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

Business data analytics involves the process of examining large and varied data sets – i.e., big data – to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information. The ultimate goal is to facilitate data-driven decision-making, improve operational efficiency, and gain a competitive edge.

In the context of ERP (Enterprise Resource Planning) systems, business data analytics integrates seamlessly to enhance various business processes by providing real-time insights. This module will explore the fundamental aspects of business data analytics, delve into its importance, and illustrate its application through real-world examples.

 

Understanding Business Data Analytics

Definition and Importance

Business data analytics encompasses the methodologies and processes involved in analyzing business data to drive decision-making and strategic planning. It transforms raw data into meaningful insights that help businesses understand their performance, identify opportunities for growth, and mitigate potential risks.

 

Key Objectives:

  1. Improving Decision-Making: By providing a factual basis for decision-making, business analytics helps eliminate guesswork and intuition from business strategies.
  2. Enhancing Operational Efficiency: Analytics can identify bottlenecks and inefficiencies in business processes, suggesting improvements and automation opportunities.
  3. Gaining Competitive Advantage: Companies that effectively utilize analytics can better understand market trends and customer behavior, enabling them to stay ahead of competitors.
  4. Customer Satisfaction: By analyzing customer data, businesses can tailor their products and services to better meet customer needs, enhancing satisfaction and loyalty.

 

Key Components of Business Data Analytics

  1. Data Collection and Integration

Data Sources: Business data comes from various sources, including transactional systems, customer relationship management (CRM) systems, social media, IoT devices, and more. An ERP system integrates these disparate data sources, providing a unified platform for analysis.

Example: A retail company collects data from point-of-sale systems, online transactions, and customer loyalty programs. Integrating this data into an ERP system allows for comprehensive analysis of customer purchasing patterns.

  1. Data Cleaning and Pre-processing

Data Quality: Ensuring the data is clean, accurate, and formatted correctly is crucial. Data cleaning involves removing duplicates, correcting errors, and filling in missing values.

Example: Before analysing sales data, a retail company might clean the data by removing duplicate transactions, correcting inaccurate entries (e.g., wrong product codes), and ensuring all sales data is timestamped correctly.

  1. Data Analysis Techniques

Descriptive Analytics: This involves summarizing historical data to understand what has happened in the past. Techniques include statistical summaries, data aggregation, and data visualization.

Example: A retailer uses descriptive analytics to create sales reports, highlighting top-selling products, seasonal trends, and overall revenue performance.

Predictive Analytics: This technique uses historical data to make predictions about future events. It employs methods like regression analysis, time series analysis, and machine learning algorithms.

Example: A retail chain predicts future sales trends based on historical sales data, accounting for factors such as seasonal variations and marketing campaigns.

Prescriptive Analytics: This goes beyond predicting future outcomes by recommending actions to achieve desired results. It uses optimization algorithms and simulation techniques.

Example: An inventory management system in a retail business might use prescriptive analytics to suggest optimal stock levels and reorder points to minimize costs while ensuring product availability.

 

Real-Time Examples of Business Data Analytics

Example: Retail Industry

Company: Zara (Fashion Retailer)

Scenario: Zara aims to enhance its supply chain efficiency and respond swiftly to fashion trends.

Application:

  • Data Collection: Zara collects data from sales transactions, online customer interactions, and social media trends.
  • Data Integration: This data is integrated into Zara’s ERP system, providing a centralized repository for analysis.
  • Descriptive Analytics: Zara analyzes historical sales data to identify top-selling items and seasonal trends.
  • Predictive Analytics: The company uses predictive models to forecast demand for different clothing items, considering factors like weather forecasts and upcoming fashion trends.
  • Prescriptive Analytics: Zara’s ERP system recommends optimal inventory levels and replenishment schedules to ensure stores are stocked with the right products at the right time.

Outcome: By leveraging data analytics, Zara can quickly adapt to changing fashion trends, reduce excess inventory, and minimize stockouts, thereby increasing customer satisfaction and profitability.

 

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