AI in Churn Reduction: What G2’s 2026 Expert Survey Found

Reducing customer churn has always been a priority for subscription businesses. For SaaS leaders, even a small uptick in churn can undercut expansion, inflate CAC, and destabilize revenue forecasts. Retention is no longer just a customer success KPI. It’s a board-level growth lever, and increasingly, AI in churn reduction is becoming central to that strategy.

As you plan for 2026, the conversation has shifted from reacting to cancellations to preventing them altogether. The real question isn’t whether you can calculate a churn score. It’s whether your team can see risk early enough, understand what’s driving it, and take confident action before revenue walks out the door. This is where AI in churn reduction moves from experimentation to operational necessity.

For decision makers evaluating AI-driven retention platforms, prediction and execution together are the differentiators. Can the system unify product usage, sentiment, billing signals, and relationship context into something your team actually trusts? Can it prioritize accounts, trigger the right playbooks, and create measurable impact — not just more dashboards?

To understand how prepared organizations really are, G2 surveyed four customer success and subscription management platforms, Custify, ChurnZero, Chargebee, and Velari, which are actively building AI-driven churn and retention capabilities. The insights reflect what these platforms are observing across their customer bases, what is working today, and what still limits churn reduction efforts.

The insights that follow are grounded in direct input from platforms building and deploying churn-focused AI in production environments today. This context helps explain not just what is possible with AI, but what is proving practical at scale.

Before diving into the findings, it’s important to understand who contributed insights to this survey and why their perspectives matter. Each participant represents a platform that works closely with customer-facing teams and observes churn patterns across hundreds of real-world accounts.

Meet the 4 innovators behind this research

To capture a well-rounded view of AI-driven churn reduction, the survey included four platforms serving subscription and customer success teams across SaaS and adjacent industries. Each brings a distinct perspective shaped by its product focus and customer base.

• Custify (G2 rating: 4.7/5) is focused on helping teams monitor customer health, understand sentiment, and automate retention workflows. Its AI capabilities emphasize contextual insights, conversational interfaces, and playbook-driven execution that support proactive customer engagement.
• ChurnZero (G2 rating: 4.7/5) is designed to help organizations reduce churn and drive expansion at scale. Its AI initiatives focus on autonomous agents that analyze engagement, product usage, relationship dynamics, and broader customer context to surface risk and expansion signals with recommended next best actions, helping teams act early and consistently.
• Chargebee (G2 rating: 4.4/5) supports recurring revenue businesses. Its approach to churn reduction leverages historical billing data, payment data, customer profiles, and usage behavior to identify risk, optimize renewal strategies, and improve retention outcomes tied closely to revenue operations.
• Velaris (G2 rating: 4.7/5) emphasizes explainable, actionable AI across customer portfolios. Its AI capabilities help teams interpret qualitative and quantitative signals, prioritize risk and opportunity, and embed churn prevention directly into everyday workflows.

What defines AI maturity in churn reduction platforms?

Over the past few years, AI in customer success has evolved from experimental dashboards to embedded intelligence layers inside core workflows. Today, it’s built directly into the tools teams use every day. This evolution reflects a broader shift in how organizations are investing in AI in churn reduction as a core growth strategy. Modern churn platforms bring together product usage, support tickets, customer sentiment, and billing data into one clear, contextual view of account health. Instead of sending static alerts, they help teams understand what’s happening, prioritize real risks, and suggest practical next steps.

As subscription businesses grow, teams need more than a churn prediction number. They need AI that helps them act — guiding playbooks, focusing resources where they matter most, and enabling earlier, more confident outreach. This broader market evolution provides important context for evaluating where vendors stand today.

Across the surveyed platforms, AI maturity for churn reduction is no longer experimental. Custify, ChurnZero, and Velaris describe their AI capabilities as advanced and well-developed, while Chargebee positions itself as moderately mature and actively expanding its AI footprint. Despite differences in maturity, a common theme emerges: churn AI has moved beyond simple scoring.

• Platforms such as Custify and ChurnZero offer combinations of predictive churn scoring, sentiment or intent analysis, automated retention workflows, and customer health scoring.
• Velaris extends this further with AI-powered ticket clustering, NPS and CSAT analysis, renewal forecasting, and portfolio-level churn analysis.
• Chargebee's churn signals are grounded in billing and payment behavior alongside product usage data, giving RevOps teams revenue-level visibility that complements traditional CS-focused analytics.

What stands out is not just the current feature set, but the direction of investment for 2026. All four platforms highlighted plans to improve model accuracy, expand into AI-driven expansion prediction, and strengthen action recommendations embedded directly within customer success workflows. The emphasis is shifting from simply identifying risk to enabling more consistent and timely retention execution across growing customer portfolios.

Which AI churn capabilities are gaining real adoption?

Adoption patterns offer a useful lens into where AI is delivering immediate value, especially under real-world constraints like limited time, noisy data, and large books of business. Customer success teams are being asked to manage larger portfolios, deliver faster time-to-value, and drive predictable renewals without proportional increases in headcount. At the same time, recurring revenue models are becoming more data-rich and more complex, making it harder to manually interpret churn signals across product usage, support, and billing systems. In this environment, AI is being evaluated on whether it improves day-to-day execution.

This shift is shaping which capabilities gain traction in the market. AI investments that reduce manual analysis, consolidate fragmented signals, and surface clear next steps are being adopted more quickly than standalone dashboards or passive churn scores.

When asked which AI features have seen the highest adoption over the past year, platforms consistently pointed to capabilities that reduce manual analysis and surface context quickly.

• Custify cited customer summaries and conversational interfaces that help teams understand account health without digging through raw data.
• ChurnZero pointed to its Engagement AI, which analyzes customer interactions across emails, meetings, support tickets, and surveys to surface sentiment, tone, and relationship dynamics.
• Velaris highlighted its AI Copilot, an interactive layer designed to help teams identify risk, understand drivers, and prioritize mitigation actions across an entire book of business.
• Chargebee noted strong adoption of predictive churn scoring tied to billing and usage behavior.

The pattern is clear: AI features that fit naturally into existing workflows and help teams move faster are adopted more readily than standalone dashboards or static scores.

What signals do CS platforms use to predict customer churn?

Customer churn has become more nuanced as customer journeys span product usage, onboarding milestones, support interactions, and commercial touchpoints. A single metric rarely captures the full health of an account. Engagement may appear stable while sentiment declines, or billing may remain consistent even as feature adoption drops. AI systems are designed to detect when multiple trends begin shifting simultaneously, helping teams interpret risk within a broader behavioral context.

Despite differences in product focus, the platforms reported strong alignment on the most reliable churn signals. While implementation varies by product and customer segment, there is strong agreement that churn risk emerges when engagement, adoption, and sentiment trends begin to align.

Product usage drops, feature adoption decline, onboarding failures, and negative sentiment consistently ranked among the strongest predictors. Support ticket surges and billing failures also emerged as meaningful signals, particularly for platforms with deeper financial or support data.

However, the survey responses emphasize that no single signal tells the full story. Churn is rarely triggered by one event. Instead, it emerges from patterns — declining engagement combined with sentiment shifts, stalled onboarding paired with unclear value realization, or healthy usage masking strategic disengagement.

• Velari and ChurnZero both stressed the importance of contextual signals, noting that customers can appear active while quietly disengaging at the relationship or stakeholder level.
• Chargebee reinforced this by pointing to healthy billing behavior combined with sustained usage as a strong indicator of retention, particularly when payment methods and engagement metrics remain stable.
• Custify emphasized contextual health monitoring that combines product usage, engagement patterns, and sentiment signals to identify early risk. Rather than relying on static thresholds, its approach focuses on tracking shifts in adoption depth and customer interaction trends over time.

While identifying churn signals is critical, prediction alone does not improve outcomes. The real impact emerges when these insights inform retention strategy. Understanding what drives risk naturally leads to a broader question: what behaviors and conditions consistently correlate with long-term renewal? The survey responses suggest that the inverse of churn signals is not merely higher usage, but clearer value realization and stronger customer ownership.

What truly drives long-term retention?

As churn prediction models mature, attention naturally shifts from identifying risk to reinforcing renewal drivers. Across the SaaS market, retention strategy is increasingly framed around value realization rather than raw engagement. Industry-wide, organizations are recognizing that sustainable retention is not secured through usage volume alone, but through customers achieving meaningful, measurable outcomes tied to their original objectives.

As portfolios scale and buying committees grow more complex, retention is becoming less about activity metrics and more about alignment — alignment between product usage, stakeholder expectations, and business impact. Vendors are therefore looking beyond telemetry to understand what signals long-term commitment, not just short-term engagement.

When asked which behaviors correlate most strongly with improved retention, the responses moved beyond telemetry. Instead of relying purely on usage metrics, platforms pointed to customer intent and value realization as stronger indicators of long-term retention. Customers who understand their goals and actively work toward measurable outcomes are consistently more likely to renew.

• ChurnZero emphasized customer ownership of value — customers who clearly define success, engage proactively with their vendor, and treat the relationship as a partnership rather than a transaction. These customers invest time, people, and data into achieving outcomes and consistently renew as a result.
• Custify echoed this perspective, highlighting frequent touchpoints, broad adoption across teams, and short time-to-value as key retention drivers.
• Velaris pointed to sustained depth of usage in core features tied directly to customer value.
• Chargebee emphasized stable billing behavior and consistent product usage as strong retention indicators, particularly when payment methods remain active, and engagement patterns do not fluctuate unexpectedly.

Across platforms, retention improves when customers understand why they are using a product and can see measurable outcomes.

What outcomes CS teams achieve with AI-driven churn management

Industry-wide, leaders are shifting conversations from “Can AI predict churn?” to “How much revenue protection and efficiency does it create?” Boards and executive teams now expect churn AI initiatives to show clear retention lift, faster onboarding cycles, and improved team leverage. Outcome measurement has become the true benchmark of AI maturity, separating experimental deployments from fully operationalized retention strategies.

All four platforms reported tangible improvements achieved by customers using AI-driven churn features. Velaris cited churn reductions in the range of 15% on average, along with faster time-to-value and improved operational efficiency for customer success teams. Chargebee reported churn reductions of up to 25% in specific high-performing implementations, particularly among subscription businesses with well-defined customer segments. These results were seen where teams had proactive retention workflows and acted quickly on risk signals. The company emphasized that outcomes depend heavily on how effectively model outputs are operationalized. These results illustrate that AI in churn reduction delivers the greatest impact when predictive insight is tightly coupled with execution.

Beyond churn reduction, platforms observed improvements in activation, expansion identification, and team efficiency. ChurnZero described how AI-driven signals enable customer success managers to focus on higher-value conversations, manage larger books of business, and deliver more consistent renewal and expansion outcomes. The operational benefit, standardizing best practices and extending coverage to long-tail customers, was highlighted as equally important as predictive accuracy.

The survey responses make one pattern clear: AI-driven churn capabilities deliver the strongest results when customers take ownership of outcomes and teams operationalize insights consistently. Platforms emphasized that predictive signals alone do not reduce churn — action does.

AI in churn reduction: What are the hardest problems to solve?

While AI technologies have matured rapidly, many teams still struggle with fragmented data systems, inconsistent event tracking, and organizational misalignment that make it hard to extract reliable signals or embed insights into everyday workflows. Industry-wide, teams report that churn prediction often stalls not because models are weak, but because the surrounding infrastructure — data pipelines, tool integration, and cross-functional process alignment — is underdeveloped or unevenly adopted.

Even when technical prerequisites are in place, broader challenges remain. Customer behavior continues to evolve with expanding product complexity, dispersed stakeholder engagement, and shifting usage patterns. At the same time, many companies are still building their capability to interpret qualitative signals, such as sentiment or relationship dynamics, alongside quantitative metrics. Together, these dynamics make meaningful churn prediction and retention execution harder than simply ‘building a model.’

Despite progress, the survey reveals persistent challenges. Variability in customer behavior was the most frequently cited obstacle to improving model accuracy, followed by inconsistent data inputs, limited historical data, and integration gaps. Data gaps across tools, limited event tracking, and poor labeling continue to constrain AI performance.

Several platforms emphasized that the real challenge is not generating insights but executing them. ChurnZero described this as the “execution gap” — the delay between recognizing churn risk and consistently acting on it at scale. Velaris similarly noted that customer success teams are overwhelmed by weak signals and need help interpreting them early enough to matter.

How AI automation helps close the churn execution gap

Automation plays a central role in closing this gap. All platforms reported automating workflows such as inactivity alerts, renewal reminders, onboarding sequences, upsell triggers, and post-support follow-ups. Platforms noted that automation is most impactful when it embeds churn signals directly into daily workflows, reducing manual monitoring and enabling faster, more consistent action. This approach helps ensure that churn insights translate into timely interventions rather than remaining passive alerts.

• ChurnZero and Velaris emphasized always-on AI agents that continuously monitor usage, sentiment, and engagement patterns, surfacing prioritized, prescriptive signals directly in daily workflows.
• Custify, ChurnZero, and Velaris reported that customers can operationalize AI predictions very easily. Chargebee emphasized that customers leverage its solution to enhance automated retention strategies, such as triggering proactive renewal offers and deploying reactive save interventions based on risk signals.

Workflows that combine risk detection with recommended actions rather than raw alerts delivered the strongest results. Across platforms, the emphasis is shifting from simply notifying teams of potential churn to guiding them toward the next best action. Automation is most effective when it reduces cognitive load, clarifies priorities, and embeds clear remediation steps within existing systems. In this model, AI does not just surface risk — it orchestrates response, ensuring that insights consistently translate into measurable retention impact.

How do CS teams measure AI’s impact on churn reduction?

As AI becomes embedded in customer success workflows, measuring its impact is becoming just as important as deploying it. Across the industry, organizations are shifting from activity-based reporting toward outcome-based accountability, especially as executive teams demand clearer visibility into ROI from AI investments. Retention metrics are increasingly viewed not only as customer success indicators but as board-level business health signals. This shift places pressure on CS teams to quantify how predictive insights, automation, and prioritization workflows translate into measurable churn reduction. As a result, AI in churn reduction is increasingly evaluated not just on accuracy, but on revenue influence.

Measurement practices also influence how seriously churn signals are treated across the organization. When churn prediction is tied directly to renewal outcomes, executive dashboards, and revenue forecasting, it gains strategic weight. Conversely, when AI outputs remain disconnected from formal performance metrics, adoption tends to weaken.

Among customers actively measuring retention outcomes, churn rate and renewal rate remain the most common metrics. Product adoption metrics, health score improvements, and manual customer success inputs are also widely used. Some platforms reported AI-based attribution as an emerging metric, while others noted limited visibility into how consistently customers measure retention impact.

Notably, a majority of customers on Custify and ChurnZero actively measure retention outcomes.

They stated that more than 75% of their customers actively monitor retention or churn reduction metrics inside the product, suggesting a strong integration of AI insights into formal performance tracking and renewal accountability. This reflects how AI-driven churn reduction is increasingly embedded as a core operational discipline rather than treated as a niche capability.

Where AI needs context to improve churn outcomes

Across responses, the common thread is that oversimplification, not ambition, is what limits AI’s impact on churn.

The qualitative responses revealed strong alignment on what is overrated today. Custify cautioned against fully automated churn decisions that remove human judgment from contextual situations. ChurnZero warned against over-indexing on historical usage data without incorporating sentiment, relationship, and commercial signals. Velaris highlighted the limitations of standalone churn scores without explanation or actionability.

Chargebee added that overly precise models are often unnecessary. A churn model only needs to be accurate enough to justify the cost of intervention. Waiting for perfect accuracy often delays real-world impact.

What will matter most for AI-driven churn reduction by 2026

Rather than chasing novelty, platforms are prioritizing AI capabilities that deepen understanding and support earlier, more human interventions.

Looking ahead, platforms pointed to more nuanced AI capabilities as the next frontier. Platforms indicated that future churn AI will emphasize contextual understanding and early signal detection, helping teams intervene before risk becomes explicit rather than reacting to late-stage indicators.

• Custify highlighted AI’s ability to infer future emotional states, enabling earlier and more empathetic interventions.
• ChurnZero emphasized deep contextual memory—AI that understands customer journeys as evolving narratives rather than isolated events.
• Velaris pointed to qualitative signal analysis across conversations, notes, and feedback as an early indicator of churn risk.

Across responses, one overlooked foundation stood out: data hygiene and ownership. Clean, well-labeled, historically complete data, especially data from churned accounts, was repeatedly cited as essential for reliable AI performance.

Beyond signals: Turning AI insight into retention impact

Taken together, these insights reflect a maturing view of AI, one grounded in practicality, context, and long-term customer relationships.

The survey makes one conclusion clear: AI-driven churn reduction in 2026 will be less about prediction alone and more about execution, context, and integration. Platforms like Custify, ChurnZero, Chargebee, and Velaris are converging on a shared vision: AI that helps teams understand risk earlier, act faster, and scale best practices without losing human judgment. The next phase of AI in churn reduction will be defined by execution depth, contextual intelligence, and measurable business outcomes.

For organizations investing in churn AI, success will depend not just on models, but on clean data, embedded workflows, and a clear definition of customer value. AI does not eliminate churn on its own, but when purpose-built and operationalized correctly, it becomes a powerful force multiplier for retention.

If you’re in SaaS, don’t wait for churn to show up in your metrics. See which customers are actively researching competitors and step in before it’s too late. Learn more about G2 Buyer Intent.