Course Content
Introduction to GreeneStep CRM
Understand the role and significance of CRM systems in modern business operations.Gain practical knowledge of configuring and managing GreeneStep CRM System. Develop skills in user access management, reporting structures, and organization chart setup. Learn to implement effective performance management strategies using CRM tools. Learn communication and collaboration skills through CRM platforms. Acquire proficiency in setting up and optimizing business processes within GreeneStep CRM. Apply CRM systems in various business scenarios, including employee training, and day-to-day operations management. Analyze and utilize CRM data for decision-making and improving business outcomes.
0/6
GreeneStep ‘Lead to Cash Process’ Management
00:00
CRM System Overview
00:00
Implementation of CRM & Best Practices and Challenges
00:00
Navigating GreeneStep CRM
00:00
Customer 360 in Greenestep
00:00
CRM Video Explanation
00:00
Lead Gen / Lead Qualification Process
Understand the role and significance of CRM in Revenue and Sales processes. To comprehend the role of financial statements, especially the balance sheet, income statement, and cash flow statement, in evaluating sales performance. To utilize ratio analysis and common size statement analysis for evaluating sales performance and financial health. To understand how sales and revenue works inside the GS software
0/7
L2C process Implementation in Greenestep
00:00
Leveraging Financial Data for Sales Insights using ERP
00:00
Financial Analysis for Sales Optimization
00:00
ERP in Action for Financial Analysis
00:00
Cash Flow Management and Sales Forecasting made Simple
00:00
Assessment
00:01:00
Understanding Financial Statements for Sales Analysis
00:00
Customer Order Management
Management (SCM) and its role in business operations. Identify and explain key components of Distribution Management within SCM, including distribution channels, inventory management, and warehousing. Describe the order fulfillment process and its importance in ensuring customer satisfaction and efficient distribution. Analyze the significance of logistics and transportation in SCM and its impact on overall supply chain efficiency. Recognize the integration of CRM with SCM and its benefits in centralizing customer data and enhancing customer satisfaction. Explain the role of CRM systems, particularly as designed in GreeneStep's software, in streamlining operations and resource utilization within SCM. Understand the key modules and processes for SCM, including SCM, sourcing, delivery, logistics, return management, and performance indicators. Discuss the application of IT in SCM and its contribution to enhancing communication, collaboration, and decision-making across the supply chain
0/6
L2C process Implementation in Greenestep
00:00
Concepts of Distribution Industry Supply Chain Management & Importance
00:00
Overview of Supply Chain Management in CRM
00:00
Supply Chain Management Process in GreeneStep ERP Integration
00:00
Benefits and Key Features of SCM
00:00
CaseStudy Analysis
Purchases and Inventory Management
Identify the key activities involved in the procurement process. Recognize real-world examples of procurement activities across various industries. Learn how procurement contributes to effective supply chain management. Understand the steps involved in the procurement process in detail. Comprehend the importance of supplier assessment and key elements involved. Explore the benefits and implementation of e-procurement. Identify the advantages and risks associated with outsourcing in procurement. Grasp key aspects of inventory management and related strategies. Understand the use of ERP systems in procurement and expense management. Learn how ERP systems streamline procurement activities and enhance efficiency.
0/7
L2C process Implementation in Greenestep
00:00
Introduction to Procurement in Supply Chain Management
00:00
Role of Procurement in Supply Chain Management
00:00
Supplier Assessment in Procurement
00:00
Outsourcing: Benefits and Risks
00:00
Inventory Management
00:00
Utilizing ERP Systems in Procurement and Expense Management
00:00
Invoicing and Tax Compliances
Explain the pivotal role of financial management in organizational success, using real-world examples. Analyze the risk-return relationship and its impact on investment decisions. Apply time value of money (TVM) concepts to assess the worth of future cash flows. Evaluate capital budgeting techniques (NPV, IRR, Payback Period) and understand capital rationing. Calculate the weighted average cost of capital (WACC) and its significance in financing decisions. Interpret EBIT-EPS analysis to determine the impact of financing choices on earnings. Differentiate between operating and financial leverage and their effects on profitability. Discuss theories of capital structure and their implications for corporate value. Formulate a dividend policy that aligns with company goals and shareholder expectations. Manage working capital effectively by optimizing cash, receivables, and inventory. Forecast working capital requirements based on business needs and market conditions. Tax management using ERP
0/7
L2C process Implementation in Greenestep
00:00
Introduction to Financial Management
00:00
Financial Analysis and Planning
00:00
Capital Budgeting and Capital Rationing for Investment Decisions
00:00
Financing Decisions Concepts
00:00
Working Capital Management
00:00
Tax Management
00:00
Performance Analytics
Grasp the fundamental concepts of data, including its types, sources, and the classification of digital data. Differentiate between data science, statistics, mathematics, programming languages, databases, and machine learning. Comprehend the definition, importance, and key objectives of business data analytics within ERP systems. Learn the steps to integrate business data analytics into ERP systems and understand the role of BI tools.
0/6
L2C process Implementation in Greenestep
00:00
Introduction to Data
00:00
Introduction to Business Data Analytics
00:00
Implementing Business Data Analytics in ERP Systems
00:00
Real-Time Analytics
00:00
Data Visualization
00:00
Accounting and Compliance
Understand revenue recognition methods and their application in different business scenarios. Identify measurement issues related to accounts receivables and manage them effectively using ERP systems. Classify inventory types and apply appropriate valuation methods. Comprehend various depreciation methods and automate calculations through ERP systems. Accurately account for long-lived assets, including their depreciation. Distinguish between equity and liability and manage them using ERP systems. Apply lease accounting standards and manage lease agreements in ERP systems. Interpret and prepare components of an annual report using consolidated financial data from ERP systems. Grasp fundamental principles and concepts of financial accounting. Generate trial balances and prepare final accounts. Understand cost accounting terminology and the creation of cost sheets. Develop and control budgets, utilizing ERP systems for detailed cost tracking and strategic planning.
0/6
L2C process Implementation in Greenestep
00:00
Fundamentals of Accounting
00:00
Revenue and Receivables
00:00
Asset Management and Depreciation
00:00
Equity, Liabilities, and Leases
00:00
Understanding the Annual Report
00:00
June14-GreeneStep CRM (Backup Copy 1)
About Lesson

Data is raw, unorganized facts that need to be processed. When data is processed, organized, structured, or presented in a given context to make it useful, it becomes information.

Example: A tech company collects vast amounts of data from user interactions on its platform. By processing this data, they gain valuable insights into user behavior and preferences.

 

Data Terminologies

  • Database: A collection of organized data that allows for easy access, management, and updating.
  • Data Mining: The process of discovering patterns and relationships in large data sets.
  • Data Warehouse: A central repository of integrated data from multiple sources, used for reporting and analysis.
  • Data Evolution Roadmap: The progression of data management from basic storage to advanced analytics.
  • Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.

 

Types of Data

  • Numeric: Quantitative data that can be measured and counted.
  • Categorical: Data that can be categorized based on characteristics.
  • Graphical: Data represented in graphs and charts.
  • High Dimensional Data: Data with a large number of attributes or features.
  • Hot Data: Frequently accessed and used data.
  • Cold Data: Rarely accessed data stored for archival purposes.
  • Warm Data: Data that is accessed occasionally.
  • Thick Data: Rich qualitative data providing context.
  • Thin Data: Quantitative data with limited context.

 

Classification of Digital Data

  • Structured Data: Data that is organized in a fixed format, like databases.
  • Semi-Structured Data: Data that does not conform to a fixed schema, like XML or JSON.
  • Unstructured Data: Data without a predefined format, like text and multimedia content.

Example: Social media platforms handle a mix of structured (user profiles), semi-structured (posts and comments), and unstructured data (images and videos).

 

Data Sources

  • Time Series: Data points indexed in time order.
  • Transactional Data: Data generated from transactions, such as sales.
  • Biological Data: Data derived from biological sources, like genetic sequences.
  • Spatial Data: Data related to physical locations.
  • Social Network Data: Data generated from social interactions and relationships.

Example: A logistics company uses time series data from GPS trackers to optimize delivery routes and improve efficiency.

 

Data Science

  1. Data Science vs. Statistics
  • Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data.
  • Statistics: The study of the collection, analysis, interpretation, presentation, and organization of data.

Example: A sports team uses data science to analyze player performance and develop strategies, while statistics are used to understand historical performance trends.

  1. Data Science vs. Mathematics
  • Data Science: Focuses on extracting insights from data using computational techniques.
  • Mathematics: The abstract science of number, quantity, and space.

Example: In financial services, data science models predict market trends, while mathematics provides the theoretical foundation for these models.

  1. Data Science vs. Programming Language
  • Data Science: Involves programming but focuses on data analysis and insights.
  • Programming Language: Tools used to write software and scripts for various applications.

Example: Data scientists at a tech company use Python for data analysis, while software engineers use the same language for developing applications.

  1. Data Science vs. Database
  • Data Science: Uses databases to store and retrieve data for analysis.
  • Database: A structured set of data held in a computer.

Example: A retail company executive uses databases to store customer data, which data scientists analyze to understand buying patterns.

  1. Data Science vs. Machine Learning
  • Data Science: Broad field encompassing data analysis, visualization, and insights.
  • Machine Learning: Subset of data science focused on building algorithms that learn from data.

Example: An e-commerce platform executive uses machine learning to recommend products, while data science provides insights into overall customer behavior.

 

Data Analytics

Data analytics involves examining data sets to draw conclusions about the information they contain. It uses statistical analysis, data mining, and predictive modeling to discover patterns and relationships.

Example: A telecommunications company executive uses data analytics to understand customer churn and develop strategies to retain customers.

 

Relationship: Data Science, Analytics, Big Data Analytics

  • Data Science: Encompasses data analytics and big data analytics, using scientific methods to extract insights.
  • Analytics: Focuses on analyzing data to find actionable insights.
  • Big Data Analytics: Deals with analyzing large and complex data sets.

Example: A healthcare company executive uses data science to predict patient outcomes, data analytics to understand treatment effectiveness, and big data analytics to process large volumes of patient data.

 

Data Science Components

  • Data Engineering: Involves preparing data for analysis by building pipelines and managing data infrastructure.
  • Data Analytics: Uses statistical methods and algorithms to analyze data.
  • Data Visualization: Represents data graphically to communicate insights.

Example: A finance firm uses data engineering to manage data flows, data analytics to detect fraud, and data visualization to present findings to stakeholders.

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