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Marketing Analytics ( Powerpoints)

Marketing Analytics ( Powerpoints)

Original price was: $6.00.Current price is: $3.00.

The Marketing Analyst Course is designed to provide learners with a comprehensive understanding of data-driven marketing strategies, tools, and techniques. The course begins with an introduction to the fundamental concepts of marketing analytics, including the importance of metrics and KPIs. Students will explore various data collection methods and sources, learning how to harness both primary and secondary data to inform marketing decisions.

Next, learners dive into practical tools like Excel, Google Analytics, and SQL, where they will gain hands-on experience in analyzing marketing data. Through these tools, students will learn how to segment customers, develop personas, and target specific audiences effectively.

The course also covers essential market research methods, competitive analysis, and SWOT analysis to help learners understand market positioning and strategy. As the course progresses, students will delve into data visualization, learning how to create actionable dashboards and reports using tools like Tableau or Power BI.

Performance analysis of marketing campaigns, including paid digital ads and email marketing, forms a critical part of the curriculum. Students will examine key metrics like ROI, CTR, and conversion rates, and apply predictive analytics and forecasting techniques to anticipate market trends and campaign outcomes.

The course includes an in-depth exploration of attribution modeling, helping learners understand the customer journey and attribute marketing success across various channels. It also introduces marketing automation tools and CRM integration, ensuring that learners can efficiently manage and analyze customer interactions.

In advanced topics, students are introduced to machine learning and natural language processing (NLP) for tasks like customer segmentation and sentiment analysis, highlighting the potential of AI in marketing. The course culminates with a capstone project, where students apply their knowledge to a real-world dataset, develop marketing insights, and present their findings in a final report.

Overall, the Marketing Analyst Course equips learners with the analytical skills and tools needed to thrive in today’s data-driven marketing landscape, preparing them to make informed, strategic decisions that drive business success.

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Module 1: Introduction to Marketing Analytics

  1. Overview of Marketing Analytics
    • Definition and Importance
    • Key Roles and Responsibilities of a Marketing Analyst
  2. Types of Marketing Data
    • Quantitative vs. Qualitative Data
    • Primary vs. Secondary Data Sources
  3. Marketing Metrics and KPIs
    • Overview of Key Marketing Metrics
    • Understanding KPIs in Marketing

Module 2: Marketing Data Collection and Sources

  1. Customer Data Sources
    • Surveys, Interviews, and Focus Groups
    • Website Analytics Tools (Google Analytics, etc.)
    • Social Media Analytics Tools (Facebook Insights, Twitter Analytics, etc.)
  2. Data Collection Methods
    • Tracking and Attribution Models (First-Click, Last-Click, etc.)
    • Data Warehousing and Customer Relationship Management (CRM) Systems
    • Data Privacy and Ethical Considerations

Module 3: Introduction to Data Analysis Tools

  1. Excel for Marketing Analytics
    • Basic Formulas and Functions
    • Data Filtering and Sorting
    • Pivot Tables and Charts
  2. Google Analytics
    • Setting Up and Configuring Google Analytics
    • Analyzing Website Traffic
    • Measuring User Behavior and Conversions
  3. SQL Basics
    • Understanding Databases and Queries
    • Writing Basic SQL Queries for Data Extraction
    • Combining SQL with Marketing Data

Module 4: Customer Segmentation and Targeting

  1. Understanding Customer Segmentation
    • Demographic, Geographic, Psychographic, and Behavioral Segmentation
    • Tools for Segmentation Analysis
  2. Creating Customer Personas
    • Data-Driven Persona Development
    • Application of Personas in Targeted Marketing Campaigns
  3. Case Study: Segmentation and Targeting in Real Campaigns

Module 5: Market Research and Competitive Analysis

  1. Conducting Market Research
    • Primary vs. Secondary Research
    • Techniques for Online and Offline Research
  2. Competitive Analysis
    • Tools for Competitive Benchmarking
    • Identifying Industry Trends and Market Positioning
  3. SWOT Analysis in Marketing

Module 6: Data Visualization and Reporting

  1. Creating Dashboards in Excel and Google Analytics
    • Designing Clear and Actionable Dashboards
    • Visualizing Key Metrics
  2. Using Data Visualization Tools
    • Introduction to Tableau or Power BI for Marketing Analysts
    • Best Practices for Presenting Data Insights
  3. Report Generation and Communication
    • Writing Effective Reports
    • Presenting Insights to Stakeholders

Module 7: Campaign Performance Analysis

  1. Analyzing Paid Digital Campaigns
    • Google Ads, Facebook Ads, and Instagram Ads Metrics
    • ROI, CTR, Conversion Rate, and Other Performance Indicators
  2. Email Marketing Analytics
    • Open Rate, Click-Through Rate (CTR), Bounce Rate, etc.
    • A/B Testing in Email Campaigns
  3. Case Study: Successful Marketing Campaign Performance Review

Module 8: Predictive Analytics and Forecasting

  1. Introduction to Predictive Analytics
    • Predictive Models in Marketing
    • Understanding Regression and Time Series Analysis
  2. Sales Forecasting
    • Forecasting Sales Using Historical Data
    • Building and Testing Predictive Models
  3. Predicting Customer Lifetime Value (CLV)
    • Calculation and Importance of CLV
    • Using CLV in Marketing Strategy

Module 9: Attribution Modeling

  1. Understanding Attribution in Marketing
    • Single-Touch vs. Multi-Touch Attribution Models
    • Importance of Attribution in Marketing Analytics
  2. Common Attribution Models
    • First-Click, Last-Click, Linear Attribution, Time Decay
    • Data-Driven Attribution
  3. Case Study: Attribution in Multi-Channel Marketing Campaigns

Module 10: Marketing Automation and CRM Integration

  1. Introduction to Marketing Automation
    • Benefits and Tools for Automation (e.g., HubSpot, Marketo)
    • Automating Email, Social Media, and Ad Campaigns
  2. CRM Systems and Analytics
    • Integration of CRM with Marketing Analytics
    • Tracking Customer Interactions Across Channels
  3. Case Study: CRM Data Analysis in Marketing Campaigns

Module 11: Advanced Topics in Marketing Analytics

  1. Introduction to Machine Learning in Marketing
    • Applications of Machine Learning in Customer Segmentation, Sentiment Analysis, and Personalization
  2. Natural Language Processing (NLP) for Social Media Analytics
    • Understanding Customer Sentiments through Text Analytics
  3. Real-Time Analytics
    • Using Real-Time Data for Immediate Marketing Insights
  4. Case Study: Using Machine Learning in Marketing

Module 12: Capstone Project

  1. Project Overview
    • Analyzing a Real-World Marketing Dataset
    • Developing Insights and Recommendations for a Hypothetical Client
  2. Final Report and Presentation
    • Writing and Presenting the Capstone Project
    • Peer Review and Feedback

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