DSGenAI for Sales & Marketing

Curriculum

Module 1: Introduction to Data Science (5 hours)

Topics Covered:

• Overview of Data Science: Introduction to the role of data science in sales and marketing, understanding the power of data-driven decisions.

• Sales & Marketing Basics: Understanding key sales and marketing concepts (e.g., customer acquisition, churn rate, customer lifetime value).

• Data Science Tools: Introduction to Python, SQL, Excel, Google Analytics, CRM tools (Salesforce, HubSpot).

• Data Visualization: Using tools like Matplotlib, Seaborn, and Power BI for visualizing marketing and sales data.

Case Study:

• Anomaly Detection: Detect anomaly transactions in retail store.

No-Code Tools:

• Tableau/PowerBI, Google Sheets, Google Colab

Hands-on Activity:

• Identify the pattern for retail sales supermarket data and make decisions

 

Module 2: Data Collection and Cleaning for Sales & Marketing (5 hours)

Topics Covered:

• Types of Sales and Marketing Data: Customer data, transactional data, web analytics, campaign performance data, social media engagement.

• Data Sources: CRM systems, social media platforms, Google Analytics, and sales databases.

• Data Preprocessing: Handling missing values, data transformations, encoding categorical variables, dealing with outliers, data normalization.

• ETL Process: Extract, Transform, and Load techniques for integrating data from multiple sources.

Case Study:

• Data Normalization / cleansing: Find the normal distribution pattern

No-Code Tools:

• Power BI, Google AutoML

Hands-on Activity:

• Treatment & detection of missing & outliers

 

Module 3: Exploratory Data Analysis (5 hours)

Topics Covered:

• Descriptive Statistics: Mean, median, mode, variance, standard deviation applied to sales and marketing metrics.

• Customer Segmentation: Segmenting customers based on demographic, behavioral, and transactional data.

• Visualizations for Marketing Insights: Bar charts, line plots, pie charts, histograms, and heatmaps to uncover trends in sales and marketing performance.

• Customer Journey Analysis: Analyzing the customer lifecycle and interactions with marketing campaigns.

Case Study:

• EDA analysis: Business intelligence for marketing research analysis

No-Code Tools:

• Google Sheets, Google Colab, PowerBI

Hands-on Activity:

• Visual analytics of superstore sales data to perform descriptive statistics and measures of dispersion

 

Module 4: Customer Segmentation and Targeting (5 hours)

Topics Covered:

• K-means Clustering: Identifying groups of customers with similar behaviors or characteristics.

• RFM Analysis (Recency, Frequency, Monetary): Classifying customers based on purchasing patterns to drive personalized marketing.

• Persona Development: Creating customer personas using data to drive more targeted marketing strategies.

• Geographic and Demographic Segmentation: Analyzing geographic data, age, income, interests, and other demographic data to tailor marketing efforts.

Case Study:

• Case Studies: Classification, grouping, segmentation

No-Code Tools:

• Google Sheets, Google Colab, PowerBI

Hands-on Activity:

• Split the data into test & train to classify customer details with ML model using no-code tools

 

Module 5: Predictive Modeling for Sales & Marketing (5 hours)

Topics Covered:

• Customer Lifetime Value (CLV) Prediction

• Churn Prediction

• Sales Forecasting (Time series models like ARIMA, Prophet)

• Lead Scoring

• Campaign Effectiveness

Case Study:

• AI in Predicting Marketing Campaign Performance & Promotions

No-Code Tools:

• Google Sheets, Google Colab, PowerBI

Hands-on Activity:

• Predictive models for sales & marketing

 

Module 6: Marketing Campaign Optimization (5 hours)

Topics Covered:

• A/B Testing

• Conversion Rate Optimization

• Multivariate Testing

• Marketing Attribution Models

• Budget Allocation

Case Study:

• Portfolio Management: Optimizing a portfolio using real-time data and predictive models

No-Code Tools:

• Google Sheets, MS Excel

Hands-on Activity:

• Multivariate test and A/B test

 

Module 7: Time Series Analysis for Sales & Marketing (5 hours)

Topics Covered:

• Time Series Decomposition

• Demand Forecasting

• Seasonality and Trend Analysis

• Real-Time Data Processing

Case Study:

• Economic Forecasting: Using machine learning to predict macroeconomic indicators

No-Code Tools:

• Google Colab, PowerBI

Hands-on Activity:

• Trend, seasonal, irregular, cyclic variation and ARIMA forecasting model

 

Module 8: Machine Learning for Marketing and Sales Optimization (5 hours)

Topics Covered:

• Supervised Learning Algorithms

• Unsupervised Learning Algorithms

• Recommendation Systems

• Association Rule Mining

Case Study:

• Classification & prediction: Using supervised / unsupervised model to predict the sales reports

No-Code Tools:

• Google Colab, MS Excel, Google Sheets

Hands-on Activity:

• Sensor data / transcripts analysis using AI models

Final Project: DSGenAI for Sales & Marketing

Project Theme:

Optimizing an E-commerce Marketing Campaign Using Data Science

Industry: E-commerce/Retail
Company: An online electronics store running digital marketing campaigns on Google, Facebook, and Instagram.

Objectives:

1. Maximize ROI by optimizing marketing spend across platforms

2. Identify top-performing customer segments using clustering

3. Evaluate campaign performance and recommend improvements

Project Guidelines (Summary)

• Data Sources: Ads data (Google/Facebook/Instagram), sales data, customer demographics, CRM tools

• Data Cleaning: Remove duplicates, handle missing values

• EDA: Analyze conversion, ROAS, demographics, and campaign efficiency

• Segmentation: K-means and RFM to create buyer groups

• Modeling: CLV prediction, churn forecasting

• Optimization: Budget allocation via linear programming, A/B testing

• Reporting: Dashboard (Power BI/Tableau) with KPIs and actionable insights

• Outcome:

  • 15% increase in sales

  • 20% reduction in CAC

  • 10% improvement in conversion rates

Project Assessment Criteria (Total: 100 Marks)