Certificate in Marketing Analytics

This module establishes the core principles of marketing analytics and its strategic importance in today’s data-driven business environment. You’ll gain a comprehensive understanding of the marketing analytics process, the various types of data used, and how analytics drives effective decision-making. We will also introduce key marketing concepts and frameworks that form the basis for subsequent analytical applications.

• Defining Marketing Analytics and its Scope
• The Marketing Analytics Process and Data-Driven Decisions
• Types of Marketing Data: Primary vs. Secondary, Quantitative vs. Qualitative
• Understanding Key Marketing Frameworks (e.g., Marketing Funnel, Customer Journey)
• The Role of Analytics in Different Marketing Functions (e.g., Campaign Management, Content Marketing)
• Introduction to Marketing KPIs and Measurement
• Ethical Considerations and Data Privacy in Marketing Analytics

This module kicks off your journey into Python, a powerful and versatile language essential for marketing analytics. You’ll learn the basics of Python programming, including how to set up your environment, understand fundamental data types, work with variables and operators, and control the flow of your code using conditional statements. We’ll also introduce you to basic data structures like lists and tuples.

• Introduction to Python: Installation and setup
• Basic data types: Integers, floats, strings, booleans
• Variables, operators, and expressions
• Control flow: Conditional statements (if, elif, else)
• Data structures: Lists and tuples – creation, indexing, and basic operations

Building upon the previous module, this section delves deeper into Python’s capabilities for data manipulation. You’ll explore more advanced data structures like dictionaries and sets, master the use of loops for efficient data processing, and learn how to define and utilize functions to write modular code. Finally, we’ll introduce you to the foundational libraries for data analysis in Python: NumPy and Pandas, with a focus on creating and manipulating DataFrames.

• More on data structures: Dictionaries and sets
• Looping constructs: for and while loops
• Functions: Defining and calling functions, passing arguments
• Introduction to Python libraries for data analysis: NumPy and Pandas
• Introduction to DataFrames: Creation and basic manipulation

This module provides a comprehensive exploration of Google Analytics, a vital tool for understanding website performance and user behavior. You’ll learn how to set up your account, navigate the interface, and differentiate between key concepts like dimensions and metrics. We’ll dive into analyzing various reports, setting up conversion tracking, and effectively using UTM parameters for campaign analysis.

• Setting Up Google Analytics & Tracking Code
• Navigating the Google Analytics Interface
• Understanding Dimensions vs. Metrics
• Audience Reports: Demographics, interests, behavior
• Acquisition Reports: Traffic sources, campaigns
• Behavior Reports: Site content, user flow, site speed
• Conversion Reports: Goals and Ecommerce tracking
• Campaign Tracking with UTM Parameters

This module introduces you to Microsoft Power BI, a powerful platform for data visualization and business intelligence. You’ll get familiar with the Power BI Desktop environment and learn how to connect to various data sources relevant to marketing. The focus will then shift to the principles of effective data visualization, enabling you to create impactful charts and dashboards to communicate marketing insights clearly.

• Introduction to Power BI Desktop and its components
• Connecting to various data sources relevant to marketing (Excel, CSV, Web, Databases, Google Analytics)
• Understanding data connectivity modes (Import vs. DirectQuery)
• Principles of Effective Data Visualization in Marketing
• Creating Key Marketing Visualizations (Bar charts, line charts, pie charts, etc.)
• Introduction to building basic dashboards for marketing KPIs

In this module, you’ll learn how to prepare your marketing data for analysis using Power BI’s Power Query Editor. You’ll master essential data transformation techniques, including cleaning, filtering, merging, and creating calculated fields. We’ll then delve into building robust data models by understanding table relationships and introducing you to the basics of DAX (Data Analysis Expressions) for performing calculations relevant to marketing metrics.

• Introduction to Power Query Editor for data transformation
• Performing common data cleaning and transformation steps
• Building data models: Understanding and creating table relationships
• Introduction to DAX (Data Analysis Expressions) for basic marketing calculations

This module dives deep into the strategic marketing framework of Segmentation, Targeting, and Positioning (STP). You’ll learn how to apply data-driven techniques to segment your customer base effectively. We’ll explore methods like RFM analysis and K-means clustering, and you’ll gain hands-on experience creating visualizations in Power BI to understand and profile your different customer segments.

• In-depth exploration of Segmentation, Targeting, and Positioning (STP)
• RFM Analysis for Customer Segmentation
• Rule-Based Segmentation vs. Creating Clusters using K-means Clustering
• Visualizing Customer Segments in Power BI
• Customer Profiling and Persona Development

This module introduces you to two powerful analytical techniques in marketing: Market Basket Analysis and A/B Testing. You’ll learn how to uncover associations between products using the Apriori algorithm in Python and visualize these insights. We’ll also cover the fundamentals of A/B testing, including hypothesis formulation, sample size determination, and analyzing test results to optimize your marketing efforts.

• Market basket analysis and association rules
• Apriori algorithm for association rule mining in Python
• Visualizing association rules
• Basics of A/B testing and hypothesis testing
• Designing A/B tests and determining sample sizes
• Analyzing A/B test results

In this module, you’ll learn how to calculate and interpret Customer Lifetime Value (CLTV), a crucial metric for understanding long-term customer profitability. We’ll explore different methodologies and data considerations for CLTV calculation. Additionally, you’ll be introduced to the fundamental concepts of marketing attribution, understanding how to assign credit to different marketing touchpoints in the customer journey.

• Definition and components of Customer Acquisition Cost (CAC)
• Definition and components of Customer Lifetime Value (CLTV)
• Methods for CLTV Calculation
• Introduction to Marketing Attribution and its Importance
• Common Attribution Models (e.g., First-Touch, Last-Touch)
• Challenges in Marketing Attribution

This module delves into more advanced marketing analytics techniques, providing you with a broader toolkit for sophisticated analysis. You’ll explore concepts like propensity modeling for predicting customer behavior, the basics of marketing mix modeling for understanding the impact of different marketing channels, and an introduction to time series analysis for forecasting future marketing trends.

• Introduction to Propensity Models for Conversion, Engagement, and Churn
• Basic Concepts of Marketing Mix Modeling (MMM)
• Introduction to Time Series Analysis for Marketing Forecasting
• Overview of Text Analytics for Marketing Insights
• Introduction to Experimental Design in Marketing
• Ethical Considerations in Advanced Marketing Analytics