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Providing optimum customer experience, accurate capacity planning, customer-centric assurance, focused marketing and campaign management etc requires strategic utilization of network data. One sure way we help service providers maximize their revenue potential from call data records, inbound and outbound campaign data and network traffic information is through analytics-driven solutions. These deliver insights on how to offer superior customer experience and make you more intelligent in your day to day interactions with your customers. Our solutions extract greater insights and pinpoints new opportunities for improving lives and growing your business every day.

Churn Management

Octave Analytics makes easy the process of predicting customer churn before it happens through:

  1. Inbound Contact vs Churn Modeling
  2. Dormancy Prediction Model
  3. Churn Forecasting
  4. Product Attrition Modelling

Customer Value Management

Our solutions identify the full potentials of your customers in a bid to maximize their lifetime value​ using these models

  1. Time of Best Impact Model
  2. Telecom Recommender Engine
  3. Value Extraction Model
  4. Early HV Detection Model

Customer Centricity Models

When your customer are treated in the right way, profit is inevitable

  1. Recency, Frequency & Monetary Value Matrix
  2. Customer Profitability Model
  3. Customer Elasticity Model
  4. Customer Experience Indexing

Base Activity Monitors

We provide near realtime monitoring and analysis of base activities to determine your customers’ tendencies

  1. Deep Packet Inspection
  2. Customer Sensitivity Analysis
  3. Multisimming Model
  4. Social Network Analytics
  5. Customer Link Analytics

Market/Customer Segmentations

Our models identify who are your most valuable customers and help to build your business around them​

  1. Lifesytle Segmentation
  2. Sentiment/Sensitivity Analysis
  3. Geo-spatial Predictive Models
  4. Market Share Localization
  5. Value Band Segmentation
  6. Demographics & Behavioural Profiling

Campaign Management Models

​Effective targeting of campaigns using propensity and segmentation for buy/churn is crucial and we know it

  1. Cross seling & Upselling Model
  2. Next Best Offer Modelling
  3. Incremental Response Models
  4. Campaign Optimization Models
  5. Contact & Response Models

Digital Product Marketing Analytics

Octave Analytics solutions detects patterns in usage of digital products and identify opportunities for product developments​

  1. Digital Content Analytics
  2. Product/Offer Mix Analytics
  3. Context Analytical Engine
  4. Survival Data Mining
  5. Simulation of Portfolio Revamp

Sales & Distribution Analytics

S&D is no longer about availability but visibility. Octave provides the requisite visibility for your channels​

  1. Digital Content Analytics
  2. Coverage Analysis
  3. Quality of Actvation Modeling
  4. Location Based Analytics
  5. Neural Networks
  6. Localization of Sales Performance
  7. Demand Analytics
  8. Logistics Regression & Correlations

Network Optimization Models

We provide insights from your network data to optimize network asset utilization and aid capacity planning

  1. Service Assurance Optimization
  2. CEX versus High Value Clustering
  3. Digital Network Transformation
  4. Device Analytics
  5. Capacity Planning Analytics

Brands & Communicaton Models

The effectiveness of your media campaigns is measured through our B&C analytical models

  1. Spontaneous Awareness Models
  2. TOMA Simulation
  3. NPS Segementation
  4. Brand Equity Modeling
  5. Power Score Assessment
  6. Awareness Attribution
  7. Active Gross Point Adv. Rating

Mobile Financial Service Model

​Implementation of insights from our Mobile Money analytics services accelerates your customers’ adoption of MFS services​

  1. Credit Scoring Model
  2. Fraud Detection Model
  3. Mobile Financial Services Risk Matrix
  4. Debt Collection Model
  5. KYC Management
  6. Mobile Money Data Cleansing
  7. Agent Network Manager
  8. Agent Liquidity Model

More Telecom Analytics Offerings

Traditional BI looks backwards at what happened. At octave, we look forward using insights from the past and present to determine the future

  1. Visual Analytics
  2. Data Cleansing for Revenue Assurance
  3. Roaming Analytics
  4. Data Service & Device Analytics
  5. Enterprise Business Modelling
  6. SIM Registration Data Clean-up
  7. Marketing Data Mart Management
  8. Loyalty Score Modeling

Trusted By Top Global Brands

We deliver first class service to all clients, both large and small-scale venture

The Business Analytics in Oil & Gas programme is an industry specific training which is
designed to offer bespoke analytics training to professionals in the Oil and Gas
Industry. This training will be focused on helping Oil & Gas executives and
professionals to understand key business analytics concepts, solve business
challenges, and a lot more.

The Business Analytics in Insurance programme is an industry specific training which is designed to offer bespoke analytics training to professionals in the Insurance Industry. This training will be focused on helping Insurance executives and
professionals to understand key business analytics concepts, solve business
challenges, and a lot more.

The Business Analytics in Telecommunications programme is an industry specific training which is designed to offer bespoke analytics training to professionals  in this Industry. This training will be focused in helping
Telecommunications executives and professionals to understand key business
analytics concepts, solve business challenges, and a lot more.

The Business Analytics in Microfinance programme is an industry specific training
which is designed to offer bespoke analytics training to professionals in the
Microfinance Industry. This training will be focused on helping Microfinance Banks
executives and professionals to understand key business analytics concepts, solve
business challenges, and a lot more.

The Business Analytics in Banking programme is an industry specific training which is
designed to offer bespoke analytics training to professionals in the Banking Industry.
This training will be focused on helping Retail, SME, Corporate and E-Banking
executives and professionals to understand key business analytics concepts, solve
business challenges, and a lot more.

What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You’ll develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization.

Data about customers interactions with the businesses are everywhere.  This course will provide an information on key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices Etisalat, Zenith Bank, Custodian Insurance, Jumia to name a few.

This course is designed to impact the way Sales Managers and Salesforce think about transforming data into better decisions. Improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. In this course, you will learn how to model future demand uncertainties and how to predict sales.

Human Resource analytics is a data-driven approach to managing people at work. Business leaders can now make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. In this course, participants will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies.

Financial Performance Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, you’ll learn how data is used to assess what drives financial performance and to forecast future financial scenarios. This course will explore the many areas in which financial data provides insight into other business areas including consumer behavior predictions, Sales and marketing strategy, risk management and more.

What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You’ll develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization.

Data about customers interactions with the businesses are everywhere.  This course will provide an information on key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices Etisalat, Zenith Bank, Custodian Insurance, Jumia to name a few.

This course is designed to impact the way Sales Managers and Salesforce think about transforming data into better decisions. Improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. In this course, you will learn how to model future demand uncertainties and how to predict sales.

Human Resource analytics is a data-driven approach to managing people at work. Business leaders can now make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. In this course, participants will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies.

Financial Performance Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, you’ll learn how data is used to assess what drives financial performance and to forecast future financial scenarios. This course will explore the many areas in which financial data provides insight into other business areas including consumer behavior predictions, Sales and marketing strategy, risk management and more.

The Business Analytics in Oil & Gas programme is an industry specific training which is
designed to offer bespoke analytics training to professionals in the Oil and Gas
Industry. This training will be focused on helping Oil & Gas executives and
professionals to understand key business analytics concepts, solve business
challenges, and a lot more.

The Business Analytics in Insurance programme is an industry specific training which is designed to offer bespoke analytics training to professionals in the Insurance Industry. This training will be focused on helping Insurance executives and
professionals to understand key business analytics concepts, solve business
challenges, and a lot more.

The Business Analytics in Telecommunications programme is an industry specific training which is designed to offer bespoke analytics training to professionals  in this Industry. This training will be focused in helping
Telecommunications executives and professionals to understand key business
analytics concepts, solve business challenges, and a lot more.

The Business Analytics in Microfinance programme is an industry specific training
which is designed to offer bespoke analytics training to professionals in the
Microfinance Industry. This training will be focused on helping Microfinance Banks
executives and professionals to understand key business analytics concepts, solve
business challenges, and a lot more.

The Business Analytics in Banking programme is an industry specific training which is
designed to offer bespoke analytics training to professionals in the Banking Industry.
This training will be focused on helping Retail, SME, Corporate and E-Banking
executives and professionals to understand key business analytics concepts, solve
business challenges, and a lot more.

  1. Analytics Thinking
  2. Design Thinking and Business Models
  3. The Information-Value Chain
  4. Data Systems Break
  5. Data Storage and Databases
  6. Data Analytics Technologies
  7. Retaltonal Database Systems
  8. How tools fit into Infornation-Value Chain Break
  9. Data Extraction Using SQL
  10. Discuss Data Governance & Data Privacy 11. Install KNIME
  1. Exploratory data analysis
  2. Data Cleansing
  3. Data Preprocessing
  4. Feature Engineering
  5. Basic Principles of Predictive Modelling
  6. Introduction to KNIME
  7. Describe the process of classification tree
  8. Interpret trees and summarize trees as business rules.
  9. Time Series Predictive Models
  10. Fundamentals of Linear Regression
  11. Linear Regression as a predictive modeling tool.
  12. Apply linear regression using KNIME
  1. More Classification Tree Examples
  2. More Regression Examples
  3. Apply Modelling CART and Regression using KNIME
  4. Explain the difference between regression and classification
  5. Break Evaluating Analytics Models
  6. Confusion Matrix
  7. Root Means Square Error
  8. AUC ROC etc
  9. Fundamental ideas of neural network models
  10. Building Neural networks Models from datasets using KNINE
  11. Explain NN explain the results.
  1. Explain Clustering and How is is applied
  2. Clustering Marketing Data using K-Means on KNIME
  3. Interprete the resulting
  4. Introduction to Excel Solver
  5. Develop a model for a Sales Optimization Problem
  6. Use Excel to Solve optimization models
  7. Interpret solutions and conduct what-if analysis
  8. Visualization – Why it is importanat
  9. Thinkcell- Excel Addin
  10. Connecting Data to Power BI
  11. Presenation with PowerBI
  1. Design Thinking and Analytics
  2. Information-Value Chain
  3. SQL Programming
  4. Data Exploration
  5. Feature Engineering
  6. Building Predictive Models
  7. Evaluating Predictive Models
  8. Clustering Break
  9. Excel Solver
  10. Data Story Teling
  11. Correlation & Causation
  1. Introduction to Customer Analytics
  2. Customer Data Collection (Passive & Causal)
  3. Net Promoter Score and Self-Reports
  4. Survey Design
  5. Time-Series Model
  6. Regression Analysis on Customer Churn
  7. Forecasting Churn
  8. Causal Models Break
  9. Descrbing Customer Quantitatively
  10. Visualizing Customer
  11. Spotting Errors/Outliers in Analytics Projects
  12. Descriptive Analytics Quiz
  13.  
  1. Exploratory data analysis
  2. Data Cleansing
  3. Data Preprocessing
  4. Feature Engineering
  5. Basic Principles of Predictive Modelling
  6. Introduction to KNIME
  7. Describe the process of classification tree
  8. Interpret trees and summarize trees as business rules.
  9. Time Series Predictive Models
  10. Fundamentals of Linear Regression
  11. Linear Regression as a predictive modeling tool.
  12. Apply linear regression using KNIME
  13.  
  1. Implementing Churn Prediction with Random Forest
  2. Interprete & Evaluate the CART Model
  3. Explain the difference between CART and Random Forest
  4. Tweeking Variable to improve Model Performance Break
  5. Confusion Matrix
  6. Root Means Square Error
  7. AUC ROC etc
  8. Customer Lifetime Value Management Break
  9. Customer Campaign Design & Configuration
  10. Campaign Evaluation
  11. A/B testing
  12.  
  1. Introduction to Sales Analytics
  2. Sales Data Collection (Passive & Causal)
  3. Fundamentals of Sales Forecasting Break
  4. The Newsvendor Problem
  5. Moving Averages
  6. Trends Seasonality
  7. Weighted Moving Average
  8. Exponential Smoothing
  9. Adjusted Exponential Smoothing
  10. Travelling Salesman Example
  1. Fundamentals of Predictive Modelling
  2. Asking Predictive Questions
  3. Building S-Analytics Data Store Break
  4. Feature Engineering
  5. Dimesionality Reduction – Pricipal Component Analysis
  6. Making Predictions: Sales Prediction
  7. Implementing Sales Prediction with Linear Regression on KNIME Break
  8. Interprete & Evaluate the Linear Regression Model
  9. Root Means Square Error
  10. Sales Analytics Quiz
  1. How to Build an Optimization Model
  2. Optimizing with Solver
  3. Network Optimization Example Break
  4. Comparing Decisions in Uncertain Settings
  5. Simulating Uncertain Outcomes in Excel
  6. Interpreting and Visualizing Simulation Output
  7. Implementing Decision Tree
  8. Using Simulation with Decision Trees
  9. Decision Tree Analysis Quiz
  1. Introduction to Human Resource Analytics
  2. Performance Evaluation: the Challenge of Noisy Data
  3. Chance vs. Skill — Tope vs Yasir Story
  4. Finding Persistence: Regression to the Mean
  5. Extrapolating from Small Samples
  6. The Wisdom of Crowds: Signal Independence
  7. Process vs. Outcome
  8. Summary of Performance Evaluation
  9. Performance Evaluation Quiz
  1. Recruitment Analytics
  2. Predicting Performance
  3. Staff Features Engineering Break
  4. Analyzing Promobility
  5. Optimizing Movement with the Organization
  6. Casuality: Part One
  7. Casuality: Part Two
  8. Exploratory Analytics on Staff Churn
  9. Predicting Staff Churn
  10. Recruitment Quiz
  1. Basics of Collaboration
  2. Mapping Collaboration Networks
  3. Evaluating Collaboration Networks Break
  4. Measuring Outcomes
  5. Invervening in Collaboration Networks Break
  6. Hands-on Practice
    1. Discussions on Collaboration Tools
  1. Introduction to Talent Analytics
  2. Interdependence
  3. Self-Fulfilling Prophecies
  4. Reverse Casuality
  5. Test and Algorithms
  6. Challenges of Talent Analytics
  7. Talent Analytics Quiz
  8. Organizational Challenges
  1. Review of Financial Statements
  2. Ratio Analysis: Case Overview
  3. Ratio Analysis: Dupont Analysis Break
  4. Ratio Analysis: Profitability and Turnover
  5. Ratios Ratio Analysis: Liquidity Ratios
  6. Forecasting
  7. Accounting-based Valuation
  8. Ratio Analysis and Forecasting Quiz
  1. Overview of Earnings Management
  2. Revenue Recognition Red Flags
  3. Revenue Before Cash Collection
  4. Revenue After Cash Collection Break
  5. Expense Recognition Red Flags
  6. Capitalizing vs. Expensing
  7. Reserve Accounts and Write-Offs Break
  8. Quiz · Earnings Management
  9. Discussions
  1. Overview
  2. Discretionary Accruals: Model
  3. Discretionary Accruals: Cases
  4. Discretionary Expenditures: Models
  5. Discretionary Expenditures: Refinements and Cases
  6. Fraud Predictions
  7. Break Benford’s Law
  8. Quiz · Financial Prediction Models
  1. Introductions and Overview
  2. Steps to Linking Non-financial Metrics to Financial Performance
  3. Setting Targets Break
  4. Comprehensive Examples
  5. Incorporating Analysis Results in Financial Models
  6. Using Analytics to Choose Action Plans
  7. Organizational Issues Quiz