Data Science essential for Marketing/Sales professionals

There are no specific requirements needed to attend this course.

This course is meant for Marketing Sales Professionals who are intending to get deeper into application of data science in Marketing/ Sales. The course provides
detailed coverage of different data science techniques used for “upsale”, “cross-sale”, market segmentation, branding and CLV.

Difference of Marketing and Sales - How is that sales and marketing are different?

In very simplewords, sales can be termed as a process which focuses or targets on individuals or small groups. Marketing on the other hand targets a larger group or the general public. Marketing includes research (identifying needs of the customer), development of products (producing innovative products) and promoting the product (through advertisements) and create awareness about the product among the consumers. As such marketing means generating leads or prospects. Once the product is out in the market, it is the task of the sales person to persuade the customer to buy the product. Sales means converting the leads or prospects into purchases and orders, while marketing is aimed at longer terms, sales pertain to shorter goals.

Module 1

Introduction to Data Science & Applications in Marketing

• Analytics Overview: Type of analytics- Predictive, Prescriptive, Inferential
• Analytics Practice in Marketing
• Use of Big Data and Different Technologies - Introduction

Module 2

Marketing in a Digital World

• Introduction to Digital Marketing
• Online Advertising - Introduction
• Search Engine Optimization (SEO) – Google Case Study
• Social Media Marketing: Tips and Secret – Example of Facebook, Twitter

Module 3

Exploratory Data Analysis & Statistical Modeling

• Data Presentation and Visualization – Understanding the Business data using Histogram, Pie-chart, Bar Chart, Scatter Diagram – Fast inference – Using Python
• Basic Statistical Modeling – Trend, Seasonality, Clustering, Classifications (Only basics, different Algorithm and usage, not any detail) – Ready code in Python
• Market Basket Analysis (MBA) – Case Study using Association rules, Support, Confidence, Lift

Module 4

Marketing Analytics I

• Introduction to Marketing Process – Case Study
• Utilizing Data to Improve Marketing Strategy
• Measuring Brand Assets, Snapple and Brand Value – Brand Positioning
• Text Mining for Marketing – Basics of Text mining – Case Study for Social Media Marketing

Module 5

Marketing Analytics II

• Customer Lifetime Value (CLV) with Calculation – Case Study of CLV for business decisions
• Measuring Case and Effect through Experiments – Case Study
• Calculating Projected Lift
• Data Science in Online Advertising – Click-rate Conversion, Website Analytics

Module 6

Regression Basics

• What Regression Reveals and basic Statistics (not much details of Mathematics)
• Interpreting Regression Results – With Case Study using Python
• Understanding Log-Log Models – With Case study using Python
• Marketing Mix Models – Case study using Python

Module 7

Classification and Clustering

• Basics of Classification and Clustering – Usage; Mention of Algorithms
• Interpreting the Results – Python Programs with Outputs
• Customer Targeting using Classification and Clustering – Case Study
• Business Strategy Improvement – Example of Email Marketing, Promotions
• Need of Big Data Technologies in Classification and Clustering

Module 8

Time Series Analysis

• Trend and Seasonality – Using Python driven Case Study - Visualizations
• Different Time Series Techniques – AR and MA
• Time Series Models – ARMA, ARIMA, ARIMAX (Usage and Examples with Python) – Case Study
• Time Series Prediction for Marketing Campaign

Module 9

Recommendation Engine

• Personalization and Business Strategy
• Different Types of Personalized Recommendations – Collaborative, Content based
• Different Algorithms for Recommendation Engine – User driven, Item Driven, Hybrid, Matrix Factorization (Only mention and usage of the algorithms without Mathematical details)
• Recommendation Metrics for Incremental Revenue – Detailed Case Study

Module 10

Maximizing Sales using Data Science

• Basics of Optimization Technique and its Uses
• Inventory Optimization – Case Study
• Increasing ROI using Data Science
• Lean Analytics – Startup Accelerator

Module 11

Data Science in Pricing & Promotion I

• Pricing – The Science of Profitable Growth
• Demand Forecasting Techniques - Model and estimate the structure of price-response demand curves
• Pricing Decision – How to Optimize Pricing Decision – Case Study Using Python
• Promotion Analytics – Baseline Calculation and Trade Promotion Model
• Using Promotion for Better Strategy - Sales Model Specification – Multiplicative Model

Module 12

Data Science in Pricing and Promotion II

• Revenue Management - How to manage perishable resources with multiple market segments
• Product Bundling – Fast and Slow Moving Products – Case Study with Python
• Pricing of Perishable Goods and Services - Airline & Hotel Pricing – Mention of Stochastic Models
• Promotion Metrics – Traditional and Social