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Customer use case.

Usecase scenarios of businesses successfully engaging Octave analytic’s services

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MTN Nigeria

MTN Nigeria works with Octave Analytics to facilitate in-house trainings that will require building of Advanced Analytics Model to predict which customer would become dormant.

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Airtel Nigeria

Octave Analytics Salesforce Automation App helped Airtel address its sales force deployment decisions for sales items, time-effort allocation and territory/cluster performance management.

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Vodacom DRC

Octave was approaced by Vodacom DRC to implement a Next Best Action (NBA) application which enables sales through service by providing identified touch points with a sales recommendation solution

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ntel Nigeria

Octave Analytics was commissioned by ntel Nigeria to implement an end to end CVM platform which included advanced business Intelligence on product performance, customers activities, device analytics, customer base and campaign management

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Telkom Kenya

Telkom Kenya contracted Octave Analytics to provide a segmentation algorithm where its customers usage data are matched against the quantitative research data to determine the likelihood for a customer to match a given segment

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Etisalat Nigeria

Octave analytics was commissioned to investigate why the launch of a series of new products by a Etisalat to 23m subscribers did not bring about their projected revenue.

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Octave offers Analytics and end-to-end Customer Value Management Process Outsourcing Services, which deals with the application of computing tools, statistics and mathematical models to solve business and industry problem.

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LEARN HOW TO MAKE DATA DRIVEN DECISION

Fill the form below carefully to signup for our training session. Our team would get back to you as soon as possible to confirm your seat and proceed with payment details.

Financial Performance Analytics Overview

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. While many accounting and financial organizations deliver data, Financial performance Analytics deploys that data to deliver insight, and 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, optimization, and more.

Ratios and Forecasting

  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

Earnings Management Analytics

  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

Financial Prediction Models

  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

Linking Non-financial Metrics to Financial KPIs

  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

Human Resource Analytics Overview

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.

Performance Evaluation Analytics

  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

Recruitment Analytics

  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

Human Resource Collaboration

  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
  7. Discussions on Collaboration Tools

Talent Management Analytics

  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

Sales Analytics Overview

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. The course on Sales Analytics, focuses on how sales team use data to profitably match supply with demand in various business settings. In this course, you will learn how to model future demand uncertainties and how to predict sales.

Introduction to Sales Analytics

  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

Causal Predictive Sales Analytics

  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

Optimizing Sales - Prescriptive Modelling

  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

Customer Analytics Overview

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.

Introduction to Customer Analytics

  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

Predictive Modelling

  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

Predictive Modelling 2

  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

Business Analytics Overview

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.

Introduction to Business Analytics

  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

Predictive Modelling

  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

Predictive Modelling 2

  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.

Segmentation, Optimization & Visualization

  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

Revision

  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