Customer Personality Analysis involves a thorough examination of a company's optimal customer profiles. This analysis facilitates a deeper understanding of customers, enabling businesses to tailor products to meet the distinct needs, behaviors, and concerns of various customer types.

By conducting a Customer Personality Analysis, businesses can refine their products based on the preferences of specific customer segments. Rather than allocating resources to market a new product to the entire customer database, companies can identify the segments most likely to be interested in the product. Subsequently, targeted marketing efforts can be directed toward those particular segments, optimizing resource utilization and increasing the likelihood of successful product adoption.

Details of Features are as below:

• Id: Unique identifier for each individual in the dataset.
• Year_Birth: The birth year of the individual.
• Education: The highest level of education attained by the individual.
• Marital_Status: The marital status of the individual.
• Income: The annual income of the individual.
• Kidhome: The number of young children in the household.
• Teenhome: The number of teenagers in the household.
• Dt_Customer: The date when the customer was first enrolled or became a part of the company's database.
• Recency: The number of days since the last purchase or interaction.
• MntWines: The amount spent on wines.
• MntFruits: The amount spent on fruits.
• MntMeatProducts: The amount spent on meat products.
• MntFishProducts: The amount spent on fish products.
• MntSweetProducts: The amount spent on sweet products.
• MntGoldProds: The amount spent on gold products.
• NumDealsPurchases: The number of purchases made with a discount or as part of a deal.
• NumWebPurchases: The number of purchases made through the company's website.
• NumCatalogPurchases: The number of purchases made through catalogs.
• NumStorePurchases: The number of purchases made in physical stores.
• NumWebVisitsMonth: The number of visits to the company's website in a month.
• AcceptedCmp3: Binary indicator (1 or 0) whether the individual accepted the third marketing campaign.
• AcceptedCmp4: Binary indicator (1 or 0) whether the individual accepted the fourth marketing campaign.
• AcceptedCmp5: Binary indicator (1 or 0) whether the individual accepted the fifth marketing campaign.
• AcceptedCmp1: Binary indicator (1 or 0) whether the individual accepted the first marketing campaign.
• AcceptedCmp2: Binary indicator (1 or 0) whether the individual accepted the second marketing campaign.
• Complain: Binary indicator (1 or 0) whether the individual has made a complaint.
• Z_CostContact: A constant cost associated with contacting a customer.
• Z_Revenue: A constant revenue associated with a successful campaign response.
• Response: Binary indicator (1 or 0) whether the individual responded to the marketing campaign.

This dataset contains raw, unprocessed data files pertaining to the management tool 'Customer Segmentation', including the closely related concept of Market Segmentation. 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: "customer segmentation" + "market segmentation" + "customer segmentation 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: Customer Segmentation + Market Segmentation 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: ("customer segmentation" OR "market segmentation") AND ("marketing" OR "strategy" OR "management" OR "targeting" OR "analysis" OR "approach" OR "practice") 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: Customer Segmentation (1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2017). 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: 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool not included in the 2022 survey data. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Customer Segmentation (1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2017). 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: 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool not included in the 2022 survey data. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268. 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.

Customer Segmentation with K-Means

Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data. Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retain those customers. Another group might include customers from non-profit organizations. And so on.

Dataset donwloaded from - IBM Object Storage

dataset download link : https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv

This dataset provides processed and normalized/standardized indices for the management tool 'Customer Segmentation', including the closely related concept of Market Segmentation. 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 Segmentation 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 "customer segmentation" + "market segmentation" + "customer segmentation 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 Customer Segmentation + Market Segmentation. 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 Segmentation-related keywords [("customer segmentation" 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 (Segmentation 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: Customer Segmentation (1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2017). Note: Not reported in 2022 survey data. Processing: Normalization: Original usability percentages 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: Customer Segmentation (1999-2017). Note: Not reported in 2022 survey data. Processing: 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 Segmentation dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann.

The dataset utilized comes from a german bank in 2016 collected by Professor Hoffman of the University of Califonia.

In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes.

The original dataset required extensive cleaning and variable selection I due to its complicated system of categories and symbols. Several columns are simply ignored, because they were viewed as not important or their descriptions are obscure. The selected attributes are:

• Age (numeric)
• Sex (text: male, female)
• Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)
• Housing (text: own, rent, or free)
• Saving accounts (text - little, moderate, quite rich, rich)
• Checking account (numeric, in DM - Deutsch Mark)
• Credit amount (numeric, in DM)
• Duration (numeric, in month)
• Purpose (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others).

The objective of this analysis is to segment the German bank's customers based on the various factors (variables) available in their database.

The library makes use of the following packages:

• pandas - to manipulate data frames
• numpy - providing linear algebra
• seaborm - to create visualizations
• matplotlib - basic tools for visualizations
• scikit-learn - machine learning library

Conclusion.

The analysis found that the most optimal clusters were 4 as explained below:

Cluster 0 – high mean of credit amount, long duration, younger customers

Cluster 1 – low mean of credit amount, short duration, younger customers

Cluster 2 - low mean of credit amount, short duration, older customers

Cluster 3 - high mean of credit amount, middle-time duration, older customers

Segmenting bank customers through clustering techniques offers significant benefits for both the bank itself and its various stakeholders. Here are some key advantages:

For Banks:

• Improved Customer Targeting and Marketing: Clustering allows banks to identify distinct customer segments with similar characteristics and needs. This enables them to tailor marketing campaigns and product offerings to specific segments, resulting in greater effectiveness and efficiency.
• Enhanced Customer Relationship Management (CRM): By understanding customer segments better, banks can personalize their interactions and communications, fostering stronger relationships and improving customer satisfaction.
• Risk Management: Customer segmentation can help identify high-risk segments, allowing banks to implement strategies to mitigate potential risks, such as fraud or credit defaults.
• Resource Optimization: Banks can allocate resources, such as personnel and marketing budgets, more efficiently by directing them towards segments with the highest potential for profitability.

• New Product Development: By analyzing the needs and preferences of different segments, banks can develop new products and services that cater to their specific requirements, increasing customer loyalty and driving revenue growth.

  For Stakeholders:

• Improved Customer Experience: Segmented communication and personalized offerings lead to a more satisfying and relevant experience for customers, boosting overall satisfaction and trust in the bank.

• Increased Value Perception: By providing products and services aligned with their needs, customers perceive greater value from the bank's offerings, leading to strengthened relationships and increased loyalty.

• Enhanced Financial Inclusion: Customer segmentation can help banks identify underserved segments and develop strategies to offer them tailored financial products and services, promoting greater financial inclusion.

• Improved Regulatory Compliance: By understanding customer behavior and risk profiles better, banks can better comply with regulations and mitigate potential regulatory risks.

  Overall, customer segmentation via clustering empowers banks to make data-driven decisions, optimize their operations, and deliver a more personalized and satisfying experience for their customers. This ultimately leads to increased profitability, stronger stakeholder relationships, and a competitive advantage in the market.

Some additional examples of how customer segmentation can benefit other stakeholders:

• Investors: By analyzing the performance of different customer segments, investors can gain valuable insights into the bank's future growth potential and make informed investment decisions.
• Regulators: Customer segmentation can help regulators identify systemic risks within the financial system and develop targeted policies to maintain financial stability.
• Employees: By understanding the needs an...

Customer Segmentation Market Outlook 2032

The global customer segmentation market size was USD XX Billion in 2023 and is likely to reach USD XX Billion by 2032, expanding at a CAGR of XX % during 2024–2032. The market growth is attributed to the increasing demand for personalized marketing strategies and targeted customer engagement.



Increasing globalization and digitalization is projected to drive the market in the assessment year. Businesses are leveraging segmentation techniques to better understand their diverse customer base and tailor marketing strategies accordingly. One growing trend is the use of advanced analytics and machine learning algorithms to segment customers based on intricate criteria such as behavior, preferences, and purchase history.

Growing competition in the marketplace is a significant factor fueling the market. Businesses are increasingly relying on segmentation to gain a competitive edge by identifying and targeting niche market segments. Moreover, with the rise of e-commerce and digital marketing channels, there is a rising demand for segmentation solutions that optimizes marketing strategies.

Impact of Artificial Intelligence (AI) in Customer Segmentation Market

The use of artificial intelligence is revolutionizing the customer segmentation market by enabling businesses to unlock deeper insights and enhance targeting precision. AI-powered algorithms analyze vast amounts of customer data with unprecedented speed and accuracy, allowing businesses to identify subtle patterns and correlations that traditional segmentation methods may overlook. By leveraging AI, companies segment their customer base effectively based on diverse criteria such as behavior, preferences, and purchase history.<

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Customer Data Platform Market Size 2024-2028

The customer data platform market size is forecast to increase by USD 19.02 billion at a CAGR of 32.12% between 2023 and 2028.

The customer data platform (CDP) market is experiencing significant growth due to several key trends. The increasing demand for personalized customer services in various industries, particularly e-commerce retail, is driving market growth. This trend is being fueled by the rising preference for omnichannel platforms that enable seamless customer interactions across multiple touchpoints. Additionally, the need to address customer data privacy concerns is another major factor contributing to the market's growth.
As businesses strive to provide more personalized experiences to their customers while ensuring data security, CDPs and workforce analytics are becoming an essential tool for managing and activating customer data in real time. This CDP market analysis report provides a comprehensive examination of these trends and other growth factors, offering valuable insights for businesses looking to leverage CDPs to enhance their customer engagement strategies.

What will be the Size of the Customer Data Platform Market During the Forecast Period?

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The customer data platform (CDP) market is experiencing significant growth due to the increasing importance of customer intelligence for delivering omnichannel experiences. Businesses seek to understand their customers across multiple channels and touchpoints, requiring the ability to handle large volumes of complex data. CDP solutions enable data unification and identity resolution, ensuring accurate and consistent customer profiles. Data governance and privacy laws are driving the need for robust data protection and security measures, including data breach prevention and compliance with regulations such as GDPR and CCPA.
Additionally, AI and machine learning are being integrated into CDPs to enhance data analytics capabilities, providing valuable insights for industries like healthcare, telecom, travel and hospitality, and advertising.
The customer data platform market is evolving with AI-powered CDP solutions enhancing real-time data processing, customer data integration, and omnichannel marketing. Businesses focus on data privacy compliance and first-party data management to drive predictive analytics, customer segmentation, and personalized marketing. Cloud-based CDP adoption supports customer journey analytics, CDP for e-commerce, and cross-channel data activation. Data monetization strategies, identity resolution, and enterprise CDP solutions fuel CDP market growth, enabling data-driven customer insights and customer retention strategies.
Big data and real-time data processing are essential features, enabling businesses to make informed decisions and respond quickly to customer needs.

How is this Customer Data Platform Industry segmented and which is the largest segment?

The customer data platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud based


End-user

  Large enterprises
  Small and medium size enterprises


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The on-premises the market is experiencing substantial growth due to its ability to process and personalize customer data while maintaining data security within an organization's data centers or servers. On-premises CDPs offer customizable solutions tailored to specific business needs and unique data processing workflows, which may not be available in cloud-based alternatives. However, the need to upgrade hardware for data scalability is a consideration for on-premises CDPs. Key features of on-premises CDPs include data unification, identity resolution, data governance, data privacy, and data security. These platforms enable organizations to comply with data privacy laws, protect against data breaches, and address consumer concerns.

On-premises CDPs are particularly valuable for industries with large data volumes and complexities, such as advertising, healthcare services, telecom, media and entertainment, retail, and travel and hospitality. Integration with mobile devices, Short Message Service, and communication channels is essential for providing a seamless omnichannel experience. Machine learning and natural language processing technologies enhance data analysis and personalization capabilities. Cloud-based technology offers flexibility and cost savings, but on-premises CDP

Demografy is a privacy by design customer demographics prediction AI platform.

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Use cases: - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You can provide even masked last names keeping personal data in-house. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

Boost your marketing with Success.ai’s Consumer Marketing Data API. Access detailed demographic, behavioral, and purchasing data to craft targeted campaigns that resonate—best price guaranteed!

Geodemographic Segmentation Data from Caliper Corporation contain demographic data in a way that is easy to visualize and interpret. We provide 8 segments and 32 subsegments for exploring the demographic makeup of neighborhoods across the country.

Dataset

This dataset was created by HiremathAmits

Released under Apache 2.0

Contents

The Healthcare Customer Data Platform Market report segments the industry into By Component (Software, Services), By Deployment Mode (On-Premise, Cloud-Based), By Organization Size (Large Enterprises, Small & Medium-Sized Enterprises), By Application (Personalized Recommendations, Predictive Analytics, and more.), and By Geography (North America, Europe, Asia-Pacific, Middle East and Africa, South America).

The global Customer Intelligence Tools market is experiencing robust growth, projected to reach $882.8 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 7.0% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of digital technologies across industries is driving the demand for sophisticated tools that provide actionable insights from customer data. Businesses are recognizing the critical need to understand customer behavior, preferences, and needs to personalize experiences, improve customer satisfaction, and ultimately drive revenue growth. The rise of big data and advanced analytics capabilities, coupled with the increasing affordability of these tools, is further accelerating market penetration. Furthermore, the growing emphasis on customer-centric strategies and the need for real-time customer feedback analysis are significant contributors to the market's upward trajectory. Segmentation analysis reveals that large enterprises currently dominate the market, leveraging these tools for comprehensive customer relationship management and strategic decision-making. However, the SME segment is exhibiting strong growth potential, indicating a broader adoption across diverse business sizes in the coming years. Key applications include customer experience management, customer data analysis, and feedback analysis, reflecting the diverse functionalities offered by these tools. The market is witnessing several prominent trends, including the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) for advanced analytics and predictive modeling. This allows for more precise customer segmentation, personalized marketing campaigns, and proactive customer service interventions. Cloud-based deployments are also gaining traction, offering scalability, flexibility, and cost-effectiveness. However, challenges remain, such as data privacy concerns, the need for robust data security measures, and the complexity involved in integrating these tools with existing business systems. The competition among established players like Oracle, IBM, and SAP, along with innovative startups, is intense, resulting in continuous product innovation and competitive pricing. Geographic expansion is also underway, with North America currently holding a significant market share, followed by Europe and Asia Pacific, each presenting unique growth opportunities based on regional digital maturity and market dynamics. The forecast period suggests that the market will continue its strong growth, driven by the ongoing need for data-driven customer understanding and engagement.

Dataset

This dataset was created by Anishkaa Choudhary

Contents

Success demands customer-centric models – a shift towards this approach is essential in order to create products that consumers want to buy and brands that consumers want to associate themselves with. Attitudinal traits are rarely mutually exclusive – products or services that cater to a trend crossover offer consumers multiple benefits and will be attractive to a wider range of consumers. Trust is key. A sense of honesty from an insurance provider is not only the most widely sought factor among consumers – regardless of customer group – but also drives among the strongest sources of sentiment. Read More

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Customer Engagement Solutions Market Size 2024-2028

The customer engagement solutions market size is forecast to increase by USD 16.31 billion at a CAGR of 13.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing adoption of e-commerce business models and the growing demand for social interaction. E-commerce businesses recognize the importance of engaging customers effectively to enhance brand loyalty and drive sales. Meanwhile, consumers seek personalized and interactive experiences, fueling the demand for customer engagement solutions. However, market expansion faces challenges. Data security concerns are a major obstacle, as companies must ensure the protection of sensitive customer information. Regulatory hurdles also impact adoption, as organizations navigate complex compliance requirements. Additionally, supply chain inconsistencies temper growth potential, as businesses strive to deliver seamless and reliable customer experiences.
To capitalize on market opportunities, companies must prioritize data security and regulatory compliance. Investment in advanced security technologies and adherence to industry best practices will help build trust with customers. Furthermore, collaboration with regulatory bodies and industry associations can facilitate compliance and mitigate risks. By addressing these challenges effectively, businesses can differentiate themselves in the market and thrive in the evolving customer engagement landscape.

What will be the Size of the Customer Engagement Solutions Market during the forecast period?

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The market is witnessing significant advancements, with the integration of technologies such as digital experience platforms, customer intelligence, and conversational marketing shaping the landscape. Customer lifecycle management and CRM marketing are key focus areas, enabling businesses to optimize the customer journey and improve engagement rates. Headless CMS and content management systems facilitate seamless content delivery, while customer data platforms and engagement analytics provide valuable customer insights. Service and marketing analytics offer actionable data for customer relationship marketing, segmentation, and targeting.
Predictive analytics and sentiment analysis enable businesses to anticipate customer needs and preferences, enhancing CX optimization. Customer engagement strategy hinges on continuous measurement and improvement of engagement metrics, including customer behavior analysis, customer journey optimization, and customer profiling. Engagement scores and customer segmentation are essential indicators of successful customer engagement.

How is this Customer Engagement Solutions Industry segmented?

The customer engagement solutions industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solutions
  Services


Deployment

  Cloud
  On-premises


Size

  SMEs
  Large enterprises
  SMEs
  Large enterprises


Geography

  North America

    US


  Europe

    France
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

Businesses are revolutionizing customer interactions by delivering personalized experiences through various engagement solutions. These solutions, which range from live chat tools to enterprise-level software, enable real-time communication and data-driven insights. User interfaces are designed to be immersive, while customer engagement platforms facilitate two-way communication and feedback collection. Virtual assistants and mobile apps offer convenience, while cloud computing ensures accessibility and scalability. Customer journey mapping and satisfaction are prioritized through tailored communications on various media channels. Social media marketing and sales enablement tools foster brand advocacy and customer loyalty. Digital marketing strategies leverage data analytics and data visualization to enhance customer experience.

Machine learning and artificial intelligence power customer segmentation and predictive analytics. Customer support is streamlined through support portals, knowledge bases, and community forums. Rewards programs and net promoter scores are used to measure and improve customer satisfaction. Customer onboarding and retention are optimized through personalized experiences and education. Customer success is ensured by addressing churn and providing continuous support. Engagement solutions also include email marketing, customer surveys, and user experience design. Big data and customer analytics provide valuable insights for marketing automation and cust

Customer experience analytics platforms typically offer a range of features, including data collection, analysis, reporting, and visualization. Key features include:Data Collection: Collects data from various touchpoints, including websites, mobile apps, social media, and call centers.Data Analysis: Analyzes data using statistical techniques, AI, and ML algorithms to identify patterns and trends.Reporting and Visualization: Presents analyzed data in interactive reports and dashboards, providing insights and recommendations.Customer Segmentation: Segments customers based on demographics, behavior, and preferences.Real-Time Monitoring: Monitors customer interactions in real-time, enabling businesses to identify and resolve issues proactively. Recent developments include: March 2022: Adobe announced a new Customer Journey Analytics feature under Adobe's Experience Cloud. Adobe introduced a new experimentation feature in Journey Analytics that allows businesses to test real-world scenarios and analyze their results to better understand how even minor changes can affect the overall customer journey across their various products. For Adobe's ability to discover customer segments, the Adobe Customer Data Platform [CDP] and Customer Journey Analytics have also been integrated..

The Customer Behavioral Analysis market is experiencing robust growth, driven by the increasing need for businesses to understand and predict customer actions to optimize marketing strategies, enhance customer experiences, and improve operational efficiency. 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 rise of big data and advanced analytics techniques allows businesses to glean deeper insights from customer interactions across diverse touchpoints. Furthermore, the increasing adoption of cloud-based solutions provides scalable and cost-effective access to powerful analytical tools. The proliferation of mobile devices and the growth of e-commerce have significantly increased the volume of available customer data, further driving market demand. Segmentation within the market reveals strong growth across various application areas, including financial services (leveraging behavioral data for fraud detection and personalized offers), retail (optimizing pricing and inventory management), and game entertainment (improving player engagement and monetization). Technological advancements such as artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of customer behavioral analysis platforms, enabling more accurate predictions and personalized experiences. However, certain restraints continue to pose challenges to market growth. Data privacy concerns and the increasing complexity of data regulations are leading to tighter controls on data collection and usage. The need for skilled professionals proficient in data analytics and interpretation is another constraint, creating a talent gap within the industry. Further, the high cost of implementing and maintaining sophisticated analytical platforms can be a barrier to entry for smaller businesses. Nevertheless, the overall market outlook remains positive, driven by continued technological advancements, increasing data availability, and the growing recognition of the strategic value of customer behavioral analysis in achieving sustainable business growth. The competitive landscape is characterized by a mix of established players and emerging startups, leading to innovation and competitive pricing, thus benefiting the market as a whole.

GapMaps Panorama Segmentation Data from Applied Geographic Solutions (AGS) is built on over three decades of experience in the creation and use of geodemographic segmentation systems in the United States and Canada. Building on and integrating the existing suite of AGS modeling and analytical tools, GapMaps Panorama Segmentation Data creates actionable perspective on an increasingly complex and rapidly churning demographic landscape.

GapMaps Segmentation Data consists of sixty eight segments currently paired with the industry leading GfK MRI survey, providing the essential linkage between neighborhood demographics and consumer preferences and attitudes.

The segments include: 01 One Percenters 02 Peak Performers 03 Second City Moguls 04 Sprawl Success 05 Transitioning Affluent Families 06 Best of Both Worlds 07 Upscale Diversity 08 Living the Dream 09 Successful Urban Refugees 10 Emerging Leaders 11 Affluent Newcomers 12 Mainstream Established Suburbs 13 Cowboy Country 14 American Playgrounds 15 Comfortable Retirement 16 Spacious Suburbs 17 New American Dreams 18 Small Town Middle Managers 19 Outer Suburban Affluence 20 Rugged Individualists 21 New Suburban Style 22 Up and Coming Suburban Diversity 23 Enduring Heartland 24 Isolated Hispanic Neighborhoods 25 Hipsters and Geeks 26 High Density Diversity 27 Young Coastal Technocrats 28 Asian-Hispanic Fusion 29 Big Apple Dreamers 30 True Grit 31 Working Hispania 32 Struggling Singles 33 Nor'Easters 34 Midwestern Comforts 35 Generational Dreams 36 Olde New England 37 Faded Industrial Dreams 38 Failing Prospects 39 Second City Beginnings 40 Beltway Commuters 41 Garden Variety Suburbia 42 Rising Fortunes 43 Classic Interstate Suburbia 44 Pacific Second City 45 Northern Blues 46 Recessive Singles 47 Simply Southern 48 Tex-Mex 49 Sierra Siesta 50 Great Plains, Great Struggles 51 Boots and Brews 52 Great Open Country 53 Classic Dixie 54 Off the Beaten Path 55 Hollows and Hills 56 Gospel and Guns 57 Cap and Gown 58 Marking Time 59 Hispanic Working Poor 60 Bordertown Blues 61 Communal Living 62 Living Here in Allentown 63 Southern Small City Blues 64 Struggling Southerners 65 Forgotten Towns 66 Post Industrial Trauma 67 Starting Out 68 Rust Belt Poverty

We provide instructions, codes and datasets for replicating the article by Kim, Lee and McCulloch (2024), "A Topic-based Segmentation Model for Identifying Segment-Level Drivers of Star Ratings from Unstructured Text Reviews." This repository provides a user-friendly R package for any researchers or practitioners to apply A Topic-based Segmentation Model with Unstructured Texts (latent class regression with group variable selection) to their datasets. First, we provide a R code to replicate the illustrative simulation study: see file 1. Second, we provide the user-friendly R package with a very simple example code to help apply the model to real-world datasets: see file 2, Package_MixtureRegression_GroupVariableSelection.R and Dendrogram.R. Third, we provide a set of codes and instructions to replicate the empirical studies of customer-level segmentation and restaurant-level segmentation with Yelp reviews data: see files 3-a, 3-b, 4-a, 4-b. Note, due to the dataset terms of use by Yelp and the restriction of data size, we provide the link to download the same Yelp datasets (https://www.kaggle.com/datasets/yelp-dataset/yelp-dataset/versions/6). Fourth, we provided a set of codes and datasets to replicate the empirical study with professor ratings reviews data: see file 5. Please see more details in the description text and comments of each file. [A guide on how to use the code to reproduce each study in the paper] 1. Full codes for replicating Illustrative simulation study.txt -- [see Table 2 and Figure 2 in main text]: This is R source code to replicate the illustrative simulation study. Please run from the beginning to the end in R. In addition to estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships, you will get dendrograms of selected groups of variables in Figure 2. Computing time is approximately 20 to 30 minutes 3-a. Preprocessing raw Yelp Reviews for Customer-level Segmentation.txt: Code for preprocessing the downloaded unstructured Yelp review data and preparing DV and IVs matrix for customer-level segmentation study. 3-b. Instruction for replicating Customer-level Segmentation analysis.txt -- [see Table 10 in main text; Tables F-1, F-2, and F-3 and Figure F-1 in Web Appendix]: Code for replicating customer-level segmentation study with Yelp data. You will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 3 to 4 hours. 4-a. Preprocessing raw Yelp reviews_Restaruant Segmentation (1).txt: R code for preprocessing the downloaded unstructured Yelp data and preparing DV and IVs matrix for restaurant-level segmentation study. 4-b. Instructions for replicating restaurant-level segmentation analysis.txt -- [see Tables 5, 6 and 7 in main text; Tables E-4 and E-5 and Figure H-1 in Web Appendix]: Code for replicating restaurant-level segmentation study with Yelp. you will get estimated coefficients (posterior means of coefficients), indicators of variable selections, and segment memberships. Computing time is approximately 10 to 12 hours. [Guidelines for running Benchmark models in Table 6] Unsupervised Topic model: 'topicmodels' package in R -- after determining the number of topics(e.g., with 'ldatuning' R package), run 'LDA' function in the 'topicmodels'package. Then, compute topic probabilities per restaurant (with 'posterior' function in the package) which can be used as predictors. Then, conduct prediction with regression Hierarchical topic model (HDP): 'gensimr' R package -- 'model_hdp' function for identifying topics in the package (see https://radimrehurek.com/gensim/models/hdpmodel.html or https://gensimr.news-r.org/). Supervised topic model: 'lda' R package -- 'slda.em' function for training and 'slda.predict' for prediction. Aggregate regression: 'lm' default function in R. Latent class regression without variable selection: 'flexmix' function in 'flexmix' R package. Run flexmix with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, conduct prediction of dependent variable per each segment. Latent class regression with variable selection: 'Unconstraind_Bayes_Mixture' function in Kim, Fong and DeSarbo(2012)'s package. Run the Kim et al's model (2012) with a certain number of segments (e.g., 3 segments in this study). Then, with estimated coefficients and memberships, we can do prediction of dependent variables per each segment. The same R package ('KimFongDeSarbo2012.zip') can be downloaded at: https://sites.google.com/scarletmail.rutgers.edu/r-code-packages/home 5. Instructions for replicating Professor ratings review study.txt -- [see Tables G-1, G-2, G-4 and G-5, and Figures G-1 and H-2 in Web Appendix]: Code to replicate the Professor ratings reviews study. Computing time is approximately 10 hours. [A list of the versions of R, packages, and computer...