#churnrate

Investing heavily in customer acquisition is pointless if users are leaving at almost the same speed they arrive. This creates the classic leaky bucket problem, where revenue constantly slips out despite strong marketing efforts. If you want to build a sustainable business, you must focus on strategies that reduce SaaS churn rate effectively. It is no longer optional; retaining your existing customers is now the core foundation of SaaS growth, profitability, and long-term survival. 

With increasing competition and AI-powered SaaS tools entering the market every day, effective churn mitigation has become more important than acquisition itself.

What is SaaS Churn Rate and Why It Matters in 2026

SaaS churn rate is the percentage of customers who cancel their subscription within a specific time period. It directly impacts revenue stability, growth velocity, and investor confidence.

In 2026, SaaS investors prioritize Net Revenue Retention (NRR) and the ratio between Customer Acquisition Cost (CAC) and Customer Lifetime Value (CLV). A high churn rate signals poor product value, while a low churn rate reflects strong product-market fit and scalable growth.

Types of SaaS Churn

Voluntary Churn: Users intentionally cancel due to dissatisfaction, pricing issues, or missing features. This usually indicates a product or value mismatch.

Involuntary Churn: Users leave due to failed payments, expired cards, or billing errors. This is preventable churn and highly recoverable through automation.

Why SaaS Churn is More Dangerous Than Low Acquisition

SaaS growth does not depend only on acquiring users but also on retaining them. High churn directly destroys your Customer Lifetime Value (CLV), making your marketing spend (CAC) inefficient.

If a SaaS company acquires 1,000 users monthly but loses 10% churn every month, the entire user base can collapse within a year. This makes churn the silent killer of SaaS scalability.

SaaS Onboarding Optimization to Reduce Early Churn

Most SaaS churn happens in the first few days when users fail to experience product value quickly. This stage is critical for activation and retention.

Reduce Time to Value (TTV). Time to Value (TTV) is the speed at which users experience their first meaningful result. A shorter TTV leads to higher retention. SaaS companies must remove unnecessary steps, reduce friction, and guide users toward their “Aha Moment” immediately after signup.

Use Interactive Onboarding Experiences. Interactive onboarding improves User Engagement Metrics and reduces confusion:

Progress bars show completion status.

Tooltips provide contextual guidance.

Checklists gamify the onboarding process.

Explainer Videos for Faster Understanding. Explainer videos help simplify complex SaaS workflows into easy visual steps. Instead of reading long documentation, users quickly understand product value through 60–90 second videos. This improves activation rate and reduces early churn caused by confusion.

Personalized Onboarding for B2B SaaS High-value SaaS users expect personal attention. A welcome email, Loom video, or onboarding call from a Customer Success Manager creates trust and emotional connection. This significantly increases early retention and improves long-term User Engagement Metrics.

Predictive Analytics to Prevent SaaS Churn

Modern SaaS companies must move from reactive to proactive retention strategies.

Identify At-Risk Users Early. Monitoring key User Engagement Metrics helps identify users at risk of churn before they leave:

Reduced login frequency

Lower feature usage

Decreased email engagement

AI-Based Churn Prediction Systems: AI tools analyze user behavior patterns to predict churn in advance. This allows SaaS teams to proactively engage users with targeted support, special offers, or feature guidance before they decide to cancel.

Modern SaaS companies must move from reactive to proactive retention strategies and strong SaaS lead generation strategies to maintain consistent growth.

Reduce Involuntary Churn (Revenue Recovery Strategy)

A large portion of SaaS churn is not intentional; it is caused by payment issues.

Smart Dunning Management Systems: Dunning systems automatically retry failed payments using optimized timing and notify users before card expiration. This helps recover lost revenue without manual intervention, directly improving your NRR.

Grace Period Strategy Instead of blocking users immediately after payment failure, SaaS companies should provide a 3–7 day grace period. This improves customer experience and increases the chance of successful payment recovery.

Increase Product Stickiness Using PLG Strategy

Product stickiness ensures users rely heavily on your SaaS product, making churn difficult.

Build Switching Costs Through Integrations. When SaaS products integrate with tools like Slack, CRM, or email platforms, users become dependent on them. For example, Slack and Zoom maintain high retention because they are deeply integrated into a user's daily workflow.

Data Dependency Increases Retention. When users store workflows, reports, and historical data inside your system, leaving becomes difficult. This creates long-term dependency and naturally boosts your Customer Lifetime Value (CLV).

Strong Feedback Loop Builds Loyalty. When users see their feedback reflected in product updates, they feel valued. Public roadmaps and feature voting systems increase emotional loyalty and reduce churn.

Optimize Pricing Strategy for SaaS Retention

Pricing is a major factor in churn behavior and your overall CAC to CLV ratio.

Promote Annual Subscription Plans: Annual plans reduce churn by increasing user commitment and improving cash flow stability.

Subscription Pause Instead of Cancellation: Allowing users to pause subscriptions prevents permanent churn during temporary inactivity.

Exit-Intent Retention Offers: When users try to cancel, offering discounts, free consultation, or alternative pricing plans can be a highly effective churn mitigation tactic.

Conclusion

In 2026, SaaS success is not defined by how many users you acquire, but by how many you retain and grow. Companies that focus on onboarding optimization, proactive churn prevention, product stickiness, and pricing strategy will dominate the SaaS market.

El customer churn sirve como termómetro de la salud de tu negocio. Su fluctuación indica si los consumidores están contentos o corren el riesgo de dejarlo. En tu rutina de gestión de calidad, conocer esta métrica tiene algunos usos prácticos...

Churn is a term used to describe the percentage of customers who leave or stop using a service/product over a given period. Calculating the churn rate is essential for SaaS companies to find out user success. It enables them to monitor the overall health of their business and understands when and where they need to take corrective actions.

A study published by KBCM Technology Group suggests that SaaS businesses’ median annual revenue churn is a little over 13%, and the customer churn rate is 21%. Of course, an acceptable churn rate depends widely on the kind of market and the size of your business, but a SaaS business should aim to keep both its annual revenue churn and customer churn as low as possible. This is especially important for retaining new customers, who are crucial for a company’s growth.

UX mistakes to Avoid in SaaS Products

When it comes to customers churning on a particular product, factors can include how well they were onboarded, the app’s usability, and their overall customer experience. SaaS UX is an essential aspect of your product’s success and can play a decisive role in whether customers stick around or leave. Customer retention and satisfaction rely heavily on the quality of the customer experience and how well you understand their needs. UX can help reduce churn by making it easier for your customers to connect with what your product does for them. For example, SaaS companies such as Zoom and Slack have improved their UX to deliver excellent experiences that meet user needs and deliver added value over competitors.

So it is all the more necessary to manage and adjust UX mistakes that cause SaaS customers to churn. Users will churn on a product if the UX creates an unpleasant experience, is slow and clunky, or lacks the interface. Designers need to be careful and avoid some common mistakes when designing SaaS products. So let’s take a look at each of them along with the possible solutions.

1. Insufficient or Too Many Features

Though simple interfaces make it easier for customers to understand the products, having insufficient features will force them to look for alternatives that can better meet their business requirements, increasing the churn rate. Another common mistake is assuming that adding more features will improve your product. On the contrary, adding too many features can increase the complexity of an already complex service, making it difficult for users to adopt the solution.

Solution: So designers should think before adding features willy-nilly. A simple solution is to create a clear feature-focused map to better align the product to the features.

2. Scalability Issues

A SaaS product needs to be scalable. Scalability issues can cause users to churn when you cannot meet their expectations for speed and performance. Slow-loading pages, network delays, and performance problems can ultimately affect user satisfaction. Scalability is essential, but at the same time, designers need to ensure that the focus on scalability does not detract from what makes the product unique.

Solution: There are other ways to scale your business without compromising on the quality of services the products offer. Working on performance optimization, speed, and scalability together, can serve as a solid solution. Process automation is another way to get there without compromising on your customers’ experience.

3. Slow Setup Procedures

SaaS products depend on a fast and easy setup process to get users up and running in a short period so they can immediately experience the value. But if a product takes too long to set up, customers may lose patience during the process or leave because the process is too time-consuming. Likewise, delays and errors during the sign-up process can turn customers away. And if this happens, it is easy to see why those users will churn.

Solution. The solution is simple, designers need to make the sign-up process faster and not keep customers waiting for authorization and provisioning. The onboarding flow should be designed to be as smooth as possible.

4. No Space for Customer Engagement

User engagement can build a loyal customer base. Engaged customers are more likely to trust a product and won’t churn as easily as those who can’t engage with the product or don’t want to. Engagement will provide a steady stream of product improvements and customer insights to designers, making it less likely for users to churn.

Solution: SaaS products need an engaged user-base with regular engagement channels such as email marketing campaigns or newsletters that foster open communication between users and the product. This will help them to keep in touch with their customers’ changing needs, improving features over time.

5. Weak Customer Journey

Designers need to research existing products and understand what features users love or hate to avoid churn. The customer journey should not be made up of pain points. Instead, it should be as smooth as possible to build stronger relationships between customers and businesses.

Solution: You can do this by performing a customer experience audit which helps you better understand how users interact with your product, identifying their pain points and what they need to improve the overall product experience. This way, customers will stay with you longer because they will feel like their needs are being met.

6. Not Utilizing Customer Feedback and Reviews

One of the most important reasons customers stay with a product is because they feel their feedback and ideas for improvement are heard and implemented. In UX, designers cannot ignore the voice of the customer. If customers don’t feel like their ideas are heard and matter, they will be more unsatisfied with the product overall and more likely to churn.

Solution: One way to fix this is by implementing customer-centered design principles whenever possible. Bring the user into the process and show them, through research and testing, how much your product evolves based on their feedback. Provide customer feedback channels like online support forums, a blog for posting questions, Twitter or Facebook accounts where users can reach out to your business. If customers think they have no voice in improving the products they use, they will churn.

7. Failing to Strike a Balance Between Aesthetics and Functionality

Functionality and aesthetics are both necessary. The product must be functional enough to facilitate users’ needs and be aesthetically pleasing to engage them with the design. Focusing too much on one or the other will drive users away. Customers will not stick around if they are unhappy with how your business looks or if it fails to function correctly.

Solution: It is best to balance these two when designing your product. The aesthetics of a product is important, but they need not overshadow the design’s primary purpose.

8. No Prototyping and Testing

This is an obvious tell that the design has significant flaws. But, unfortunately, designers can’t see if their designs are working before going live. So, it is vital to develop prototypes.

Solution: Designers should always include prototyping and user testing as a part of their process to help them better understand how users interact with their product, making designing easier and more efficient in the long run. Prototyping includes low-fidelity wireframes, common in Agile processes, or high-fidelity prototypes.

9. Not Utilizing the UX Hook Model

The UX design hook model helps designers understand how to create their products to hook and retain users. This model allows you to identify which features and functions will keep your customers coming back. Failing to understand the customers and implementing the UX hook model will make churning more likely.

Solution: Designers should employ the hook model in their design process whenever they can. The UX Hook Model breaks down product engagement into four stages—trigger, action, variable reward, and investment. When all four are successfully implemented together, they lead to habit formation in users.

Conclusion

Customer churn rate can either make or break a business. Customers churning too often can become a serious problem and affect the business’ revenue and growth, whether because of bad customer service or lack of quality products that don’t meet their needs. These issues can easily be avoided with proper design processes and UX design. SaaS UX designers should always keep these mistakes in mind, as they are some of the most common ways for a SaaS product to lose users. Implementing strategies to help fix these issues will lead to happier customers and more revenue.

Продолжая разговор про цифровизацию HR-процессов, сегодня мы рассмотрим, как технологии больших данных (Big Data) и машинного обучения (Machine Learning) помогают сократить текучку кадров и удержать ключевых работников. Читайте в нашей новой статье 5 успешных примеров применения аналитики Big Data в HR для принятия эффективных управленческих решений.

Big Data и Machine Learning против эмоционального выгорания и других причин «внезапного» увольнения

Эмоциональное выгорание (однообразность рабочих процессов, потеря мотивации и отсутствие интереса к задачам) входит в ТОП-10 причин увольнения по собственному желанию. Зная о степени подверженности своих сотрудников этой проблеме, а также другим важным факторам (удовлетворенность зарплатой, отношениями с коллективом, условиями труда и пр.) [1], работодатель своевременно может предпринять нужные меры, не допустив ухода нужных работников. Вообще снижение текучести кадров и удержание ключевых сотрудников – одна из ключевых HR-задач любого руководителя. Аналитические технологии на базе Big Data и Machine Learning позволяют определить вероятность увольнения с помощью предиктивных моделей. Как правило, чтобы получить корректную модель будущего поведения сотрудника, используются следующие данные [2]: ·       профессиональные компетенции (уровень образование, владение иностранными языками); ·       сведения о выполняемой деятельности (должность, наличие подчиненных, условия труда, показатели эффективности работы и их значения); ·       индивидуальные характеристики человека (пол, возраст, социальный статус, наличие детей, психологические особенности); ·       информация об удовлетворенности (рабочий график, уровень комфорта на рабочем месте, уровень оплаты труда, перспективы карьерного роста, время проезда до места работы, конфликты с коллегами и начальством); ·       внешние факторы на рынке труда (востребованность компетенций, динамика изменений зарплаты, репутация работодателя).   Например, собрав такие данные на каждого сотрудника, HR-менеджеры корпорации IBM заблаговременно получают сигналы о возрастании рисков увольнения своих работников и могут предотвратить эти события [3]. Аналогичная Big Data система была внедрена в ИТ-департаменте одной крупной отечественной компании. Примечательно, что прогнозные модели строились не только на статической информации о сотрудниках. Показатели текущей удовлетворенности и вовлеченности работников вычислялись на основе данных из внутренней системы контроля и управления доступом (СКУД). По сути, СКУД выступала источником данных о корпоративном поведении каждого человека, четко фиксируя время перерывов, опозданий, ранних уходов и поздних приходов. Выявленная таким образом динамика отношения сотрудника к работе позволяла спрогнозировать вероятность его профессионального выгорания и предупредить это явление [2]. Похожим образом Big Data система предиктивной аналитики для HR используется в корпорации Hewlett-Packard. Еще в 2011 году штатные Data Scientist’ы построили собственные ML-модели, прогнозирующие вероятность увольнения каждого из 300 000 сотрудников. Для этого анализировались данные отдела кадров за предыдущие 2 года. В результате был сгенерирован показатель «Flight Risk» (риск ухода), значение которого рассчитывается индивидуально для каждого сотрудника в зависимости от его личных метрик. Благодаря этому HR-менеджеры своевременно реагировали на сигналы-предвестники увольнений и предупреждали их. Такая система машинного обучения сэкономила предприятию около $300 000 000. Кроме того, предиктивная аналитика в HR показала, что для сотрудников Hewlett-Packard увеличение зарплаты также важен, как и карьерный рост. В частности, в отличие от корпорации Google, при отсутствии повышения денежной компенсации за свой труд, даже при повышении в должности, работник Hewlett-Packard увольняется в течение 5 лет. А вот анализ данных о текучести кадров в Google показал, что новые сотрудники отдела продаж, которые не получают повышения в течение 4 лет, чаще увольняются [3]. Интересен также опыт американского банка JPMorgan, который внедрил HR-систему на базе технологий Big Data и машинного обучения, чтобы заранее выявлять риски инсайдерского мошенничества. Также финансовая корпорация использует этот ML-проект для прогнозирования случаев внезапного увольнения банковских работников. Например, отследить настроение сотрудников и снижение их мотивации помогает NLP-анализ эмоциональной активности в рабочих чатах и переписке по корпоративной почте [4].

ML-модели помогут заблаговременно выявить риск ухода сотрудника и предупредить его

Поможет ли предиктивная аналитика сократить число увольнений?

Впрочем, говоря о прогнозировании увольнений, подчеркнем еще раз важность процесса сбора данных для аналитической модели и определения ключевых предикторов – переменных, которые влияют на результат. В частности, обновление резюме далеко не всегда означает поиск нового места работы. И, наоборот, отсутствие в обучающем датасете отдельных метрик может привести к неполноте модели Machine Learning и, соответственно, неверной интерпретации результатов моделирования. Кроме того, даже при наличии верного прогноза и корректной HR-аналитики, руководитель далеко не всегда обладает рычагами влияния на сотрудника с высоким риском ухода [5]. Поэтому ML-модель оттока работников (Churn Rate) должна непрерывно дополняться новыми данными и корректироваться с учетом внутреннего состояния предприятия и ситуации на рынке труда. Например, кризис в отрасли или в отдельной компании существенно повышает вероятность массового увольнения сотрудников по собственному желанию. Аналогичным образом действуют дорожные заторы и пробки, увеличивающие время, которое человек тратит, чтоб добраться до работы [1]. Как технологии Big Data позволяют выявить такие факторы и снизить их влияние на работников, сокращая риск ухода и повышая вовлеченность, мы расскажем в следующей статье.

Цифровизация HR-процессов с помощью технологий Big Data и Machine Learning позволит сократить текучесть кадров Как эффективно использовать большие данные и машинное обучение для предиктивной аналитики в управлении персоналом и запустить цифровизацию HR-процессов в своем бизнесе, вы узнаете на наших образовательных курсах в лицензированном учебном центре обучения и повышения квалификации руководителей и ИТ-специалистов (менеджеров, архитекторов, инженеров, администраторов, Data Scientist’ов и аналитиков Big Data) в Москве: Аналитика больших данных для руководителей

 Смотреть расписание занятий

In this blog, we covered in-depth the rise of the subscription business model as the dominant model in the tech startup world and beyond. There are several reasons for that, which are mainly addressed in The Power of the Subscription Business Model: https://fourweekmba.com/subscription-business-model/ In this short article, I want to report some of the numbers from the Subscription Economy Index from Zuora, a company that sells subscription management services. As reported from Zuora: Growth rates per customer segments: B2B growth: 20% B2C growth: 9% B2A growth: 14% Growth rates per industry SaaS growth: 23% Telecommunications growth: 9% Media growth: 10% Corporate Services growth: 5% Average annual growth: $1M-$20M: 18% $20M-$100M: 14% $100M+: 28% Churn rates: B2B: 26% B2C: 35% B2A: 27% Corporate Services: 35% Telecommunications: 29% SaaS: 25% Media: 36% You can consult the whole report here. For a better understanding of the subscription business model industry read this: