Customer retention rates are highest in the media and professional services industries, with a 2018 survey of businesses worldwide finding a customer retention rate of ** percent in both of these industries. The industry with the lowest customer retention rate was hospitality, travel and restaurants with ** percent.

Industry-specific customer retention rate benchmarks and performance metrics for business optimization

According to a May 2025 study on the client retention rates of leading public relations agencies, Public Communications Inc. had the highest rate, at 97 percent, closely followed by JCPR, Inc., at 96 percent.

This is a customer churn dataset from the telecom industry, which includes customer data such as long-distance usage, data usage, monthly revenue, types of offerings, and other services purchased by customers. The data, based on a fictional telecom firm, includes several Excel files which have been combined.

Not all app categories can boast the same degree of user retention on day 30. While news apps were reported in the third quarter of 2024 to have a retention rate of almost 10 percent, social media apps presented less than two percent retention rate after 30 days from install. Entertainment apps presented a three percent installation rate, while a shopping apps had a retention rate of around four percent one month after installation. Before retention: user acquisition Gaining new users is fundamental for the healthy growth of a mobile application, and app developers have an array of tools that can be used to expand their audience. As of the second quarter of 2022, CPI, or cost per install, was the most used pricing model for user acquisition campaigns according to app developers worldwide. The cost of acquiring one new install in North America was of 5.28 U.S. dollars, but driving in-app purchases in the region was more pricey, with a cost of roughly 75 U.S. dollars per user. The future of in-app advertising In recent years, subscriptions and in-app purchases have become more popular app monetization practices, with users finally willing to pay for app premium functionalities and services. In 2020, video ads were reportedly the most expensive type of ads to drive conversions on both iOS and Android apps, while banner ads had a cost per action (CPA) of 36.77 U.S. dollars on iOS, and 10.28 U.S. dollars on Android.

Although the results were close, the industry in the United States where customers were most likely to leave their current provider due to poor customer service appears to be cable television, with a 25 percent churn rate in 2020.

Churn rate

Churn rate, sometimes also called attrition rate, is the percentage of customers that stop utilizing a service within a time given period. It is often used to measure businesses which have a contractual customer base, especially subscriber-based service models.

Context

"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets]

Content

Each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents

Inspiration

To explore this type of models and learn more about the subject.

New version from IBM: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

Business problem overview In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition.

For many incumbent operators, retaining high profitable customers is the number one business goal.

To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Understanding and defining churn There are two main models of payment in the telecom industry - postpaid (customers pay a monthly/annual bill after using the services) and prepaid (customers pay/recharge with a certain amount in advance and then use the services).

In the postpaid model, when customers want to switch to another operator, they usually inform the existing operator to terminate the services, and you directly know that this is an instance of churn.

However, in the prepaid model, customers who want to switch to another network can simply stop using the services without any notice, and it is hard to know whether someone has actually churned or is simply not using the services temporarily (e.g. someone may be on a trip abroad for a month or two and then intend to resume using the services again).

Thus, churn prediction is usually more critical (and non-trivial) for prepaid customers, and the term ‘churn’ should be defined carefully. Also, prepaid is the most common model in India and Southeast Asia, while postpaid is more common in Europe in North America.

This project is based on the Indian and Southeast Asian market.

Definitions of churn There are various ways to define churn, such as:

Revenue-based churn: Customers who have not utilised any revenue-generating facilities such as mobile internet, outgoing calls, SMS etc. over a given period of time. One could also use aggregate metrics such as ‘customers who have generated less than INR 4 per month in total/average/median revenue’.

The main shortcoming of this definition is that there are customers who only receive calls/SMSes from their wage-earning counterparts, i.e. they don’t generate revenue but use the services. For example, many users in rural areas only receive calls from their wage-earning siblings in urban areas.

Usage-based churn: Customers who have not done any usage, either incoming or outgoing - in terms of calls, internet etc. over a period of time.

A potential shortcoming of this definition is that when the customer has stopped using the services for a while, it may be too late to take any corrective actions to retain them. For e.g., if you define churn based on a ‘two-months zero usage’ period, predicting churn could be useless since by that time the customer would have already switched to another operator.

In this project, you will use the usage-based definition to define churn.

High-value churn In the Indian and the Southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if we can reduce churn of the high-value customers, we will be able to reduce significant revenue leakage.

In this project, you will define high-value customers based on a certain metric (mentioned later below) and predict churn only on high-value customers.

Understanding the business objective and the data The dataset contains customer-level information for a span of four consecutive months - June, July, August and September. The months are encoded as 6, 7, 8 and 9, respectively.

The business objective is to predict the churn in the last (i.e. the ninth) month using the data (features) from the first three months. To do this task well, understanding the typical customer behaviour during churn will be helpful.

Understanding customer behaviour during churn Customers usually do not decide to switch to another competitor instantly, but rather over a period of time (this is especially applicable to high-value customers). In churn prediction, we assume that there are three phases of customer lifecycle :

The ‘good’ phase: In this phase, the customer is happy with the service and behaves as usual.

The ‘action’ phase: The customer experience starts to sore in this phase, for e.g. he/she gets a compelling offer from a competitor, faces unjust charges, becomes unhappy with service quality etc. In this phase, the customer usually shows different behaviour than the ‘good’ months. Also, it is crucial to...

The user retention software market is experiencing robust growth, driven by the increasing need for businesses to improve customer loyalty and reduce churn. The market, estimated at $5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors. Firstly, the rise of subscription-based business models necessitates effective strategies to retain paying customers. Secondly, advancements in data analytics and machine learning are enabling more sophisticated user behavior analysis, leading to targeted interventions to improve engagement and prevent churn. Thirdly, the increasing adoption of cloud-based solutions is making user retention software more accessible and affordable for businesses of all sizes. Companies are increasingly leveraging data-driven insights to personalize customer experiences, improve onboarding processes, and proactively address potential issues that could lead to customer attrition. The competitive landscape is dynamic, with established players like Zendesk Connect and Qualtrics competing alongside innovative startups. Market segmentation is largely driven by industry vertical (e.g., SaaS, e-commerce, gaming), software functionality (e.g., predictive analytics, customer feedback tools), and deployment model (cloud vs. on-premise). Geographic growth is expected to be most significant in North America and Asia-Pacific, driven by high technology adoption rates and burgeoning digital economies. Despite the promising growth trajectory, the market faces certain challenges. Integration complexities with existing systems can hinder adoption, while concerns around data privacy and security remain paramount. Furthermore, the market's success hinges on the continued development of advanced analytical capabilities and the ability of software providers to demonstrate a clear return on investment (ROI) for their clients. Ultimately, user retention software is evolving beyond simple engagement tracking to encompass a holistic approach towards fostering long-term customer relationships and building brand loyalty. Future success in this market will depend on providers offering integrated solutions that leverage AI and machine learning for predictive analytics, personalized communication, and proactive customer support.

Customer Retention Solutions for Insurers Market Outlook

According to our latest research, the global customer retention solutions for insurers market size reached USD 3.2 billion in 2024, reflecting the rapidly growing importance of customer-centric strategies in the insurance sector. The market is expected to expand at a robust CAGR of 11.4% during the forecast period, with the market projected to reach USD 8.6 billion by 2033. This remarkable growth is primarily driven by the increasing need for insurers to differentiate themselves in a highly competitive landscape, leveraging advanced technologies to enhance customer satisfaction and loyalty.

One of the most significant growth factors propelling the customer retention solutions for insurers market is the ongoing digital transformation within the insurance industry. Insurers are increasingly adopting digital platforms and advanced analytics to personalize interactions, streamline claims processing, and proactively address policyholder needs. The integration of artificial intelligence, machine learning, and big data analytics empowers insurers to gain deeper insights into customer behavior, preferences, and potential churn risks. This shift towards data-driven decision-making is not only optimizing operational efficiency but also enabling insurers to craft highly targeted retention strategies that improve customer lifetime value and reduce acquisition costs. The demand for seamless, omnichannel experiences has further accelerated the adoption of sophisticated customer retention solutions, as insurance providers strive to meet evolving customer expectations for convenience, transparency, and responsiveness.

Another critical driver for the market’s expansion is the intensifying competition among insurers, both from traditional players and new-age insurtech startups. As policyholders are presented with an ever-widening array of choices, insurers are compelled to invest in robust customer retention solutions to minimize churn and foster long-term loyalty. The cost of acquiring new customers in the insurance industry remains significantly higher than retaining existing ones, making retention initiatives a strategic imperative. Furthermore, regulatory developments emphasizing fair treatment of customers and enhanced transparency have placed additional pressure on insurers to prioritize customer satisfaction. The adoption of retention solutions not only helps insurers comply with these regulatory mandates but also enhances their brand reputation and trustworthiness in the eyes of policyholders.

The growing emphasis on personalized communication and tailored product offerings is also fueling the adoption of customer retention solutions among insurers. Modern policyholders expect insurers to anticipate their needs and provide relevant recommendations at the right time, whether it is through proactive renewal reminders, personalized policy suggestions, or timely claims updates. Customer retention solutions equipped with advanced analytics and automation capabilities enable insurers to deliver these personalized experiences at scale, thereby deepening customer engagement and loyalty. The proliferation of digital channels, including mobile apps, chatbots, and social media, has further expanded the touchpoints through which insurers can interact with customers, making it imperative to deploy integrated retention strategies that ensure consistency and continuity across all channels.

From a regional perspective, North America currently dominates the global customer retention solutions for insurers market, accounting for the largest revenue share in 2024. The region’s mature insurance industry, coupled with high digital adoption rates and a strong focus on customer experience, has driven significant investments in retention technologies. Europe follows closely, benefiting from stringent regulatory frameworks and a growing emphasis on data-driven customer engagement. Meanwhile, the Asia Pacific region is poised for the fastest growth during the forecast period, fueled by rapid insurance sector expansion, increasing digital penetration, and rising awareness of the importance of customer retention in emerging markets. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a more gradual pace, as insurers in these regions begin to recognize the long-term value of investing in customer retention strategies.

The Customer Churn Software market is experiencing robust growth, driven by the increasing need for businesses to retain customers and improve profitability. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the increasing availability of sophisticated analytics and AI-powered prediction models enabling proactive churn management, and the growing focus on delivering personalized customer experiences to enhance loyalty. Major players like IBM, Adobe, Salesforce, and Microsoft are actively shaping the market through continuous innovation and strategic acquisitions, contributing to a competitive landscape that fosters further growth. However, the market also faces certain restraints. The high initial investment costs associated with implementing sophisticated churn prediction software can be a barrier for smaller businesses. Furthermore, the complexity of integrating these solutions with existing CRM and data management systems can pose challenges, requiring significant expertise and resources. Despite these challenges, the long-term benefits of reduced customer churn significantly outweigh the initial investment, driving market expansion. The segmentation within the market is diverse, encompassing solutions catering to specific industry verticals and customer sizes, allowing for targeted solutions addressing unique churn drivers within each sector. The increasing prevalence of subscription-based business models further fuels the demand for effective churn management tools.

Customer Retention Solutions for Insurers Market Outlook

According to our latest research, the global Customer Retention Solutions for Insurers market size in 2024 stands at USD 3.4 billion, with a robust compound annual growth rate (CAGR) of 13.2% projected from 2025 to 2033. This significant growth trajectory is underpinned by the rising demand for digital transformation in the insurance sector and the increasing need for personalized customer engagement strategies. By 2033, the market is forecasted to reach approximately USD 10.3 billion, as insurers worldwide intensify their focus on leveraging advanced technologies to enhance customer loyalty and lifetime value. As per our latest research, the surge in adoption of AI-powered analytics, omnichannel communication platforms, and data-driven insights is fueling the rapid expansion of customer retention solutions in the insurance industry.

One of the primary growth factors driving the Customer Retention Solutions for Insurers market is the escalating competition within the insurance landscape. As policyholders become increasingly discerning and digital-savvy, insurers are compelled to adopt sophisticated retention solutions to differentiate themselves and foster long-term relationships. The proliferation of insurtech startups and digital-first insurers has elevated customer expectations for seamless, personalized experiences across every touchpoint. In response, established insurers are investing heavily in customer retention platforms that integrate predictive analytics, behavioral segmentation, and automated engagement tools. These technologies empower insurers to proactively address customer needs, anticipate churn risks, and deliver timely, relevant offers, thereby significantly reducing policyholder attrition rates and improving overall profitability.

Another crucial factor contributing to market growth is the widespread adoption of cloud-based solutions and scalable software-as-a-service (SaaS) models. Cloud deployment offers insurers unparalleled flexibility, cost efficiency, and rapid deployment capabilities, enabling them to swiftly adapt to evolving customer preferences and regulatory requirements. The transition from legacy systems to modern, cloud-native customer retention platforms allows insurers to centralize customer data, streamline workflows, and orchestrate omnichannel engagement strategies with ease. Furthermore, cloud-based solutions facilitate seamless integration with third-party applications and data sources, enhancing the richness and accuracy of customer insights. This technological shift is particularly beneficial for small and medium-sized enterprises (SMEs) in the insurance sector, which often lack the resources to maintain complex on-premises infrastructure.

Regulatory pressures and evolving compliance standards are also acting as catalysts for the adoption of advanced customer retention solutions in the insurance industry. With stringent data privacy regulations such as GDPR and CCPA coming into force, insurers are required to implement robust data governance and consent management practices. Modern customer retention platforms are equipped with built-in compliance features, enabling insurers to securely manage customer data, track consent preferences, and maintain audit trails. This not only mitigates regulatory risks but also enhances customer trust and transparency, which are critical to long-term retention. Additionally, the growing emphasis on environmental, social, and governance (ESG) criteria is prompting insurers to adopt ethical and sustainable customer engagement practices, further driving the demand for innovative retention solutions.

From a regional perspective, North America currently dominates the Customer Retention Solutions for Insurers market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading insurance providers, advanced digital infrastructure, and a mature regulatory environment in North America have accelerated the adoption of customer retention technologies. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, rising insurance penetration, and increasing investments in insurtech. In emerging markets across Latin America and the Middle East & Africa, insurers are gradually embracing customer retention solutions to address the unique challenges posed by diverse customer bases and evolving regulatory landscapes. The global outlook for the market remains highl

According to Cognitive Market Research, the global Loyalty Management market size was USD 25.4 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 17.3% from 2024 to 2031. Market Dynamics of Loyalty Management Market

Key Drivers for Loyalty Management Market

Growing Application of Artificial Intelligence for Innovative Solutions-One of the main reasons the Loyalty Management market is increasing the application of artificial intelligence (AI) for innovative solutions. AI-powered tools enable companies to analyze vast amounts of customer data, predict behaviors, and personalize rewards programs more effectively. These solutions enhance customer engagement by delivering tailored experiences and offers, thereby increasing satisfaction and retention rates. AI also automates and optimizes various loyalty program processes, reducing operational costs and improving efficiency. Additionally, AI-driven insights help in detecting and preventing fraudulent activities, ensuring the integrity of loyalty programs.
The increasing customer preference for personalized solutions to drive the Loyalty Management market's expansion in the years ahead.

Key Restraints for Loyalty Management Market

Stringent Government regulations pose a serious threat to the Loyalty Management industry.
The market also faces significant difficulties related to data security and privacy.

Introduction of the Loyalty Management Market

The Loyalty Management Market encompasses systems and strategies designed to retain customers by rewarding their repeat business, fostering brand loyalty, and encouraging customer engagement. This market is segmented by type, deployment, organization size, end-user industry, and region. Types include customer loyalty, employee retention, and channel loyalty management. Deployment can be cloud-based or on-premises, catering to different organizational needs. Organizations of varying sizes, from SMEs to large enterprises, utilize these solutions. End-user industries span retail, hospitality, BFSI, healthcare, and IT & telecom, each with unique loyalty program requirements. Geographically, the market covers North America, Europe, Asia Pacific, Latin America, and MEA, each exhibiting distinct growth drivers and adoption trends. As businesses increasingly recognize the value of customer retention over acquisition, the loyalty management market is poised for significant growth, driven by advancements in technology and the rising importance of personalized customer experiences.

The statistic shows the share of returned customers of meal kit services in the United States as of 2017, by number of months since first purchase. Since signing up in January 2016, approximately ** percent of consumers continued to purchase meal kits after *** year.

Discover the booming Customer Success Services market! This comprehensive analysis reveals key trends, growth drivers, and challenges in this multi-billion dollar industry, featuring leading companies and market forecasts through 2033. Learn how to leverage customer success strategies for improved retention and revenue.

In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

Industry-specific customer lifespan benchmarks and retention metrics for business analysis

"Telecom Customer Churn Analysis and Prediction Dataset"

This dataset contains information on customers from a telecommunications company, designed to help identify the key factors that influence customer churn. Churn in the telecom industry refers to customers discontinuing their service, which has significant financial implications for service providers. Understanding why customers leave can help companies improve customer retention strategies, reduce churn rates, and enhance overall customer satisfaction.

Context & Source

The dataset provides real-world insights into telecom customer behavior, covering demographic, account, and usage information. This includes attributes like customer demographics, contract type, payment method, tenure, usage patterns, and whether the customer churned. Each record represents an individual customer, with labeled data indicating whether the customer is active or has churned.

This data is inspired by real-world telecom challenges and was created to support machine learning tasks such as classification, clustering, and exploratory data analysis (EDA). It’s particularly valuable for data scientists interested in predictive modeling for churn, as well as for business analysts working on customer retention strategies.

Potential Uses and Inspiration

This dataset can be used for:

Building predictive models to classify customers as churned or active Analyzing which factors contribute most to churn Designing interventions for at-risk customers Practicing data preprocessing, feature engineering, and visualization skills Whether you’re a beginner in machine learning or an experienced data scientist, this dataset offers opportunities to explore the complexities of customer behavior in the telecom industry and to develop strategies that can help reduce customer churn.

AI-Enhanced Subscription Churn Scoring Market Outlook

According to our latest research, the global AI-Enhanced Subscription Churn Scoring market size reached USD 2.14 billion in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 19.8% from 2025 to 2033, culminating in a forecasted value of USD 10.32 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of AI-powered predictive analytics across subscription-based businesses seeking to reduce customer attrition and optimize lifetime value.

The primary growth factor fueling the AI-Enhanced Subscription Churn Scoring market is the surging demand among enterprises to proactively identify and retain at-risk subscribers. In todayÂ’s highly competitive landscape, subscription-based models are prevalent across industries such as telecommunications, media, e-commerce, and SaaS. These sectors are increasingly leveraging AI-driven churn scoring solutions to analyze customer behavior, transaction history, and engagement patterns, enabling them to implement targeted retention strategies. The integration of machine learning and advanced analytics has significantly improved the accuracy and timeliness of churn predictions, empowering companies to act before a customer decides to leave. As a result, organizations are witnessing substantial improvements in customer retention rates and overall profitability, further propelling the adoption of AI-enhanced churn scoring solutions.

Another critical driver is the rapid digital transformation and the proliferation of data-driven decision-making within enterprises of all sizes. With the exponential increase in data generated by digital touchpoints, companies are seeking sophisticated tools that can process vast datasets in real time and extract actionable insights. AI-enhanced churn scoring platforms offer the ability to synthesize structured and unstructured data, including social media interactions, customer feedback, and usage trends, to create comprehensive risk profiles. This holistic approach enables businesses to personalize engagement, refine product offerings, and deliver superior customer experiences. The integration of these platforms into existing CRM and marketing automation systems further streamlines operations and maximizes the return on investment, making AI-enhanced churn scoring indispensable for modern subscription businesses.

Additionally, the growing emphasis on customer-centric business models and the rising cost of customer acquisition are compelling companies to focus more on retention strategies. AI-enhanced churn scoring tools provide a cost-effective solution by identifying high-risk segments and enabling targeted interventions, which are often more economical than acquiring new customers. Furthermore, advancements in cloud computing and the availability of scalable AI solutions have democratized access to sophisticated churn scoring technologies, allowing small and medium enterprises to compete on an equal footing with larger organizations. These trends collectively contribute to the sustained growth and widespread adoption of AI-enhanced churn scoring solutions across diverse industry verticals.

In the realm of customer retention, Churn Root Cause Analysis AI is becoming a pivotal tool for businesses aiming to understand the underlying factors leading to customer attrition. By leveraging AI technologies, companies can delve deeper into the behavioral patterns and transactional data of their subscribers to pinpoint specific triggers of churn. This analytical approach not only aids in identifying at-risk customers but also empowers organizations to devise targeted strategies that address these root causes. As a result, businesses are not only able to enhance their retention efforts but also improve overall customer satisfaction by proactively resolving issues that might otherwise lead to churn. The integration of Churn Root Cause Analysis AI into existing systems allows for a more nuanced understanding of customer dynamics, ultimately driving more effective and personalized retention strategies.

From a regional perspective, North America is currently leading the AI-Enhanced Subscription Churn Scoring market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of major technology provider

The Customer Success Software market has emerged as an essential component for businesses seeking to enhance customer relationships and improve retention rates in an increasingly competitive landscape. This software is designed to help organizations proactively manage customer interactions, ensuring that clients der