Data as of Jul 25, 2026 · Based on 326 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
To predict which customers are at risk, you can use specialized customer success software or AI-driven analytics tools. These platforms typically aggregate usage, sentiment, and support data to calculate health or risk scores. 
Brands AI recommends here
ChurnZero is a leading choice for subscription businesses. It uses machine learning to generate health scores and risk alerts by tracking product usage and support interactions, which helps teams intervene before customers leave.
Gainsight offers comprehensive retention software suitable for large teams. It aggregates data from various touchpoints to provide proactive risk alerts and actionable insights, designed for complex enterprise environments.
Akkio is a no-code AI platform that allows teams to build and deploy their own churn risk models without needing data scientists. It transforms historical data into actionable risk scores for specific accounts.
ChurnZeroSource | Citation share |
|---|---|
 10 best customer churn prediction software of 2026https://www.zendesk.com/service/customer-experience/churn-prediction-software/ | 7.2% |
 10 best customer churn prediction software options in 2026https://www.pecan.ai/blog/customer-churn-prediction-software/ | 4.0% |
 AI-Powered Churn Prediction Software & Retention Tools | Pendo Predicthttps://www.pendo.io/product/pendo-predict/ | 3.9% |
Predicting and Preventing Churn with AI | Gainsight Softwarehttps://www.gainsight.com/blog/predicting-and-preventing-churn-with-ai/ | 3.0% |
 Strategies to Predict and Reduce Customer Churn - Gonghttps://www.gong.io/blog/predicting-churn | 3.0% |
 Best Customer Success Tools in 2026 (for Startups to Enterprises)https://userpilot.com/blog/churn-prediction-software/ | 2.9% |
 8 Best Churn Management Software Tools in 2026https://contentsquare.com/guides/customer-churn/software/ | 2.8% |
 5 Best Tools for Churn Preventionhttps://www.teamgate.com/blog/5-best-tools-for-churn-prevention/ | 2.6% |
 How AI and Analytics Help You Predict and Prevent Customer Churnhttps://www.expressanalytics.com/blog/predict-customer-churn-retention-strategies | 2.4% |
 13 Top Churn Management Softwares in 2026: Features, Pricinghttps://vwo.com/blog/churn-management-software/ | 2.3% |
Yes, there are many tools designed specifically to predict and prevent customer churn . They generally fall into a few distinct categories depending on your company size, tech stack, and whether you want to focus on customer success operations, billing/cancellation flows, or raw product analytics.
Top churn prediction and retention software options include:
If you'd like to narrow down the options, tell me:
Yes. There’s a fairly mature category of customer-success / churn-prediction tools that score customers based on signals like product usage, support interactions, engagement, payment history, and renewal data.
A few worth looking at:
If you're a B2B SaaS company with a customer-success team, I'd start by comparing ChurnZero vs. Gainsight vs. Totango.
If you're a smaller SaaS company and mainly want “give me a list of customers most likely to leave, and tell me why”, a lighter predictive tool may be more appropriate than buying a full enterprise CS platform.
The important distinction is prediction vs. health scoring. A basic health score says “this customer looks unhealthy according to rules we configured.” A predictive model tries to learn which combinations of historical behaviors actually preceded churn. ChurnZero, for example, explicitly describes its predictive system as finding hidden factors correlated with churn.
If you tell me what kind of business you have, roughly how many customers you have, and what data you already collect (Stripe, HubSpot, Salesforce, product usage, support tickets, etc.), I can narrow this down to the 2–3 tools I'd actually consider for your situation, including pricing and implementation effort.
Yes, there are many tools designed specifically to predict and prevent customer churn . They generally fall into three categories depending on your company size, budget, and tech stack:
To help narrow down the best fit, tell me:
Yes. What you’re looking for is usually called customer health scoring or predictive churn scoring. These tools analyze signals such as product usage, engagement, support activity, sentiment, and renewal history to identify accounts that are becoming likely to churn.
A few strong options:
My recommendation: if you're trying to reduce churn rather than just report it, prioritize a tool that answers both “Who is likely to leave?” and “Why are they at risk, and what should we do?” A risk score alone isn't very useful unless it drives an intervention.
If you tell me what kind of business you have (e.g. SaaS, subscription, ecommerce), roughly how many customers you have, and what CRM/product-analytics tools you already use, I can narrow this down to 2–3 options that fit.
Yes, multiple platforms use predictive analytics and machine learning to identify customers at risk of leaving. Top specialized tools include Gainsight for enterprise customer success orchestration, ChurnZero for real-time subscription tracking and health scores, Braze for automated and predictive multichannel marketing triggers, and Enterpret for analyzing early qualitative feedback signals.
Popular Churn Prediction Tools
Key Signals Tracked by Predictive Tools
If you can share your business model (e.g., B2B SaaS, e-commerce, mobile app) and what data you currently track , I can recommend the most compatible platform for your stack.
Yes. What you’re looking for is usually called customer health scoring or predictive churn analysis. These tools combine signals like product usage, engagement, support activity, sentiment, and renewal history to flag accounts that are likely to churn.
A few strong options:
My quick recommendation: if your main question is “Which customers are likely to leave in the next 30–90 days, and why?”, I'd start by evaluating ChurnZero and Gainsight.
One important caveat: don't judge these tools solely by whether they produce a “churn score.” The useful system should tell you which signals caused the risk and give your team something actionable to do. Usage decline alone can be a weak predictor; combining behavioral, commercial, support, and qualitative/sentiment signals tends to produce a much more useful risk model.
If you tell me what kind of business you have, roughly how many customers you have, and where your customer data lives (Stripe, Salesforce, HubSpot, product database, etc.), I can narrow this down to 2–3 tools that fit your situation.
Yes. There are several customer-success platforms specifically designed to identify accounts at risk of churning.
The key distinction I'd make is health scoring vs. actual predictive churn modeling. A basic system might say “this customer hasn't logged in recently.” A stronger predictive system looks across multiple signals and learns which combinations historically precede churn.
If you tell me what kind of business you have (SaaS, subscription, ecommerce, services, etc.) and roughly how many customers you have, I can recommend 2–3 tools that fit your situation and compare them on price, setup effort, integrations, and predictive accuracy.
Yes, there are several dedicated tools that predict customer churn by analyzing product usage, support tickets, billing events, and engagement patterns . The right choice depends heavily on your company size, budget, and whether you need a full customer success workspace or just pure predictive analytics.
Top tools used to predict and prevent churn are organized by category below:
To help narrow down which tool fits your stack, could you share:
Yes, there are many tools designed specifically to predict and prevent customer churn . They range from lightweight analytics to heavy, enterprise-grade Customer Success (CS) platforms.
The best tool for you depends heavily on company size, budget , and where your data lives (product usage vs. billing vs. support tickets).
These platforms pull in product usage, CRM data, and support logs to calculate a live "health score" and alert you when an account is slipping.
If you already track user events inside your application, you can configure these tools to spot declining usage patterns (like a drop in daily active users or specific feature abandonment) before the customer cancels.
Some churn happens at the checkout page or via failed credit cards, requiring a completely different type of intervention.
To help narrow down which tool fits your exact setup, tell me:
Yes. There are many tools designed to predict customer churn by analyzing signals such as product usage, login frequency, support interactions, billing events, and customer satisfaction. Instead of only reporting who has already left, they estimate which customers are at risk so your team can intervene earlier.
Some well-known options include:
The best choice depends on your business:
Regardless of the tool, the most effective models usually combine multiple signals such as:
A prediction is most valuable when it's paired with an action—for example, alerting a customer success manager, triggering a personalized email, or scheduling an outreach campaign before renewal.
If you tell me:
I can suggest the options that fit your situation best.
