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 a survey about the music streaming industry in the Middle East and North Africa (MENA) region in the first half of 2020, 34 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.

In 2024, E.Leclerc scored the highest on customer loyalty among selected online grocery retailers in France, with 63 percent rate of retention of customers purchasing via its online channels. Dutch-based newcomer Picnic also featured in the ranking, scoring 47 percent on customer retention in France.

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 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

This statistic shows the customer retention share of the leading meal kit companies in the United States as of 2017. As of 2017, Blue Apron retained 15 percent of their subscribers a year after the first purchase.

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Customer Loyalty Management', including concepts like Customer Retention and Satisfaction & Loyalty programs. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Customer Loyalty dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "loyalty management" + "customer loyalty" + "customer retention" + "loyalty management marketing". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Loyalty Management, Customer Loyalty, Satisfaction and Loyalty, Customer Retention. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Customer Loyalty-related keywords [("loyalty management" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Loyalty Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Loyalty Management (2004); Loyalty Management Tools (2006, 2008); Satisfaction and Loyalty Management (2010, 2012, 2014). Note: Not reported before 2004 or after 2014. Processing: Semantic Grouping: Data points across related names treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years (same names/years as Usability). Note: Not reported before 2004 or after 2014. Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Customer Loyalty dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

The Customer Loyalty System market has emerged as a pivotal component for businesses seeking to enhance customer retention and drive sales. These systems, which include a variety of programs and technologies designed to incentivize repeat purchases, facilitate deeper customer engagement, and nurture brand loyalty, a

The Customer Loyalty Management Software market is an increasingly essential segment within the broader realm of customer relationship management (CRM), designed to enhance customer retention and foster brand loyalty. Businesses across various industries, from retail and hospitality to e-commerce, leverage these sop

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.

The Customer Loyalty Management System Software market has witnessed significant growth in recent years, driven by the increasing recognition of customer retention as a vital component of business success. Comprising tools and platforms that enable organizations to create, manage, and analyze loyalty programs, this

According to a survey about the music streaming industry in the Middle East and North Africa (MENA) region in the first half of 2020, 34 percent of respondents in the region who were Apple music users might switch to another music streaming platform. Anghami had the highest brand loyalty among music streaming brands in the region.

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.

The loyalty management market size is projected to grow from USD 13.63 billion in 2025 to USD 59.71 billion by 2035, representing a CAGR of 15.91% during the forecast period till 2035

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

According to a survey conducted in March 2023, nearly two-thirds of French online shoppers cited affordable prices as the top factor encouraging them to buy again from a certain e-commerce retailer. Delivery conditions were also a main driver of customer retention for around **** percent of respondents. In the first quarter of 2023, fast-moving consumer goods (FMCG) was the e-commerce category with the highest customer retention rate in France, at ** percent.

Analysis of ‘Client churn rate in Telecom sector’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sagnikpatra/edadata on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs."

Content The Orange Telecom's Churn Dataset, which consists of cleaned customer activity data (features), along with a churn label specifying whether a customer canceled the subscription, will be used to develop predictive models. Two datasets are made available here: The churn-80 and churn-20 datasets can be downloaded.

The two sets are from the same batch, but have been split by an 80/20 ratio. As more data is often desirable for developing ML models, let's use the larger set (that is, churn-80) for training and cross-validation purposes, and the smaller set (that is, churn-20) for final testing and model performance evaluation.

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

--- Original source retains full ownership of the source dataset ---

Customer Loyalty Program Software Market size was valued at USD 4.1 Billion in 2024 and is projected to reach USD 10.97 Billion by 2032, growing at a CAGR of 13.07% from 2026 to 2032.

Businesses are increasingly recognizing that maintaining existing clients is more cost-effective than obtaining new ones. Customer Loyalty Program Software offers an organized strategy for rewarding repeat customers, and increasing customer happiness, loyalty, and long-term involvement. Companies dramatically boost the possibility of client repeat purchases by providing targeted rewards and personalized experiences, hence driving market development.

The capacity to collect and evaluate client data is critical when developing an effective marketing strategy. Customer Loyalty Program Software enables organizations to gain deep insights into their customers' behavior, preferences, and purchasing history. This data enables the optimization of marketing activities and the creation of highly personalized consumer experiences, fueling demand for such software as businesses look to use data to achieve a competitive advantage.

Furthermore, advanced technologies such as artificial intelligence, machine learning, and blockchain have been integrated into Customer Loyalty Program Software to improve its efficiency and security. These technologies allow for the automation of rewards distribution, fraud detection, and the construction of individualized customer experiences. Furthermore, the ability to effortlessly link with other company systems (such as CRM, ERP, and e-commerce platforms) improves the operational efficiency of loyalty programs, driving market growth.