Introduction

Customer Retention Statistics: Customer retention is the art and science of maintaining the attention of existing customers and persuading them to buy again without having to suffer the glaring cost of reaching out to fresh markets. Shifting from sales to nurturing relationships, loyalty programs, and personalised experiences to prevent customer churn was the main strategy carried out in 2024 by businesses worldwide.

This article lays down vital Customer Retention statistics collected from credible sources, showing retention rates per industry, financial benefits of holding onto customers, the role of fast service, and data-driven retention solutions.

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.

A collection of statistics and survey data on customer retention strategies and challenges in the United Kingdom for the year 2025.

According to the source, in the first quarter of 2023, Amazon Prime had a 30-day trial after which ** percent of users subscribed to the service. The conversion rate has increased, as it was ** percent in the same period of 2022. Moreover, ** percent of Amazon Prime members renewed their membership for a year, and ** percent renewed it for a second year over the first three months of 2023.

In December 2023, Comfy recorded a user retention rate of around **** percent. Comfy is a famous domestic cosmetic brand in China.

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

According to a survey about the music streaming industry in the Middle East and North Africa (MENA) region in the first half of 2020, ** percent of respondents in the region who were Spotify users might switch to another music streaming platform. Anghami had the highest brand loyalty among music streaming brands in the region.

Introduction

Customer Service Statistics: Customer service is a crucial component of business operations, significantly affecting customer retention and revenue generation. Research shows that 88% of customers are more likely to make repeat purchases when they receive excellent customer service. On the other hand, U.S. companies lose approximately USD 75 billion each year due to poor customer service.

Consumer expectations have evolved; 80% of consumers believe that the experience a company provides is just as important as its products and services. Additionally, 45% of consumers expect their issues to be resolved during their first interaction.

The use of artificial intelligence (AI) in customer service is increasing, with 56% of companies currently employing AI-powered chatbots to improve their operations. Projections indicate that by 2025, 85% of customer interactions will be managed without human intervention, thanks to advancements in AI. However, the human touch remains essential, as 80% of consumers expect to interact with a live agent when they contact a company.

These statistics illustrate the vital role of exceptional customer service in building loyalty and driving business success.

The Customer Retention Software market has emerged as a pivotal segment in the realm of customer relationship management, offering businesses essential tools to enhance customer loyalty, satisfaction, and engagement. In today's competitive landscape, retaining existing customers has proven to be more cost-effective

Customer Retention Software Market: An Overview The global customer retention software market is valued at USD 27.5 billion in 2023, projected to reach USD 94.7 billion by 2033, exhibiting a CAGR of 15.0% during the forecast period (2023-2033). The market growth is driven by the increasing adoption of cloud-based solutions and the growing need for businesses to retain customers and drive loyalty. Additionally, technological advancements, such as AI and Machine Learning (ML), are enhancing the capabilities of customer retention software, making it a more valuable tool for businesses. Key Market Dynamics: Drivers, Restraints, Trends The key drivers of the customer retention software market include the growing adoption of cloud-based solutions, which offer scalability and cost-effectiveness. The increasing need for businesses to retain customers and drive loyalty has also contributed to the market growth. Moreover, technological advancements, such as AI and ML, are enhancing the capabilities of customer retention software, making it more valuable for businesses. The adoption of AI and ML enables businesses to automate tasks, improve customer segmentation, and personalize marketing campaigns. However, high implementation and maintenance costs, and concerns related to data security and privacy pose challenges to the market growth. This comprehensive market report provides an in-depth analysis of the global customer retention software industry, offering valuable insights into its market dynamics, trends, and growth drivers. With a focus on the increasing importance of customer retention in today's competitive business landscape, this report provides actionable recommendations for businesses looking to enhance their retention strategies and drive long-term profitability.

In December 2023, Proya recorded a user retention rate of around **** percent. Proya's user retention reached the lowest value of the year in November, since many users left due to the complicated sales promotion conditions during the Singles' Day sales. Proya is one of the leading domestic cosmetic brands in China.

The Proactive Customer Retention Software market has emerged as an essential component for businesses focused on enhancing customer loyalty and reducing churn rates. This innovative software empowers organizations to anticipate customer needs through data analysis and predictive modeling by identifying at-risk custo

Analyzing Customer Churn and Its Impact on Revenue

Exploring Patterns and Trends

By [source]

About this dataset

This dataset contains customer data from multiple sources that can be used to predict customer churn and analyze its effect on revenue. We'll use this data to gain insights into customer behavior, such as when customers are likely to churn, how their behavior affects revenue and what patterns of behavior can help us better understand customers. This dataset features several different attributes for each customer: their unique identifier, total charges paid over time, contract information and more. Additionally, we can use the predictive analytical models based on this data to identify at-risk customers that may be more likely to churn in the near future. By gaining deep insight into which customers are most likely to leave and why they are leaving, businesses will be better equipped with tools necessary for taking proactive measures against potential revenue losses due to customer churn

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How to use the dataset

This dataset is an excellent tool for businesses to understand what factors are associated with customer churn and its impact on revenue. It can provide insights into which customers are most likely to leave, and how companies can prevent them from leaving.

To use this dataset, here are the steps businesses can follow: 1. Understand each of the data points available in the dataset and what they represent - For example, CustomerID is a unique identifier for each customer, Churn indicates if a customer has left the company or not, gender denotes what gender the customer is etc. 2. Analyze any trends or patterns in your data – Look out for correlations between different variables like OnlineSecurity usage and Churn rate or MonthlyCharges and tenure to determine how these variables affect customers’ decisions to stay with a company or leave it etc. 3. Use machine learning models on your dataset – Utilize supervised learning algorithms such as logistic regression on this dataset to determine which variable most closely correlates with loyalty of customers i.e., which variable will decide whether a particular customer will stay with your company or not?
4. Explore various ways of increasing retention rates – Think about ways you could incentivize customers who might be considering leaving their current provider (for example, offer discounts, free trials etc.). You could try different strategies like A/B testing too see which incentive works best for churn prevention/retention rate increase etc. 5.. Test out strategies before implementing them - Once you have decided on incentives that might work well, run small scale tests to check if they generate desired results before investing resources into full rollout programs .The systems based on machine learning algorithms allows you to quickly test assumptions efficiently without large investments in time & money prior committing these changes fully operational processes

Research Ideas

• Using customer data to identify and target customers who are at a high risk of churning to counter this effect with relevant customer service initiatives.
• Analyzing the effects of promotional campaigns and loyalty programs on customer retention rates and overall revenue.
• Machine learning models that predict future chances of customer churn which can be used by businesses to improve strategies for better retention & profitability

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: dataset1.csv | Column name | Description | |:---------------------|:-----------------------------------------------------------------| | CustomerID | Unique identifier for each customer. (Integer) | | Churn | Whether or not the customer has churned. (Boolean) | | gender | Gender of the customer. (String) | | SeniorCitizen | Whether or not the customer is a senior citizen. (Boolean) ...

CE Vision USA is the premier data set tracking consumer spending on credit and debit cards. Clients use CE Vision retail & ecommerce customer retention data for CPG, grocery, restaurant, and food delivery insights by industry, brand, and psychographics to inform business strategy.

Exploring Customer Retention Dynamics: A Comparative Investigation of Factors Affecting Customer Retention in the Banking Sector Using Mediation-Moderation Approach Datasets and Questionnaire Using SmartPLS-SEM.

Customer Churn Software Market Outlook

The global customer churn software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 4.8 billion by 2032, growing at a CAGR of 13.7% during the forecast period. This robust growth is driven by several factors, including the increasing importance of customer retention in competitive markets, advancements in AI and machine learning technologies, and the growing adoption of digital transformation initiatives across industries.

One of the primary growth factors propelling the customer churn software market is the increasing emphasis on customer satisfaction and retention. In today's highly competitive business environment, retaining existing customers is more cost-effective than acquiring new ones. Companies are realizing the value of customer loyalty, and as a result, they are investing heavily in tools that can help predict and mitigate churn. Customer churn software offers advanced analytics and predictive capabilities, enabling organizations to identify at-risk customers and take proactive measures to retain them.

Another significant driver is the advancement in artificial intelligence (AI) and machine learning technologies. These technologies have revolutionized the way customer data is analyzed and interpreted. AI-powered customer churn software can process vast amounts of data from multiple sources, identify patterns, and generate actionable insights. This ability to leverage big data and predictive analytics is crucial for businesses aiming to stay ahead of the competition. As AI and machine learning continue to evolve, the effectiveness and efficiency of customer churn software are expected to improve further.

The increasing adoption of digital transformation initiatives across various industries is also contributing to the market growth. As businesses undergo digital transformation, they generate enormous amounts of data related to customer behavior, preferences, and interactions. Customer churn software helps organizations make sense of this data, enabling them to develop personalized strategies to enhance customer experience and loyalty. The shift towards data-driven decision-making is compelling companies to invest in advanced analytics solutions, thereby driving the demand for customer churn software.

From a regional perspective, North America holds a significant share of the customer churn software market, driven by the presence of major technology companies and the early adoption of advanced analytics solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors such as the rapid digitalization of economies, increasing investments in AI and machine learning, and the growing focus on customer-centric strategies in emerging markets are fueling the demand for customer churn software in this region.

Component Analysis

The customer churn software market is segmented into two primary components: software and services. The software segment includes the actual customer churn solutions, while the services segment encompasses implementation, training, support, and consulting services. The software segment is expected to dominate the market due to the high demand for advanced analytics and predictive tools. Companies across various industries are increasingly adopting software solutions to gain insights into customer behavior and predict churn. The software segment's growth is further supported by continuous advancements in AI and machine learning technologies, which enhance the capabilities of customer churn solutions.

The services segment, although smaller in comparison to the software segment, plays a crucial role in the market. Services such as implementation and training ensure that organizations can effectively deploy and utilize customer churn software. Support and consulting services are equally important, as they help companies optimize their software usage and develop customized strategies to address specific churn-related challenges. The demand for these services is expected to grow in tandem with the adoption of customer churn software, as businesses seek to maximize their return on investment and achieve better customer retention outcomes.

Moreover, the integration of customer churn software with existing CRM systems and other business applications is becoming increasingly important. This integration enables a seamless flow of data and enhances the overall efficiency of customer retention efforts. As a result, solutions that offer robust integration capa

Customer Retention with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used for customer retention purposes, such as performing a shopper retention analysis over time for a specific company.

Inquire about a CE subscription to perform more complex, near real-time competitive analysis functions on public tickers and private brands like: • Choose a pair of merchants to determine spend overlap % between them by period (yearly, quarterly, monthly) • Explore cross-shop history within subindustry and market share (updated weekly)

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Competitive Analysis

Problem A grocery delivery brand needs to assess overall company performance, including customer acquisition and retention levels relative to key competitors.

Solution Consumer Edge transaction data can uncover performance over time and help companies understand key drivers of retention: • By geography and demographics • By channel • By shop date

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on customer retention for company-wide reporting • Reduce investment in underperforming channels, both online and offline • Determine demo and geo drivers of retention for refined targeting • Analyze customer acquisition campaigns driving retention and plan accordingly

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period • Churn • Cross-Shop • Average Ticket Buckets

This dataset provides a comprehensive view of insurance policyholders, their demographic details, policy information, claims history, and churn status for both life and auto insurance products. It is designed to support predictive modeling of customer attrition, enabling insurers to identify at-risk customers and develop targeted retention strategies. The inclusion of satisfaction scores, contact history, and churn reasons makes it ideal for advanced analytics and customer experience optimization.

This dataset contains raw, unprocessed data files pertaining to the management tool group focused on 'Customer Loyalty Management', including concepts like Customer Retention and Satisfaction & Loyalty programs. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "loyalty management" + "customer loyalty" + "customer retention" + "loyalty management marketing" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Loyalty Management, Customer Loyalty, Satisfaction and Loyalty, Customer Retention (Note: Comma used as '+' per source link structure) Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("loyalty management" OR "customer loyalty" OR "satisfaction and loyalty" OR "customer retention") AND ("marketing" OR "management" OR "strategy" OR "relationship" OR "program" OR "approach") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Loyalty Management (2004); Loyalty Management Tools (2006, 2008); Satisfaction and Loyalty Management (2010, 2012, 2014). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2003, 2007, 2009, 2011, 2013, 2015). Note: Tool potentially not surveyed before 2004 or after 2014 under these specific names. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Loyalty Management (2004); Loyalty Management Tools (2006, 2008); Satisfaction and Loyalty Management (2010, 2012, 2014). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2003, 2007, 2009, 2011, 2013, 2015). Note: Tool potentially not surveyed before 2004 or after 2014 under these specific names. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

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.