Dataset

This dataset was created by Siljith Kandyil

Released under Apache 2.0

Contents

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This dataset simulates customer behavior for a fictional telecommunications company. It contains demographic information, account details, services subscribed to, and whether the customer ultimately churned (stopped using the service) or not. The data is synthetically generated but designed to reflect realistic patterns often found in telecom churn scenarios.

Purpose:

The primary goal of this dataset is to provide a clean and straightforward resource for beginners learning about:

• Exploratory Data Analysis (EDA): Understanding customer characteristics and identifying potential drivers of churn through visualization and statistical summaries.
• Data Preprocessing: Handling categorical features (like converting text to numbers) and scaling numerical features.
• Classification Modeling: Building and evaluating simple machine learning models (like Logistic Regression or Decision Trees) to predict customer churn.

Features:

The dataset includes the following columns:

• CustomerID: Unique identifier for each customer.
• Age: Customer's age in years.
• Gender: Customer's gender (Male/Female).
• Location: General location of the customer (e.g., New York, Los Angeles).
• SubscriptionDurationMonths: How many months the customer has been subscribed.
• MonthlyCharges: The amount the customer is charged each month.
• TotalCharges: The total amount the customer has been charged over their subscription period.
• ContractType: The type of contract the customer has (Month-to-month, One year, Two year).
• PaymentMethod: How the customer pays their bill (e.g., Electronic check, Credit card).
• OnlineSecurity: Whether the customer has online security service (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support service (Yes, No, No internet service).
• StreamingTV: Whether the customer has TV streaming service (Yes, No, No internet service).
• StreamingMovies: Whether the customer has movie streaming service (Yes, No, No internet service).
• Churn: (Target Variable) Whether the customer churned (1 = Yes, 0 = No).

Data Quality:

This dataset is intentionally clean with no missing values, making it easy for beginners to focus on analysis and modeling concepts without complex data cleaning steps.

Inspiration:

Understanding customer churn is crucial for many businesses. This dataset provides a sandbox environment to practice the fundamental techniques used in churn analysis and prediction.

This comprehensive dataset delivers 387M+ U.S. phone numbers enriched with deep telecom intelligence and granular geographic metadata, providing one of the most complete national phone data assets available today. Designed for data enrichment, verification, identity resolution, analytics, risk modeling, telecom research, and large-scale customer intelligence, this file combines broad coverage with highly structured attributes and reliable carrier-grade metadata. It is a powerful resource for any organization that needs accurate, up-to-date U.S. phone number data supported by robust telecom identifiers.

Our dataset includes mobile, landline, and VOIP numbers, paired with detailed fields such as carrier, line type, city, state, ZIP code, county, latitude/longitude, time zone, rate center, LATA, and OCN. These attributes make the file suitable for a wide range of applications, from consumer analytics and segmentation to identity graph construction and marketing audience modeling. Updated regularly and validated for completeness, this dataset offers high-confidence coverage across all 50 states, major metros, rural areas, and underserved regions.

Field Coverage & Schema Overview

The dataset contains a rich set of fields commonly required for telecom analysis, identity resolution, and large-scale data cleansing:

Phone Number – Standardized 10-digit U.S. number

Line Type – Wireless, Landline, VOIP, fixed-wireless, etc.

Carrier / Provider – Underlying or current carrier assignment

City & State – Parsed from rate center and location metadata

ZIP Code – Primary ZIP associated with the phone block

County – County name mapped to geographic area

Latitude / Longitude – Approximate geo centroid for the assigned location

Time Zone – Automatically mapped; useful for outbound compliance

Rate Center – Telco rate center tied to number blocks

LATA – Local Access and Transport Area for telecom routing

OCN (Operating Company Number) – Carrier identifier for precision analytics

Additional metadata such as region codes, telecom identifiers, and national routing attributes depending on the number block

These data points provide a complete snapshot of the phone number’s telecom context and geographic footprint.

Key Features

387M+ fully structured U.S. phone numbers

Mobile, landline, and VOIP line types

Accurate carrier and OCN information

Geo-enriched records with city, state, ZIP, county, lat/long

Telecom routing metadata including rate center and LATA

Ideal for large-scale analytics, enrichment, and modeling

Nationwide coverage with consistent formatting and schema

Primary Use Cases 1. Data Enrichment & Appending

Enhance customer databases by adding carrier information, line type, geographic attributes, and telecom routing fields to improve downstream analytics and segmentation.

1. Identity Resolution & Profile Matching

Use carrier, OCN, and geographic fields to strengthen your identity graph, resolve duplicate entities, confirm telephone types, or enrich cross-channel identifiers.

1. Lead Scoring & Consumer Modeling

Build predictive models based on:

Line type (mobile vs landline)

Geography (state, county, ZIP)

Telecom infrastructure and regional carrier assignments Useful for ML/AI scoring, propensity models, risk analysis, and customer lifetime value studies.

1. Compliance-Aware Outreach Planning

Fields like time zone, rate center, and line type support compliant outbound operations, call scheduling, and segmentation of mobile vs landline users for regulated environments.

1. Data Quality, Cleansing & Validation

Normalize customer files, detect outdated or mismatched phone metadata, resolve carrier inconsistencies, and remove non-U.S. or structurally invalid numbers.

1. Telecom Market Analysis

Researchers and telecom analysts can use the dataset to understand national carrier distribution, regional line-type patterns, infrastructure growth, and switching behavior.

1. Fraud Detection & Risk Intelligence

Carrier metadata, OCN patterns, and geographic context support:

Synthetic identity detection

Fraud scoring models

Device/number reputation systems

VOIP risk modeling

1. Location-Based Analytics & Mapping

Lat/long and geographic context fields allow integration into GIS systems, heat-mapping, regional modeling, and ZIP- or county-level segmentation.

1. Customer Acquisition & Audience Building

Build highly targeted audiences for:

Marketing analytics

Look-alike modeling

Cross-channel segmentation

Regional consumer insights

1. Enterprise-Scale ETL & Data Infrastructure

The structured, normalized schema makes this file easy to integrate into:

Data lakes

Snowflake / BigQuery warehouses

ID graphs

Customer 360 platforms

Telecom research systems

Ideal Users

Marketing analytics teams

Data science groups

Identity resolution providers

Fraud & risk intelligence platforms

Telecom analysts

Consumer data platforms

Credit, insurance, and fintech modeling teams

Data brokers & a...

File Description:

The dataset contains information about customers and their churn status. Each row represents a customer, and each column contains customer attributes and information.

Column Descriptions:

• customerID: Unique identifier for each customer.
• gender: Gender of the customer (Male, Female).
• SeniorCitizen: Whether the customer is a senior citizen or not (1: Yes, 0: No).
• Partner: Whether the customer has a partner or not (Yes, No).
• Dependents: Whether the customer has dependents or not (Yes, No).
• tenure: Number of months the customer has stayed with the company.
• PhoneService: Whether the customer has a phone service or not (Yes, No).
• MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service).
• InternetService: Type of internet service the customer has (DSL, Fiber optic, No).
• OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service).
• OnlineBackup: Whether the customer has online backup or not (Yes, No, No internet service).
• DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support or not (Yes, No, No internet service).
• StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service).
• StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No internet service).
• Contract: The contract term of the customer (Month-to-month, One year, Two year).
• PaperlessBilling: Whether the customer has paperless billing or not (Yes, No).
• PaymentMethod: The payment method of the customer (Electronic check, Mailed check, Bank transfer, Credit card).
• MonthlyCharges: The amount charged to the customer monthly.
• TotalCharges: The total amount charged to the customer.
• Churn: Whether the customer churned or not (Yes, No).

Context and Methodology

The dataset originates from the research domain of Customer Churn Prediction in the Telecom Industry. It was created as part of the project "Data-Driven Churn Prediction: ML Solutions for the Telecom Industry," completed within the Data Stewardship course (Master programme Data Science, TU Wien).

The primary purpose of this dataset is to support machine learning model development for predicting customer churn based on customer demographics, service usage, and account information.
The dataset enables the training, testing, and evaluation of classification algorithms, allowing researchers and practitioners to explore techniques for customer retention optimization.

The dataset was originally obtained from the IBM Accelerator Catalog and adapted for academic use. It was uploaded to TU Wien’s DBRepo test system and accessed via SQLAlchemy connections to the MariaDB environment.

Technical Details

The dataset has a tabular structure and was initially stored in CSV format. It contains:

• Rows: 7,043 customer records

• Columns: 21 features including customer attributes (gender, senior citizen status, partner status), account information (tenure, contract type, payment method), service usage (internet service, streaming TV, tech support), and the target variable (Churn: Yes/No).

Naming Convention:

• The table in the database is named telco_customer_churn_data.

Software Requirements:

• To open and work with the dataset, any standard database client or programming language supporting MariaDB connections can be used (e.g., Python etc).

• For machine learning applications, libraries such as pandas, scikit-learn, and joblib are typically used.

Additional Resources:

• Source code for data loading, preprocessing, model training, and evaluation is available at the associated GitHub repository: https://github.com/nazerum/fair-ml-customer-churn

Further Details

When reusing the dataset, users should be aware:

• Licensing: The dataset is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

• Use Case Suitability: The dataset is best suited for classification tasks, particularly binary classification (churn vs. no churn).

• Metadata Standards: Metadata describing the dataset adheres to FAIR principles and is supplemented by CodeMeta and Croissant standards for improved interoperability.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

Introduction

This Portuguese Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Portuguese-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Portuguese speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

This dataset is about books. It has 1 row and is filtered where the book is Modeling and analysis of telecommunications networks. It features 7 columns including author, publication date, language, and book publisher.

Synthetic Data for Telecom AI Market Outlook

According to our latest research, the global market size for Synthetic Data for Telecom AI reached USD 1.45 billion in 2024, demonstrating robust adoption across telecom enterprises. The market is expected to grow at a CAGR of 36.8% from 2025 to 2033, projecting a significant increase to USD 23.12 billion by 2033. This remarkable growth is primarily driven by the increasing demand for data privacy, the need to accelerate AI model training, and the proliferation of 5G and IoT technologies within the telecommunications sector. As per our latest research, the market’s rapid expansion underscores the pivotal role of synthetic data in enabling advanced AI applications while addressing regulatory compliance and data scarcity challenges.

One of the most significant growth factors for the Synthetic Data for Telecom AI Market is the escalating complexity of telecom networks, especially with the ongoing global rollout of 5G infrastructure. Telecom operators are under pressure to optimize network performance, reduce latency, and ensure seamless connectivity for a growing number of devices and users. Synthetic data enables telecom AI systems to simulate vast and diverse network scenarios, facilitating the development and validation of robust AI algorithms for network optimization and predictive maintenance. By leveraging synthetic data, telecom companies can significantly reduce the time and cost required for data collection and annotation, accelerating the deployment of AI-driven solutions that enhance operational efficiency and customer experience.

Another critical driver is the heightened focus on data privacy and compliance with stringent data protection regulations such as GDPR, CCPA, and emerging data sovereignty laws worldwide. Telecom operators and service providers handle enormous volumes of sensitive customer data, making privacy-preserving AI development a top priority. Synthetic data provides a viable alternative to real customer data, enabling the training and testing of AI models without exposing personally identifiable information. This not only mitigates the risk of data breaches but also facilitates cross-border data sharing and collaboration, which are essential for global telecom operations. As a result, the adoption of synthetic data is rapidly gaining traction as a strategic enabler of privacy-compliant AI innovation in the telecom sector.

Furthermore, the telecom industry’s shift towards digital transformation and automation is fueling demand for advanced analytics and AI capabilities. Synthetic data is instrumental in overcoming the limitations of real-world datasets, which are often incomplete, imbalanced, or difficult to access. By generating high-quality, diverse, and representative datasets, synthetic data empowers telecom companies to build more accurate and resilient AI models for applications such as fraud detection, customer analytics, and network security. The ability to generate tailored datasets on demand also supports rapid experimentation and prototyping, fostering a culture of innovation and agility within telecom organizations. This trend is expected to continue as telecom operators seek to differentiate themselves through data-driven services and personalized customer experiences.

Regionally, North America currently leads the Synthetic Data for Telecom AI Market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. North America’s dominance is attributed to the presence of major telecom operators, advanced technology infrastructure, and a strong ecosystem of AI and data science startups. Europe is also witnessing significant growth, driven by regulatory initiatives promoting data privacy and digital transformation. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid telecom network expansion, increasing investments in AI, and the proliferation of mobile and IoT devices. Latin America and the Middle East & Africa are gradually catching up, supported by ongoing digitalization efforts and growing awareness of the benefits of synthetic data for telecom AI applications.

The subject of this paper is modeling customer satisfaction in the mobile telecommunication industry following the Covid-19 pandemic. Based on standard customer satisfaction models, a specialized model tailored for the mobile telecommunication industry has been developed to account for its unique characteristics, including market concentration. This model was created within the Slovakian context using the Structural Equation Modelling method. The respondents were customers of all mobile operators in this market. The model revealed a positive relationship between image and perceived service quality and a negative relationship between customer expectations and perceived service value. However, it was not possible to demonstrate a relationship between image and customer loyalty or between customer expectations and customer satisfaction. Therefore, it seems that the factors influencing customer satisfaction in the telecommunications sector of an emerging EU economy differ from those in other sectors and economies in the post-Covid-19 context.

Introduction

This Canadian English Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Canadian English speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

Churn Prediction SaaS for Telecom Market Outlook

As per our latest research, the global Churn Prediction SaaS for Telecom market size is valued at USD 1.32 billion in 2024, with a robust compound annual growth rate (CAGR) of 19.7% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 6.33 billion, reflecting the mounting demand for advanced predictive analytics solutions across the telecom sector. This remarkable growth is driven by the increasing need for telecom operators to minimize customer churn, optimize revenue streams, and enhance overall customer experience through data-driven decision-making.

One of the primary growth factors propelling the Churn Prediction SaaS for Telecom market is the intensifying competition within the telecommunications industry. As telecom operators face shrinking margins and saturated markets, retaining existing customers has become a strategic imperative. Churn prediction SaaS solutions leverage machine learning and artificial intelligence to identify at-risk customers and recommend proactive retention strategies, thereby empowering telecom companies to reduce churn rates significantly. The integration of real-time data analytics, behavioral modeling, and customer journey mapping is enabling operators to tailor their services and offers, which in turn strengthens customer loyalty and increases lifetime value. Furthermore, the rise of digital transformation initiatives and the proliferation of connected devices have amplified the volume and complexity of customer data, making advanced churn prediction tools indispensable for telecom enterprises striving to maintain a competitive edge.

Another significant driver for the Churn Prediction SaaS for Telecom market is the increasing adoption of cloud-based solutions. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it particularly attractive for telecom operators seeking to deploy predictive analytics rapidly without substantial upfront investments in infrastructure. The ability to integrate churn prediction tools seamlessly with existing CRM and billing systems further enhances operational efficiency and enables real-time insights. Additionally, cloud-based SaaS models facilitate continuous updates and improvements, ensuring that telecom operators have access to the latest algorithms and analytics capabilities. This has led to a surge in demand for cloud-based churn prediction offerings, especially among small and medium enterprises (SMEs) looking to compete with larger incumbents.

The growing focus on customer-centricity and personalized service delivery is also fueling the expansion of the Churn Prediction SaaS for Telecom market. Telecom operators are increasingly leveraging advanced analytics to segment their customer base, identify high-value subscribers, and design targeted retention campaigns. By utilizing churn prediction SaaS platforms, companies can gain deeper insights into customer preferences, usage patterns, and potential pain points, enabling them to craft more effective engagement strategies. The integration of AI-driven recommendation engines, sentiment analysis, and predictive modeling is transforming the way telecom companies interact with their customers, resulting in higher satisfaction levels and reduced churn. As a result, investment in predictive analytics for customer retention is expected to remain a top priority for telecom operators worldwide.

From a regional perspective, North America currently leads the Churn Prediction SaaS for Telecom market, accounting for the largest share in 2024. This dominance is attributed to the presence of major telecom operators, early adoption of advanced technologies, and a mature SaaS ecosystem. Europe and Asia Pacific are also witnessing significant growth, driven by increasing digitalization, rising mobile penetration, and heightened competition among telecom service providers. The Asia Pacific region, in particular, is expected to register the highest CAGR over the forecast period, fueled by rapid urbanization, expanding subscriber base, and substantial investments in telecom infrastructure. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by ongoing digital transformation initiatives and growing awareness of the benefits of churn prediction analytics.

Component Analysis

The Churn Prediction SaaS for Telecom market is segmen

Telecom Data Labeling Market Outlook

According to our latest research, the global Telecom Data Labeling market size reached USD 1.32 billion in 2024, demonstrating robust expansion driven by the rapid adoption of artificial intelligence and machine learning across the telecommunications sector. The market is expected to grow at a CAGR of 22.8% during the forecast period, with the market size forecasted to reach USD 9.98 billion by 2033. This exceptional growth trajectory is primarily attributed to the increasing need for high-quality, labeled data to train advanced AI models for network optimization, fraud detection, and customer experience management within telecom operations.

One of the primary growth factors fueling the Telecom Data Labeling market is the exponential surge in data generated by telecom networks, devices, and users. With the proliferation of IoT devices, 5G rollouts, and the expansion of cloud-based telecom services, telecom operators are inundated with massive volumes of structured and unstructured data. To extract actionable insights and automate critical processes, these organizations are increasingly relying on labeled datasets to train and validate AI-driven algorithms. The demand for accurate and scalable data labeling solutions has thus skyrocketed, as telecom companies seek to enhance network efficiency, reduce operational costs, and deliver personalized services to their customers. Additionally, the integration of AI-powered analytics with telecom infrastructure further amplifies the necessity for precise data annotation, ensuring that predictive models and automation tools function with optimal accuracy.

Another significant driver for the Telecom Data Labeling market is the intensifying focus on customer experience management and fraud detection. Telecom providers are leveraging AI and machine learning to proactively identify and mitigate fraudulent activities, optimize network performance, and deliver seamless user experiences. These applications demand large volumes of accurately labeled data, encompassing text, audio, image, and video formats, to train sophisticated algorithms capable of real-time decision-making. The growing complexity of telecom networks, coupled with the need for advanced analytics to interpret customer interactions and network anomalies, underscores the critical role of data labeling in achieving business objectives. As telecom operators invest heavily in digital transformation, the adoption of automated and semi-supervised labeling solutions is expected to accelerate, further propelling market growth.

Furthermore, the emergence of regulatory frameworks and data privacy mandates across different regions has spurred telecom companies to adopt more robust data labeling practices. Compliance with international standards such as GDPR, CCPA, and other local data protection laws requires telecom operators to maintain high standards of data accuracy, transparency, and accountability. This regulatory landscape is prompting the adoption of advanced data labeling platforms that offer end-to-end traceability, auditability, and security. The integration of data labeling solutions with existing telecom workflows not only enhances regulatory compliance but also supports the deployment of ethical and bias-free AI models. As a result, the demand for secure, scalable, and customizable data labeling services continues to rise, positioning the market for sustained growth throughout the forecast period.

From a regional perspective, Asia Pacific is emerging as a dominant force in the Telecom Data Labeling market, driven by rapid digitalization, large-scale 5G deployments, and the presence of leading telecom operators. North America and Europe also contribute significantly to market expansion, owing to advanced telecom infrastructure, high AI adoption rates, and a strong focus on innovation. Meanwhile, Latin America and the Middle East & Africa are witnessing increasing investments in telecom modernization and AI-driven solutions, albeit from a smaller base. This regional diversification not only underscores the global nature of the market but also highlights the varying adoption patterns and growth opportunities across different geographies.

Data Type Analysis

The Data Type segment in the Telecom Data Labeling market is categorized into text, image, audio, and video data. Among these, text data labeling holds a substantial share due to the extensive use of natural languag

Big Data Analytics In Telecom Market size was valued at USD 4.91 Billion in 2024 and is projected to reach USD 155.33 Billion by 2032, growing at a CAGR of 54% from 2026 to 2032.

Global Big Data Analytics In Telecom Market Drivers

Unprecedented Growth in Data Volume: Network traffic, customer contacts, Internet of Things (IoT) devices, social media, and other sources are all contributing to the explosive growth in data volume that telecom firms are seeing. This means that in order to get useful insights from this enormous datasets, sophisticated analytics techniques are required. Demand for Personalized Services: Customers are becoming more and more accustomed to receiving services that are specific to their tastes and actions. Telecom firms can now analyze client data in real-time and provide personalized services, promotions, and goods, all thanks to big data analytics, which increases consumer happiness and loyalty.

Telecom Data Quality Platform Market Outlook

According to our latest research, the global Telecom Data Quality Platform market size reached USD 2.62 billion in 2024, driven by increasing data complexity and the need for enhanced data governance in the telecom sector. The market is projected to grow at a robust CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 8.11 billion by 2033. This remarkable growth is fueled by the rapid expansion of digital services, the proliferation of IoT devices, and the rising demand for high-quality, actionable data to optimize network performance and customer experience.

The primary growth factor for the Telecom Data Quality Platform market is the escalating volume and complexity of data generated by telecom operators and service providers. With the advent of 5G, IoT, and cloud-based services, telecom companies are managing unprecedented amounts of structured and unstructured data. This surge necessitates advanced data quality platforms that can efficiently cleanse, integrate, and enrich data to ensure it is accurate, consistent, and reliable. Inaccurate or incomplete data can lead to poor decision-making, customer dissatisfaction, and compliance risks, making robust data quality solutions indispensable in the modern telecom ecosystem.

Another significant driver is the increasing regulatory scrutiny and compliance requirements in the telecommunications industry. Regulatory bodies worldwide are imposing stringent data governance standards, compelling telecom operators to invest in data quality platforms that facilitate data profiling, monitoring, and lineage tracking. These platforms help organizations maintain data integrity, adhere to data privacy regulations such as GDPR, and avoid hefty penalties. Additionally, the integration of artificial intelligence and machine learning capabilities into data quality platforms is helping telecom companies automate data management processes, detect anomalies, and proactively address data quality issues, further stimulating market growth.

The evolution of customer-centric business models in the telecom sector is also contributing to the expansion of the Telecom Data Quality Platform market. Telecom operators are increasingly leveraging advanced analytics and personalized services to enhance customer experience and reduce churn. High-quality data is the cornerstone of these initiatives, enabling accurate customer segmentation, targeted marketing, and efficient service delivery. As telecom companies continue to prioritize digital transformation and customer engagement, the demand for comprehensive data quality solutions is expected to soar in the coming years.

From a regional perspective, North America currently dominates the Telecom Data Quality Platform market, accounting for the largest market share in 2024, followed closely by Europe and Asia Pacific. The presence of major telecom operators, rapid technological advancements, and early adoption of data quality solutions are key factors driving market growth in these regions. Meanwhile, Asia Pacific is anticipated to exhibit the fastest growth rate during the forecast period, propelled by the expanding telecom infrastructure, rising mobile penetration, and increasing investments in digital transformation initiatives across emerging economies such as China and India.

Component Analysis

The Telecom Data Quality Platform market by component is categorized into software and services. The software segment encompasses standalone platforms and integrated solutions designed to automate data cleansing, profiling, and enrichment processes. Telecom operators are increasingly investing in advanced software solutions that leverage artificial intelligence and machine learning to enhance data quality management, automate repetitive tasks, and provide real-time insights into data anomalies. These platforms are designed to handle large volumes of heterogeneous data, ensuring data accuracy and consistency across multiple sources, which is essential for efficient network operations and strategic decision-making.

The services segment, on the other hand, includes consulting, implementation, support, and maintenance services. As telecom companies embark on digital transformation journeys, the demand for specialized services to customize and integrate data quality platforms within existing IT ecosystems has surged. Consulting services help organiz

Synthetic Data for Telecom AI Market Outlook

According to our latest research, the global market size for Synthetic Data for Telecom AI reached USD 1.18 billion in 2024, demonstrating robust momentum driven by the escalating adoption of artificial intelligence and machine learning solutions within the telecommunications sector. The market is projected to expand at a compelling CAGR of 37.2% from 2025 to 2033, reaching an estimated value of USD 17.47 billion by 2033. This remarkable growth trajectory is primarily fueled by the increasing need for privacy-compliant data, rapid digital transformation, and the proliferation of AI-driven applications across telecom operations.

One of the primary growth factors for the Synthetic Data for Telecom AI market is the surging demand for high-quality, diverse, and privacy-preserving datasets to train advanced AI models. Telecom operators and service providers are increasingly leveraging synthetic data to overcome the limitations of real-world data, such as data scarcity, privacy concerns, regulatory compliance, and the high costs associated with data collection and annotation. Through synthetic data generation, telecom enterprises can simulate complex network scenarios, customer behaviors, and fraud patterns, enabling them to build and deploy robust AI solutions with greater speed and accuracy. The ability to generate large volumes of labeled data without infringing on user privacy is a significant advantage, particularly in light of stringent data protection regulations such as GDPR and CCPA.

Another key driver propelling market growth is the exponential rise in network complexity and the need for intelligent automation across telecom infrastructures. The rollout of 5G networks, Internet of Things (IoT) devices, and edge computing has intensified the complexity of telecom networks, necessitating sophisticated AI models for network optimization, predictive maintenance, and real-time anomaly detection. Synthetic data empowers telecom companies to create realistic training environments for AI algorithms, enabling them to anticipate and address network issues proactively. By leveraging synthetic data, telecom operators can accelerate the development and deployment of AI-powered solutions that enhance network reliability, reduce operational costs, and improve customer experience.

The Synthetic Data for Telecom AI market is also benefiting from the growing trend of digital transformation and the adoption of cloud-native architectures. As telecom companies modernize their IT and network infrastructures, there is an increasing emphasis on leveraging cloud-based AI and data analytics platforms. Synthetic data generation tools, which are increasingly being offered as cloud-based services, provide telecom enterprises with scalable, on-demand access to high-quality datasets for AI training and testing. This shift towards cloud deployment not only reduces infrastructure costs but also enables seamless integration with existing AI workflows and accelerates time-to-market for new AI-driven telecom services and applications.

From a regional perspective, North America currently dominates the Synthetic Data for Telecom AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to the strong presence of leading telecom operators, advanced AI research ecosystems, and proactive regulatory frameworks supporting data innovation. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid digitalization, expanding 5G deployments, and increasing investments in AI and data analytics. Europe, with its stringent data privacy regulations and focus on ethical AI, is also emerging as a significant market for synthetic data solutions in the telecom sector.

Data Type Analysis

The Synthetic Data for Telecom AI market is segmented by data type into Tabular Data, Text Data, Image Data, Video Data, and Others, each playing a pivotal role in enabling AI-driven innovations across the telecom industry. Tabular data remains the most widely used data type, given its relevance in representing structured information such as call records, billing data, network logs, and customer profiles. Synthetic tabular data generation tools are extensively utilized by telecom operators to create large-scale, privacy-preserving datasets for training AI models in customer analytics, fraud dete

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

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GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is valued to increase USD 2.35 billion, at a CAGR of 15.7% from 2024 to 2029. Increased use of GIS for capacity planning will drive the GIS in telecom sector market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 28% growth during the forecast period.
By Product - Software segment was valued at USD 470.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 256.91 million
Market Future Opportunities: USD 2350.30 million
CAGR from 2024 to 2029: 15.7%

Market Summary

The market is experiencing significant growth as communication companies increasingly adopt Geographic Information Systems (GIS) for network planning and optimization. Core technologies, such as satellite imagery and location-based services, are driving this trend, enabling telecom providers to improve network performance and customer experience. One major application of GIS in the telecom sector is capacity planning, which allows companies to optimize their network infrastructure based on real-time data.
However, the integration of GIS with big data and other advanced technologies presents a communication gap between developers and end-users, requiring a focus on user-friendly interfaces and training programs. Additionally, regulatory compliance and data security remain significant challenges for the market. Despite these hurdles, the opportunities for innovation and improved operational efficiency make the market an exciting and evolving space.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

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How is the GIS In Telecom Sector Market Segmented ?

The GIS in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

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

The global telecom sector's reliance on Geographic Information Systems (GIS) continues to expand, with the market for GIS in telecoms projected to grow significantly. According to recent industry reports, the market for GIS data visualization and spatial data infrastructure in telecoms has experienced a notable increase of 18.7% in the past year. Furthermore, the demand for advanced spatial analysis tools, such as building penetration analysis, geospatial asset management, and work order management systems, has risen by 21.3%. Telecom companies utilize GIS for network performance monitoring, data integration platforms, and network planning. For instance, GIS enables network design, radio frequency interference analysis, route optimization software, mobile network optimization, signal propagation modeling, and service area mapping.

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The Software segment was valued at USD 470.60 billion in 2019 and showed a gradual increase during the forecast period.

Additionally, it plays a crucial role in infrastructure management, location-based services, emergency response planning, maintenance scheduling, and telecom network design. Moreover, the adoption of 3D GIS modeling, LIDAR data processing, and customer location mapping has gained traction, contributing to the market's expansion. The future outlook is promising, with industry experts anticipating a 25.6% increase in the use of GIS for telecom network capacity planning and telecom outage prediction. These trends underscore the continuous evolution of the market and its applications across various sectors.

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Regional Analysis

APAC is estimated to contribute 28% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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In China, the construction of smart cities in Qingdao, Hangzhou, and Xiamen, among others, is driving the demand for Geographic Information Systems (GIS) in various sectors. By 2025, China aims to build more smart cities, leading to significant growth opportunities for GIS companies. Esri Global Inc., a leading player

Introduction

This Mexican Spanish Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Mexican Spanish speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation: