Module 1: Foundations of Marketing Analytics

• Key Marketing Metrics and KPIs: Defining metrics across the customer journey (Acquisition, Conversion, Retention).
• Data Sources and Types: Understanding first, second, and third-party data; structured vs. unstructured.
• The Marketing Analytics Process: From business question to insight and action.
• Introduction to R and Tableau Ecosystems: High-level overview of their respective roles in the pipeline.
• Practical session: Setting up the R and RStudio environment and loading initial marketing data.

Module 2: Introduction to R for Data Analysis

• R Basics and Data Structures: Vectors, matrices, data frames, and lists.
• Using RStudio: Interface, scripts, console, and project management.
• Data Import and Export: Reading data from CSV, Excel, and databases into R.
• Introduction to the Tidyverse: Understanding the dplyr and ggplot2 packages.
• Practical session: Applying basic dplyr functions (select, filter, mutate) to a sales transaction dataset.

Module 3: Data Preparation and Visualization in R

• Data Cleaning Techniques: Handling missing values, outliers, and data inconsistencies.
• Data Transformation: Aggregation, joining (merging), and reshaping data using tidyr.
• Exploratory Data Analysis (EDA) with ggplot2: Creating scatter plots, histograms, and box plots.
• Customizing Visualizations: Adding titles, themes, and annotations for clarity.
• Practical session: Building a detailed Customer Acquisition Channel performance chart using ggplot2.

Module 4: Descriptive Marketing Analytics

• Measuring Central Tendency and Variability: Mean, Median, Mode, Standard Deviation in R.
• Cohort Analysis: Tracking performance metrics of customer groups over time.
• Funnel Analysis: Quantifying drop-off rates at different stages of the conversion path.
• RFM (Recency, Frequency, Monetary) Analysis Fundamentals: Theoretical overview and segmentation logic.
• Practical session: Calculating and visualizing conversion rates for a specific marketing funnel stage.

Module 5: Customer Segmentation and Clustering (R)

• Introduction to Unsupervised Learning: When and why to use clustering in marketing.
• Implementing K-Means Clustering: Choosing the optimal number of clusters (Elbow Method).
• Interpreting and Profiling Clusters: Describing the characteristics of each customer segment.
• Hierarchical Clustering: Understanding dendrograms and alternative clustering methods.
• Practical session: Performing K-Means clustering on an RFM-derived dataset and interpreting the resulting customer segments.

Module 6: Predictive Modeling for Marketing (R)

• Introduction to Supervised Learning: Regression and classification for marketing.
• Logistic Regression for Churn Prediction: Building and evaluating a binary classification model.
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, and AUC.
• Introduction to Decision Trees: Understanding model interpretation and feature importance.
• Practical session: Developing a Logistic Regression model to predict customer churn based on usage data.

Module 7: Introduction to Tableau for Visualization

• Tableau Interface and Workspace: Understanding sheets, dashboards, and stories.
• Connecting to Data Sources: Connecting to files, databases, and servers (live vs. extract).
• Pills and Shelves: Differentiating between dimensions and measures, and using columns/rows.
• Basic Chart Types: Creating bar charts, line charts, and geo-maps in Tableau.
• Practical session: Connecting Tableau to a processed R output file and creating a basic sales trend line chart.

Module 8: Connecting Data and Building Dashboards in Tableau

• Data Joining and Blending in Tableau: Combining multiple data sources (e.g., sales and campaign data).
• Calculated Fields: Creating custom metrics and dimensions within Tableau.
• Creating Parameters and Filters: Enabling user interactivity in visualizations.
• Dashboard Design Principles: Layout, flow, and visual best practices.
• Practical session: Building a four-panel interactive dashboard displaying key campaign metrics (CTR, Conversion Rate, CPA).

Module 9: Marketing Mix Modeling (R)

• MMM Fundamentals: Allocating marketing spend across channels to maximize ROI.
• Simple Linear Regression: Modeling the relationship between spend and sales/conversions.
• Handling Lagged Effects: Incorporating the delayed impact of advertising (adstock).
• Model Diagnostics and Interpretation: Checking assumptions and deriving channel effectiveness.
• Practical session: Running a multiple linear regression model in R to assess the contribution of different marketing channels to revenue.

Module 10: Web and Social Media Analytics (R)

• Key Web Metrics: Understanding sessions, bounce rate, pages per session, and goal conversion.
• API Connection Basics: Introduction to pulling data from Google Analytics or social platforms in R.
• Text Mining Fundamentals: Tokenization and word frequency analysis of social media comments.
• Sentiment Analysis Introduction: Using dictionaries or pre-trained models for brand monitoring.
• Practical session: Analyzing a web analytics dataset in R to identify top exit pages and bottlenecks in the user journey.

Module 11: A/B Testing and Experimentation Analysis (R)

• Experimental Design: Defining control and treatment groups, and setting up hypotheses.
• Hypothesis Testing Fundamentals: p-values, significance levels, and Type I/II errors.
• T-Tests and Z-Tests in R: Determining statistical significance between A/B groups.
• Power Analysis: Calculating required sample size for reliable test results.
• Practical session: Analyzing the results of a simulated email marketing A/B test using a T-test in R to determine the winning variation.

Module 12: Customer Lifetime Value (CLV) Analysis (R)

• Defining and Calculating CLV: Understanding the components (retention, margin, cost of acquisition).
• Historical vs. Predictive CLV: Differences and when to use each approach.
• Simple Aggregated CLV Calculation in R: Basic formula implementation.
• Introduction to Probabilistic Models (BG/NBD, Gamma-Gompertz): Overview of advanced CLV models.
• Practical session: Calculating the historical CLV for various customer segments using R and comparing their values.

Module 13: Visualizing Campaign Performance in Tableau

• Gantt Charts for Campaign Timelines: Visualizing multi-channel campaign scheduling.
• Waterfall Charts for Budget Analysis: Showing spend allocation and consumption.
• Using Reference Lines and Bands: Setting targets and identifying performance gaps.
• Combining Multiple Metrics: Using dual-axis charts to show trend and volume simultaneously.
• Practical session: Creating a sophisticated dual-axis chart in Tableau to visualize Cost-Per-Acquisition (CPA) trend alongside total conversions.

Module 14: Advanced Dashboarding Techniques in Tableau

• Advanced Filtering and Actions: Using dashboard actions (Filter, Highlight, URL) for seamless navigation.
• Level of Detail (LOD) Expressions: Understanding FIXED, INCLUDE, and EXCLUDE for advanced aggregations.
• Creating Custom Shapes and Images: Enhancing the visual appeal of dashboards.
• Story Points Feature: Curating a guided narrative presentation from the dashboard.
• Practical session: Implementing LOD expressions to calculate the market share of products across different customer regions.

Module 15: Reporting and Storytelling with Data

• Structuring the Narrative: Using the SCQA (Situation, Complication, Question, Answer) framework.
• Designing for the Audience: Tailoring visualizations and language for executive vs. analyst viewers.
• Highlighting the Insight: Using annotations and visual cues to draw attention to key findings.
• The Power of Color: Strategic use of color to convey meaning and importance.
• Practical session: Taking an existing Tableau dashboard and adding narrative elements and annotations to create a compelling data story.

Module 16: Market Basket Analysis and Recommendation Systems (R)

• Association Rule Mining (Apriori Algorithm): Identifying products frequently bought together.
• Metrics of Association Rules: Support, Confidence, and Lift interpretation.
• Implementing Apriori in R: Using the arules package for pattern discovery.
• Introduction to Simple Collaborative Filtering: Overview of item-based recommendations.
• Practical session: Applying the Apriori algorithm in R to a transaction dataset to find strong product association rules.

Module 17: Geospatial Marketing Analysis

• Geocoding Data: Converting addresses or locations into latitude and longitude coordinates.
• Creating Basic Maps in R: Using ggplot2 or leaflet for simple geographical visualization.
• Creating Filled Maps in Tableau: Visualizing metrics by region, state, or postal code.
• Proximity Analysis: Identifying customer density and optimal store/ad placement locations.
• Practical session: Building a filled map in Tableau to visualize customer density and average spend across different sales territories.

Module 18: Course Capstone Project and Next Steps

• Project Definition: Working on an end-to-end marketing analytics problem (provided or self-selected).
• Integrating R and Tableau: Executing the analysis in R and visualizing the final insights in Tableau.
• Presentation Skills: Tips for delivering the final analytical findings to a business audience.
• Learning Resources and Community: Next steps for continuous learning in R and Tableau.
• Practical session: Presenting the capstone project analysis and dashboard to the class for peer review and feedback.

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588