the customer reaches the renewal decision.
A good CSM should treat churn risk as a **pattern of leading indicators**, not as a single “red/yellow/green” score. ## What to watch for - **Product adoption declines:** fewer active users, declining usage frequency, reduced feature adoption, or failure to reach key usage milestones.
A good CSM should treat churn risk as a pattern of leading indicators, not as a single “red/yellow/green” score.
Modern CS platforms can also analyze communications for signals such as stakeholder changes, adoption struggles, technical blockers, commercial friction, and strategic misalignment.
Rather than weighting everything equally, create a score tailored to your business. For example:
| Signal | Example weight |
|---|---|
| Product adoption | 30% |
| Customer engagement | 20% |
| Support experience | 15% |
| Business outcomes / ROI | 15% |
| Sentiment | 10% |
| Relationship depth | 5% |
| Renewal/commercial factors | 5% |
More importantly, track change over time. An account going from 85 → 65 may deserve more attention than one sitting steadily at 60.
ChurnZero similarly recommends combining quantitative and qualitative inputs—including product usage, engagement, support history, satisfaction, account attributes, and risk factors—rather than relying on one metric.
The best setup isn't simply:
Health = 42 → CSM gets an email It's:
Usage fell 35% + champion left + two critical tickets are open + renewal in 90 days → High risk → assign save playbook → CSM contacts new stakeholder → Support escalates tickets → executive sponsor reviews account → reassess in 14 days. That distinction matters. Detection without an intervention workflow just creates another dashboard.
A practical CSM operating rhythm is to review newly deteriorating accounts weekly, prioritize by ARR × churn probability × time to renewal, and require a documented “why is this account at risk?” and “what are we doing about it?” for every high-risk account.
As a Customer Success Manager (CSM), catching churn before it happens is all about tracking the right signals and acting on them early. Here is how you can proactively spot accounts at risk and the tools that make it…
As a Customer Success Manager (CSM), catching churn before it happens is all about tracking the right signals and acting on them early. Here is how you can proactively spot accounts at risk and the tools that make it easier.
How to Proactively Identify Accounts at Risk
Tools That Help Identify Churn Risk
Would you like me to help you build a customer health score template or dive deeper into how to run a save play once an account is flagged?
Proactively identifying churning accounts requires tracking a mix of behavioral changes, engagement drops, and relationship signals before the customer officially raises a red flag. Key Indicators to Proactively Identify At-Risk Accounts - **Product Usage Decline** : A sudden or steady drop in daily/monthly active…
Proactively identifying churning accounts requires tracking a mix of behavioral changes, engagement drops, and relationship signals before the customer officially raises a red flag.
Key Indicators to Proactively Identify At-Risk Accounts
Tools That Help Identify Churn Risk
Would you like to explore how to build a custom customer health score or set up early-warning email alerts for these specific risk indicators?
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A good Customer Success Manager (CSM) shouldn't wait for a customer to say, “We're thinking about leaving.” The goal is to identify **leading indicators of churn** early enough that there's still time to change the outcome. ## 1. Build a customer health score Create a score that combines several signals rather than…
A good Customer Success Manager (CSM) shouldn't wait for a customer to say, “We're thinking about leaving.” The goal is to identify leading indicators of churn early enough that there's still time to change the outcome.
Create a score that combines several signals rather than relying on a CSM's gut feeling. Common inputs include:
Modern health-score systems can combine usage, support, satisfaction, engagement, and other account data into a single risk indicator.
The important part is trend, not just the absolute score. An account going from 85 → 72 → 58 may be more concerning than an account that has consistently been at 65.
The strongest risk detection usually comes from multiple weak signals occurring together.
For example:
Product usage ↓ + champion leaves + support escalations ↑ + renewal in 90 days = high-priority risk Compare that with:
Product usage ↓ but executive engagement ↑ and success milestones are being hit = probably not churn risk This is why a simple "number of logins" metric isn't enough. Current CS platforms increasingly combine behavioral, relationship, sentiment, and commercial signals.
Don't make CSMs manually inspect hundreds of accounts.
Useful triggers include:
For example, ChurnZero supports real-time alerts for changes in usage, engagement, health scores, and milestones.
Not every red account deserves the same immediate response.
A useful prioritization model is:
Risk × ARR × renewal proximity × strategic importance
So a $500K account with a renewal in 60 days gets more attention than a $5K account renewing next year—even if both have the same health score.
The biggest mistake is creating a dashboard that says "Account is at risk" without telling the CSM what to do.
For each risk type, define an action:
| Risk signal | CSM action |
|---|---|
| Adoption declining | Analyze usage → identify missing use case → training/adoption plan |
| Support escalation | Executive escalation → recovery plan → regular updates |
| Champion departure | Map stakeholders → identify new champion |
| Negative sentiment | Executive check-in → understand root cause |
| Value not demonstrated | Revisit success plan → quantify ROI |
| Renewal approaching + low health | Create mutual renewal/save plan |
| Competitor mentioned | Understand evaluation → reinforce differentiated value |
Gainsight, for example, supports automated risk workflows and playbooks around health-score changes.
gainsight.com — comprehensive CS platform for health scoring, customer 360, product usage, support history, sentiment, renewals, risk detection, and automated workflows.
churnzero.com — particularly focused on health scoring, product engagement, alerts, and predictive churn analytics. Its Success Insights analyzes customer data for patterns associated with churn.
Other common pieces of the stack include:
In mature organizations, the CS platform becomes the system of action, while the CRM, product analytics, support system, and other tools supply the underlying signals. For example, one Gainsight customer built a scorecard using signals from Gong, Jira, Slack, usage, support sentiment, and CSM sentiment.
If you're building this from scratch, I'd start with just 5–7 signals:
Health = 35% adoption + 20% engagement + 15% support + 15% sentiment + 15% business outcomes
Then create Green / Yellow / Red thresholds and an automated alert whenever an account moves from Green → Yellow or Yellow → Red.
After 6–12 months, compare the signals against actual renewals and churn. Remove signals that don't predict anything and increase the weighting of the ones that do. That's how the health score evolves from a subjective dashboard into a genuinely predictive churn model.
| Risk signal | CSM action |
|---|---|
| Adoption declining | Analyze usage → identify missing use case → training/adoption plan |
| Support escalation | Executive escalation → recovery plan → regular updates |
| Champion departure | Map stakeholders → identify new champion |
| Negative sentiment | Executive check-in → understand root cause |
| Value not demonstrated | Revisit success plan → quantify ROI |
| Renewal approaching + low health | Create mutual renewal/save plan |
| Competitor mentioned | Understand evaluation → reinforce differentiated value |
Gainsight, for example, supports automated risk workflows and playbooks around health-score changes.
Gong
gong.io
Proactively identifying accounts at risk requires monitoring a blend of **quantitative product usage metrics** and **qualitative relationship signals** before a customer ever hints at…
Proactively identifying accounts at risk requires monitoring a blend of quantitative product usage metrics and qualitative relationship signals before a customer ever hints at leaving.
Proactive Identification Strategies
Tools That Help
If you'd like, I can:
Let me know what you'd like to dive into next.
A Customer Success Manager (CSM) can identify churn risk by building an **early-warning system** that combines customer behavior, sentiment, relationship signals, and business context. The key is to look for **leading indicators** (signals before churn happens), not just lagging indicators like a cancellation request.…
A Customer Success Manager (CSM) can identify churn risk by building an early-warning system that combines customer behavior, sentiment, relationship signals, and business context. The key is to look for leading indicators (signals before churn happens), not just lagging indicators like a cancellation request.
A drop in product engagement is one of the strongest warning signs.
Watch for:
Example:
An account purchased 100 seats but only 20 users are active, and usage has declined for three months → investigate before renewal.
Tools:
A health score combines multiple signals into a single risk indicator (for example: Green, Yellow, Red).
Common inputs:
A good health score is:
Platforms such as Gainsight, ChurnZero, and Totango provide configurable health scoring and risk monitoring.
Customers often reveal churn risk before usage declines.
Signals:
Sources:
Tools:
A customer can have strong usage but still churn if relationships weaken.
Risk signals:
Helpful practices:
Support data can reveal dissatisfaction.
Look for:
Integrations with systems like Zendesk and Salesforce can feed these signals into customer health models.
Renewal risk often appears months before the renewal date.
Warning signs:
Tools:
More mature CS teams use machine learning to identify patterns humans may miss.
Examples:
Some CS platforms offer predictive risk models in addition to traditional health scores.
| Need | Example tools |
|---|---|
| Customer health scoring | Gainsight, ChurnZero, Totango |
| CRM/account data | Salesforce, HubSpot |
| Product usage analytics | Amplitude, Pendo, Mixpanel |
| Surveys/sentiment | Qualtrics, SurveyMonkey |
| Support insights | Zendesk, Intercom |
| Call intelligence | Gong, Chorus |
| Reporting | Tableau, Power BI, Looker |
A strong churn-risk process usually follows this pattern:
Collect signals → Calculate health → Identify risk → Create a playbook → Measure intervention success
The best CSM teams do not just ask, “Which accounts are at risk?” They also ask, “Why are they at risk, and what action will change the outcome?”
Key Proactive Indicators for Churn Risk - **Engagement Drop-off** : A sudden decrease in active users, lower login frequencies, or a drop in core feature utilization (e.g., a steep decline in𝑊a c t i v e÷𝑊t o t a l…
Key Proactive Indicators for Churn Risk
Top Customer Success Tools for Churn Prevention
Would you like to explore how to build a custom health scoring model , or look into automated playbooks for recovering at-risk accounts?
A strong CSM doesn’t wait for a customer to say “we’re unhappy.” The goal is to detect **behavioral, relationship, and business signals early enough to intervene**. ### 1. Build a customer health score Create a health score that combines several signals rather than relying on one metric. Common inputs include:
A strong CSM doesn’t wait for a customer to say “we’re unhappy.” The goal is to detect behavioral, relationship, and business signals early enough to intervene.
Create a health score that combines several signals rather than relying on one metric. Common inputs include:
| Signal | Warning sign |
|---|---|
| Product usage | Logins, active users, or key-feature usage declining |
| Adoption | Customer isn't adopting features tied to their business outcome |
| Engagement | Meetings/calls/emails increasingly difficult to schedule |
| Support | More tickets, recurring problems, escalations, poor resolution experience |
| Sentiment | NPS/CSAT declining or negative sentiment in calls/emails |
| Stakeholders | Champion leaves, executive sponsor disengages, new decision-maker appears |
| Value/ROI | Customer can't articulate business results or hasn't achieved success milestones |
| Commercial | Budget pressure, delayed payments, downsizing, renewal approaching |
| Renewal behavior | No engagement with renewal planning or procurement starts unusually late |
Health scoring works best when it's weighted and segmented. For example, a 20-person SMB customer shouldn't necessarily be evaluated using the same adoption thresholds as a 5,000-person enterprise. Gainsight and ChurnZero both emphasize combining quantitative usage data with qualitative signals such as engagement, support, and sentiment.
A customer with a mediocre health score isn't necessarily at risk. A previously healthy customer whose usage suddenly falls 40% may be much more concerning.
Useful alerts include:
This is where automation becomes particularly valuable: platforms can alert a CSM when a meaningful signal changes rather than forcing them to manually inspect every account.
Don't let the dashboard override what the customer is actually telling you.
For example:
Usage: 🟢 High
Support: 🟢 Normal
NPS: 🟢 9
Champion: 🔴 Left company
Renewal: 🔴 60 days away
Executive engagement: 🔴 None
That account may look healthy based on product usage but actually deserves immediate attention.
Conversely, a customer with low usage may be perfectly healthy if they only need the product occasionally but consistently achieve their intended outcome.
The health score itself isn't the objective. The intervention is.
For each major risk, define a playbook:
Low adoption → identify the missing workflow → training/onboarding → measure adoption again.
Champion departure → map stakeholders → establish a new executive/user champion → revalidate value.
Negative sentiment → schedule executive conversation → understand root cause → create recovery plan.
ROI/value concern → quantify outcomes achieved → identify remaining business goals → create success plan.
Renewal risk → start renewal conversation early → identify blockers → involve appropriate internal resources.
Gainsight, for example, supports automated alerts and playbooks triggered by customer risk, while ChurnZero supports health-score-triggered alerts, plays, and journeys.
Customer Success platforms
Data sources that feed those platforms
For example, Vitally can ingest product events from Segment, Snowflake, Salesforce, or APIs and use those events in health scoring and automation.
I'd use a simple three-layer model:
1. Health: “Is this account healthy right now?”
2. Trend: “Is its health improving or deteriorating?”
3. Context: “Why is it changing, and what should I do?”
That is much more powerful than simply sorting accounts by a red/yellow/green score.
The best systems ultimately answer the CSM's real question: “Which accounts need my attention today, why, and what action should I take?”
