10 Best AI Customer Health Score Software Tools for 2026

Customer health score software helps Customer Success teams combine a multitude of signals into a clear view of account health.

The main challenge is turning all of that scattered information into a score that is reliable enough to prioritize risk and opportunity. AI can improve this by spotting patterns in historical outcomes and explaining why an account’s health is changing.

In this guide, we compare 10 of the best AI customer health score software tools for 2026, looking at how each approaches the most important aspects of health scoring.

Key takeaways

• Gainsight and Hook lean into AI and machine learning for predictive health and renewal risk.
• Planhat combines configurable health models with AI-generated qualitative signals such as sentiment and conversation intelligence.
• Velaris takes a more agent-led approach, with Risk Watchdog continuously monitoring for churn risk and Growth Scout identifying expansion opportunities across the portfolio.
• ChurnZero and Vitally connect health changes directly to broader Customer Success workflows and automation.
• Odie’s Unison and Customerscore.io use AI models to detect churn risk and explain the factors driving each account’s score.
• The best tool depends on whether you need prediction, scoring flexibility, qualitative signals, explainability, or automated action.

1. Gainsight

Overview

Gainsight combines its broader Customer Success scorecard infrastructure with Staircase AI Health Score, an AI-generated account health score designed to capture both quantitative and qualitative customer signals.

Staircase AI scores accounts from 0 to 100 using four core components: sentiment, engagement, open items, and response time. Teams can also incorporate product usage and financial data, configure scoring differently by customer tier, and use historical company data to help optimize how different factors are weighted.

Best for

Large enterprises with mature Customer Success operations that want AI-driven health intelligence alongside broader capabilities such as playbooks, Success Plans, forecasting, reporting, and Customer 360.

What it does well

• Staircase AI Health Score: Provides an AI-generated account health score based on customer interactions, relationship signals, and account activity.
• AI sentiment analysis: Analyzes customer communications to identify changes in tone, engagement, and relationship health.
• Flexible health inputs: Product usage, financial data, and other account metrics can supplement the AI-generated score.
• Tier-specific scoring: Teams can configure different scoring windows and approaches for enterprise, mid-market, and SMB customers.
• Connected Customer Success workflows: Health data feeds into Gainsight’s wider playbooks, Success Plans, forecasting, reporting, and Customer 360 workflows.

Gainsight is particularly useful for organizations that want health scoring to sit inside a broader Customer Success operating model rather than function as a standalone metric.

Limitations and considerations

Gainsight is a broad enterprise platform, so implementing and maintaining it typically requires more configuration and administration than a lightweight health-scoring tool.

It also offers multiple approaches to health across Gainsight CS and Staircase AI, so buyers should understand which scoring model, data inputs, and AI capabilities are included in the package they are evaluating.

Pricing

Gainsight uses custom pricing. Its Customer Success Platform is available through Essentials and Enterprise packages, with Health Scorecards included as part of the broader CS offering.

Teams need to request a quote based on their required users, customer volume, configuration, and product capabilities.

2. Velaris

Overview

Velaris is an AI-native Customer Success Platform that is well-rated on G2, and has health scoring at the core of the product.

Its Risk Watchdog continuously monitors customer activity for signs of churn risk, while Growth Scout looks for signals that an account may be ready to expand.

Rather than relying on CSMs to regularly check health scores themselves, these agents monitor signals across product usage, customer conversations, support activity, sentiment, engagement, commercial data, and other account context in the background.

Teams can also configure different scoring logic for different lifecycle stages and customer segments, while monitoring health at organization, account, and contact level. CSM Pulse adds the human perspective alongside AI-generated health intelligence.

Best for

Mid-market and enterprise B2B SaaS teams that want customer health to combine structured account data with qualitative signals from calls, emails, and other customer interactions.

What it does well

• AI Agents: Risk Watchdog monitors various customer signals to identify emerging churn risk, and Growth Scout looks for expansion indicators that an account may be ready to grow.
• AI Pulse: Analyzes customer conversations for sentiment, tone, and risk, then feeds those signals into the wider health model.
• Lifecycle-specific scoring: Teams can use different metrics, weights, and thresholds for different lifecycle stages or customer segments.
• Multi-level health: Health can be tracked at organization, account, and contact level rather than relying on one account-wide score.
• Human + AI health: CSM Pulse lets Customer Success Managers add their own judgment alongside AI-generated signals.
• Action from score changes: Alerts, workflows, Copilot, and AI agents can help teams investigate health changes and trigger the appropriate follow-up.

Velaris also adds an explainability layer through CallSense and AI Pulse Reason, which can connect an AI health or sentiment assessment back to the customer conversation that influenced it.

Limitations and considerations

Velaris is a full Customer Success Platform rather than a standalone health-score calculator. It makes the most sense for teams that also want customer data, account management, workflows, reporting, Copilot, and automation in the same system.

For companies looking only for a lightweight predictive score without broader Customer Success functionality, a more specialized tool may be sufficient.

Pricing

Velaris uses custom annual pricing based on factors such as customer volume, users, integrations, AI usage, and the capabilities required.

3. ChurnZero

Overview

ChurnZero uses ChurnScores to help teams assess customer health using a mix of quantitative and qualitative signals.

Teams can choose which factors contribute to the score and assign their own weights, including product usage, engagement, customer behavior, survey responses, known risks, and other account data. Different scoring models can also be used across customer segments or lifecycle stages.

ChurnZero’s AI capabilities sit around this core scoring model by helping teams interpret customer signals, detect changes in relationships or account conditions, and decide what action to take next.

Best for

Mid-market B2B SaaS teams that want configurable health scoring closely connected to proactive Customer Success workflows and automation.

What it does well

• Configurable ChurnScores: Teams can choose and weight the metrics that best represent customer health for their business.
• Segment-specific scoring: Different health models can be applied based on customer segment, lifecycle stage, or engagement model.
• Broad health inputs: Product usage, engagement, surveys, risks, concerns, and other qualitative and quantitative data can contribute to the score.
• AI-powered signal detection: AI agents can analyze relationship changes, account history, and other customer signals to surface emerging risk.
• Action from health changes: Plays, alerts, and AI agents can trigger follow-up when customer conditions change, helping teams move from detection to intervention.

ChurnZero is particularly useful for teams that want health scoring to sit inside a broader system for monitoring customers and responding when risk appears.

Limitations and considerations

The core ChurnScore is still a configurable health model rather than a machine-learning model that independently determines which factors should carry the most weight.

Teams specifically looking for predictive scoring trained on historical outcomes may prefer tools such as Hook, Odie’s Unison, or Customerscore.io.

Pricing

ChurnZero uses custom pricing for its Customer Success Platform.

Its Agentic Essentials AI package is sold as a separate annual subscription for Professional and Enterprise customers, so teams should confirm which AI capabilities are included in their chosen package.

4. Planhat

Overview

Planhat’s Health Lab lets teams build configurable customer health models using product usage, support activity, engagement, sentiment, and other account data.

Its AI approach is relatively transparent. Rather than replacing the scoring model with a black-box prediction, Planhat keeps the underlying logic rule-based while allowing teams to add LLM-enriched health evaluations and AI-generated sentiment from calls and emails as additional inputs.

Best for

Mid-market and enterprise teams that want sophisticated, segment-specific health scoring while keeping control over how the score is calculated.

What it does well

• Flexible health models: Teams can use different metrics, weights, and thresholds by customer segment, portfolio, or product line.
• LLM-enriched health: AI can add a broader qualitative assessment alongside structured health factors.
• Conversation sentiment: Calls and emails can be analyzed for sentiment and incorporated into the health model.
• Transparent scoring: Teams retain visibility into which factors influence the score rather than relying entirely on an opaque AI model.
• Automated action: Changes in health can trigger alerts, workflows, and other follow-up actions inside Planhat.

Planhat is particularly useful for teams that want AI to enrich their health methodology without giving up control over the scoring framework itself.

Limitations and considerations

The flexibility comes with more setup and maintenance. Teams still need to decide which metrics matter, how they should be weighted, and how scoring should vary across different customer groups.

For companies looking for a model that automatically learns the strongest churn predictors from historical data, a more predictive tool may be a better fit.

Pricing

Planhat uses custom pricing. Its broader platform includes AI, automation, data, and reporting capabilities, with more advanced AI functionality available separately depending on the package.

5. Vitally

Overview

Vitally provides a flexible no-code health-scoring engine that lets teams build health models from multiple components, define conditions, assign weights, and vary the scoring logic by lifecycle stage, segment, plan, or team.

Health is calculated on a 0–10 scale, with the overall score built from weighted component scores. Vitally recalculates health scores hourly, helping teams keep account status relatively current as customer data changes.

AI-generated insights and other customer intelligence can also be incorporated as inputs, giving teams a way to combine structured account data with qualitative signals.

Best for

B2B SaaS teams that want highly configurable no-code health scoring inside a modern Customer Success workspace.

What it does well

• Dynamic multi-factor scoring: Teams can combine several health components into one weighted account score.
• Segment and lifecycle-specific models: Different scoring logic can be applied depending on customer stage, segment, plan, or team.
• No-code configuration: Thresholds, conditions, and weights can be adjusted without engineering support.
• AI-enriched health inputs: AI-generated insights and qualitative customer intelligence can contribute to the wider health model.
• Action from score changes: Health changes can trigger automations, tasks, and alerts so teams can respond when customer conditions shift.

Vitally is particularly useful for teams that want detailed control over how health is calculated without needing to build the scoring logic in code.

Limitations and considerations

Vitally’s core health model is primarily configured by the team rather than being a predictive machine-learning model that automatically discovers which factors are most strongly associated with churn or renewal.

That gives teams greater control, but it also means they need to decide which inputs matter and how those inputs should be weighted.

Pricing

Vitally uses custom pricing across its Tech-Touch, Hybrid-Touch, and High-Touch packages.

The exact price depends on the package and capabilities required, so teams need to request a quote.

6. Hook

Overview

Hook takes one of the more predictive approaches to customer health scoring in this list.

Its core metric, Engagement Level, estimates the likelihood that an account will renew rather than simply measuring activity or engagement.

Hook uses machine learning trained on historical customer data to calculate renewal likelihood at the account level, with models reviewed after launch and refreshed quarterly.

Its Echo capability adds qualitative context by analyzing emails, meetings, support conversations, and other customer interactions to surface additional risk and expansion signals.

Best for

B2B SaaS companies that want machine-learned renewal and churn prediction instead of manually defining every health-score component and weight.

What it does well

• Machine-learned renewal likelihood: Uses historical customer outcomes to estimate how likely each account is to renew.
• Company-specific models: Models are trained on the customer’s own data rather than relying only on generic scoring logic.
• Account-level Engagement Levels: Gives teams a simple view of renewal likelihood across the portfolio.
• Echo AI: Analyzes qualitative customer interactions for risk, sentiment, and expansion signals that may not appear in structured data.
• Recommended actions: Hook can surface signals and next-best actions to help teams respond when risk or opportunity appears.

Hook reports roughly 93% churn-prediction accuracy in internal testing, although its documentation notes that performance varies depending on the customer and the quality of the underlying dataset.

Limitations and considerations

Hook is more opinionated and predictive than platforms built around fully configurable health scorecards.

That makes it a strong fit for teams that want the model to learn from historical outcomes, but less suitable for organizations that want complete control over every metric, weight, and threshold in the scoring methodology.

Pricing

Hook uses quote-based pricing. Teams need to contact the company for a custom price based on their requirements.

7. Odie (formerly Totango) / Unison

Overview

Odie, formerly Totango, combines its Customer Success platform with Unison, an AI layer designed to analyze customer health, churn risk, and relationship signals.

Unison’s standard AI health model evaluates four core areas: sentiment, engagement, relationship strength, and Voice of the Customer.

It analyzes interactions across emails, meetings, calls, support tickets, and other engagement data, then provides an overall health assessment alongside the factors contributing to it.

Enterprise customers can also use custom AI models trained on their own historical customer data to identify patterns associated with outcomes such as renewal and churn.

Best for

Enterprise Customer Success teams that want AI-driven health and churn intelligence, including predictive models tailored to their own historical customer outcomes.

What it does well

• AI health scoring: Unison combines sentiment, engagement, relationship strength, and Voice of the Customer into an AI-generated health assessment.
• Custom predictive models: Enterprise teams can train models using their own historical data to better reflect how churn and renewal happen in their business.
• Sentiment and engagement intelligence: Customer conversations and interactions are analyzed alongside structured account activity.
• Relationship and VoC signals: Health incorporates qualitative indicators that can be difficult to capture in traditional weighted scorecards.
• Revenue risk visibility: Teams can connect health trends and predicted risk to the revenue associated with each account.

Odie is particularly useful for organizations that want AI to play a larger role in determining account health rather than relying entirely on manually configured weights and thresholds.

Limitations and considerations

Odie is transitioning from the former Totango and Catalyst product family to its newer Customer Knowledge Engine and Customer Operating System positioning.

Teams evaluating the platform should confirm which Unison health-scoring capabilities, predictive models, and legacy Totango functionality are included in the specific package they are considering.

Pricing

Odie’s Customer Knowledge Engine starts at $500 per month, while its Customer Operating System starts at $1,500 per month and includes predictive AI models for renewal risk.

Teams should confirm which package is required for the Unison health models and any custom predictive scoring capabilities.

8. Customerscore.io

Overview

Customerscore.io is one of the more specialized tools in this list because it focuses specifically on AI-driven health and churn scoring.

Instead of asking teams to manually define and weight every health metric, its AI Health Scoring evaluates each customer individually and generates a daily 1–5 churn-risk score.

It also explains which factors are helping or hurting the account and provides a natural-language summary of why the score looks the way it does.

Best for

B2B SaaS teams with strong product-usage and billing data that want predictive health scoring without maintaining a complex rules-based model.

What it does well

• AI-generated daily scoring: Produces a fresh churn-risk score for each account without requiring teams to manually maintain score weights.
• Account-specific health drivers: Highlights the factors that are improving or worsening each customer’s health.
• Broad data inputs: Can use product, billing, CRM, support, and other customer data as part of the analysis.
• Explainable scoring: Provides a natural-language explanation alongside the score so CSMs can understand why an account is considered healthy or at risk.
• Automated action: Health and churn signals can feed into playbooks for retention and expansion.

Customerscore.io is particularly useful for teams that want AI to do more of the work of identifying which signals actually matter for each account, rather than relying on a fixed health formula.

Limitations and considerations

The quality of the model depends heavily on the amount and quality of historical data available.

Customerscore.io says its AI performs best with 100 or more active customers and roughly 40 or more historical churn events, although it can operate with less data.

It is also a more specialized and less established platform than broader enterprise Customer Success products such as Gainsight or ChurnZero.

Pricing

Customerscore.io uses custom pricing based primarily on ARR rather than the number of seats.

Teams need to request a quote based on the size of their customer base and usage requirements.

9. Custify

Overview

Custify combines configurable customer health scores with AI-assisted analysis and automation.

Teams can bring together product usage, adoption, account data, support activity, surveys, and other customer signals, then define the KPIs and custom metrics that contribute to health.

Custify’s AI layer can analyze changes in customer health and sentiment, while AI Customer Health Agents and automated Signals help teams decide when action is needed.

Best for

Small and mid-market SaaS Customer Success teams that want health scoring, automation, and AI capabilities in a relatively accessible Customer Success Platform.

What it does well

• Configurable health scores: Teams can define the KPIs and metrics that matter most for their own customer base.
• AI Customer Health Agents: AI can help monitor account health, interpret changes, and surface where attention may be needed.
• Activity and trend monitoring: Tracks changes in usage, engagement, and other customer signals over time.
• Automated Signals: Important shifts in health or behavior can be surfaced automatically instead of relying on manual review.
• Playbooks and follow-up: Health changes can trigger playbooks and other actions so teams can respond more consistently.

Custify is particularly useful for teams that want a configurable health model but also want AI to help interpret what is changing and move teams toward action.

Limitations and considerations

Custify’s core health scoring still depends on predefined metrics and rules rather than a fully predictive machine-learning model that independently determines the strongest churn predictors.

Its AI capabilities are therefore more focused on enriching, interpreting, and acting on health data than replacing the underlying scoring methodology.

Pricing

Custify uses custom pricing and does not publish standard package prices.

Onboarding is included without a separate setup fee, but teams need to request a quote based on their requirements.

10. ClientSuccess

Overview

ClientSuccess combines its configurable SuccessScore health framework with SmartCS AI.

SuccessScore lets teams build weighted health profiles using signals such as product usage, engagement, CSM Pulse, and custom fields. Different profiles can be created for different customer types, giving teams flexibility over how health is measured across the portfolio.

SmartCS AI adds a qualitative layer by analyzing NPS, Pulse, engagement notes, and customer segments to give CSMs more context around changes in account health.

Best for

Small and mid-market Customer Success teams that want straightforward configurable health scoring combined with playbooks, renewals, and AI assistance.

What it does well

• Weighted SuccessScore profiles: Teams can define which metrics contribute to health and how much influence each one has.
• Different models by customer type: Separate scoring profiles can be created for different segments, plans, or customer groups.
• Flexible health inputs: Product usage, engagement, CSM Pulse, and custom fields can all contribute to the overall score.
• SmartCS AI: AI can summarize customer context and analyze patterns across NPS, engagement notes, Pulse, and segments.
• Automated follow-up: Changes in SuccessScore can trigger automations and playbooks so teams can respond more consistently.

ClientSuccess is particularly useful for teams that want a clear, configurable health model without introducing too much complexity.

Limitations and considerations

AI supports the intelligence around the health model rather than independently generating the SuccessScore itself.

Teams specifically looking for predictive machine-learning-based scoring that learns directly from historical churn or renewal outcomes may prefer tools such as Hook, Odie’s Unison, Gainsight Staircase AI, or Customerscore.io.

Pricing

ClientSuccess uses custom pricing across its Startup, Growth, and Enterprise packages.

Health scoring is included across all three plans, but teams need to request a quote for exact pricing.

Conclusion

The best AI customer health score software depends on how much control you want over the model and what you expect AI to do.

For organizations that want machine-learning-based predictions, Gainsight Staircase AI, Hook, Odie/Unison, and Customerscore.io lean more heavily toward models that infer risk from customer data and historical outcomes.

For B2B Customer Success teams, Velaris is a highly rated platform on G2 and a strong option because it combines configurable health models with AI-generated signals such as sentiment and conversation intelligence.

Book a demo with Velaris to see how AI-enriched customer health can help your team spot risk earlier and act on changes across your portfolio.