Telecommunications Data Science and Customer Analytics Training Course

Course overview

Telecommunications operators hold rich but fragmented data across network counters, CDRs, CRM platforms, digital channels, billing systems and field operations. The challenge is not simply building models; it is connecting data science work to commercial and network decisions such as reducing prepaid churn, identifying households for fibre upsell, predicting congestion, improving collections, detecting revenue leakage and prioritising retention offers. This course equips participants to turn telecommunications data into defensible customer and operational actions while working within the realities of high-volume, regulated data environments.

Participants learn how to frame telecom analytics use cases, assemble analysis-ready datasets and select methods appropriate to customer, network and revenue questions. The course covers CDR and event-data structures, customer 360 modelling, feature engineering, segmentation, churn propensity modelling, customer lifetime value, next-best-action design, geospatial network analytics, anomaly detection and model evaluation. Practical work uses Python, SQL, Apache Spark and Tableau to move from exploratory analysis through model development to decision-ready reporting.

Delivery combines instructor-led technical sessions with guided labs built around an integrated operator case: a mobile provider facing rising churn, uneven network experience and pressure to grow ARPU. Participants write SQL queries, develop Python features and models, assess bias and commercial value, and present recommendations to a simulated telecom leadership panel. Each participant leaves with a telecom customer analytics project pack containing a problem statement, data dictionary, feature plan, model evaluation summary, dashboard specification and deployment roadmap.

The course is designed for analysts, data scientists, customer-value teams, network analytics specialists and managers who already work with telecom data and need to deliver measurable business outcomes from it.

Course objectives

By the end of this course, participants will be able to:

• Formulate telecom analytics use cases with measurable KPIs for churn, ARPU, network experience and retention value
• Create an analysis-ready customer 360 dataset by joining CDR, billing, CRM, digital and network-quality data
• Engineer telecom features including recharge behaviour, tenure, usage trends, cell congestion exposure and complaint history
• Build and validate churn propensity models using Python classification workflows and lift-based evaluation
• Segment subscribers using behavioural, value and service-experience variables to define actionable customer groups
• Estimate customer lifetime value and prioritise retention interventions using incremental commercial value
• Detect revenue and network anomalies with SQL and Spark-based aggregation and outlier techniques
• Produce a model deployment and monitoring plan covering data drift, fairness, privacy and campaign measurement

Benefits of attending

Target competencies

Who should attend

• Telecommunications Data Scientists — who need domain-specific features, model evaluation methods and deployment practices
• Customer Analytics Managers — who must convert subscriber data into retention, value-management and growth decisions
• CRM and Customer Value Managers — who design segmented offers and need evidence-based next-best-action targeting
• Network Analytics Specialists — who need to connect quality-of-service indicators with customer experience and churn
• Business Intelligence Analysts — who prepare telecom reporting and want to progress from dashboards to predictive analytics
• Digital Transformation and Data Product Managers — who must prioritise viable telecom data products and quantify business value

Requirements and prerequisites

Participants should be comfortable working with tabular data and should understand basic descriptive statistics, including averages, distributions, correlation and sampling. Prior hands-on experience with SQL SELECT, JOIN, GROUP BY and basic Python or another analytical language is expected; participants should be able to read and adapt simple code. Familiarity with telecom terms such as subscriber, ARPU, prepaid/postpaid, CDR and churn is helpful but not essential. The course does not require prior machine-learning model-building experience, advanced calculus, Spark administration or access to a live operator data environment.

Training methodology

The course uses short instructor-led briefings followed by guided technical labs using realistic, anonymised telecommunications datasets. Participants query subscriber and network data in SQL, prepare features in Python, scale selected transformations in Apache Spark and communicate findings through Tableau dashboards. Daily case discussions require participants to defend choices on churn targeting, network-quality prioritisation and offer economics. Small groups progressively build one operator analytics case, then complete an end-of-course application plan that identifies a live use case, required data, stakeholders, success measures and model-governance controls.

Course outline

Day 1: Telecom data foundations and use-case design

• Telecommunications value chain, data domains and analytics decision points
• CDR, xDR, billing, CRM and network-counter data structures
• Subscriber identifiers, household resolution and customer 360 data modelling
• Prepaid and postpaid behavioural metrics including ARPU, recharge and tenure
• Data quality profiling for missing events, duplicate subscribers and late-arriving records
• SQL joins and aggregation patterns for subscriber-level analytical datasets
• Use-case prioritisation with business value, data readiness and intervention feasibility

Workshop: Participants scope a churn and service-experience use case and produce a KPI tree, source-data map and initial analytical dataset specification.

Day 2: Customer segmentation and feature engineering

• Exploratory analysis of usage, recharge, device, channel and complaint behaviours
• Feature engineering from CDR frequency, recency, duration and destination patterns
• Network-experience features from dropped calls, throughput, latency and congestion indicators
• Behavioural cohort analysis for tenure, recharge cycles and declining usage
• RFM and value-based subscriber segmentation methods
• K-means clustering, cluster profiling and segment stability assessment
• Python pipelines for reproducible feature preparation and train-test partitioning

Workshop: Participants build a subscriber feature table in Python and produce named customer segments with commercial and service-experience profiles.

Day 3: Churn, value and next-best-action analytics

• Churn definitions for prepaid, postpaid, port-out and inactivity events
• Supervised classification using logistic regression and gradient-boosted trees
• Class imbalance handling with weighting, resampling and threshold selection
• Model evaluation using precision, recall, ROC-AUC, PR-AUC, lift and gains charts
• Probability calibration and churn-score interpretation for campaign users
• Customer lifetime value estimation and retention-cost trade-off analysis
• Next-best-action design using eligibility, propensity, value and contact constraints

Workshop: Participants train and compare churn models, then produce a prioritised retention list with recommended offer rules and expected campaign value.

Day 4: Network, revenue and anomaly analytics at scale

• Linking cell, site and geographic network performance to subscriber experience
• Geospatial aggregation of coverage, congestion and complaint hotspots
• Apache Spark DataFrame operations for high-volume event and usage data
• Revenue assurance indicators for rating errors, unusual usage and recharge anomalies
• Time-series baselines for traffic, revenue and network-performance monitoring
• Outlier detection using statistical thresholds and isolation-based methods
• Privacy, consent, pseudonymisation and purpose limitation in telecom analytics

Workshop: Participants use Spark to identify network-experience hotspots and anomalous revenue patterns, producing an investigation and prioritisation brief.

Day 5: Operationalising telecom analytics

• Model deployment options for batch scoring, campaign platforms and decision engines
• Data and model monitoring for drift, performance decay and segment change
• Campaign test-and-control design and incremental uplift measurement
• Fairness assessment across geography, tenure, device and customer-value groups
• Tableau dashboard design for churn risk, network experience and intervention outcomes
• Stakeholder storytelling for commercial, network, care and executive audiences
• Analytics product roadmap, ownership model and implementation risk management

Workshop: Participants present their end-to-end operator analytics project pack, including dashboard specification, intervention plan, monitoring controls and 90-day implementation roadmap.

Tools & standards covered

Python, SQL, Apache Spark, Tableau

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions