Customer Analytics Using Python Programming Course

Customer Analytics in Python is where marketing and data science meet. Data science and marketing are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. This course is packed with knowledge, and includes sections on customer and purchase analytics, as well as a deep-learning model, all implemented in Python.

This training is designed for participants who are reasonably proficient in English and have an understanding of basic ICT concepts.

DURATION

5 Days

By the end of the training participant(s) should be able to carry out customer based analysis using various packages based on python.

Module 1

A Brief Marketing Introduction

• Segmentation, Targeting, Positioning
• Marketing Mix
• Physical and Online Retailers: Similarities and Differences
• Price Elasticity

Setting up the environment

• Setting up the environment (Crucial)
• Why Python and Why Jupyter
• Installing Anaconda
• Jupyter Dashboard - Part 1
• Jupyter Dashboard - Part 2
• Installing the sklearn package

Module 2

Segmentation Data

• Getting to know the Segmentation Dataset
• Importing and Exploring Segmentation Data
• Standardizing Segmentation Data

Hierarchical Clustering

• Hierarchical Clustering: Background
• Hierarchical Clustering: Implementation and Results

Module 3

K-means Clustering

• Principal Component Analysis: Background
• Principal Component Analysis: Application
• Principal Component Analysis: Results
• K-Means Clustering with Principal Components: Application
• K-Means Clustering with Principal Components: Results
• Saving the Models

Purchase Data

• Purchase Analytics - Introduction
• Getting to know the Purchase Dataset
• Importing and Exploring Purchase Data
• Applying the Segmentation Model

Descriptive Analyses by Segments

• Segment Proportions
• Purchase Occasion and Purchase Incidence
• Brand Choice
• Dissecting the Revenue by Segment

Module 4

Purchase Incidence Model

• The Model: Binomial Logistic Regression
• Prepare the Dataset for Logistic Regression
• Model Estimation
• Calculating Price Elasticity of Purchase Probability
• Price Elasticity of Purchase Probability: Results
• Purchase Probability by Segments
• Purchase Probability Model with Promotion
• Calculating Price Elasticities with Promotion
• Comparing Price Elasticities with and without Promotion

Brand Choice Model

• Brand Choice Models. The Model: Multinomial Logistic Regression
• Prepare Data and Fit the Model
• Interpreting the Coefficients
• Own Price Brand Choice Elasticity
• Cross Price Brand Choice Elasticity
• Own and Cross-Price Elasticity by Segment
• Own and Cross-Price Elasticity by Segment - Comparison

Module 5

Purchase Quantity Model

• Purchase Quantity Models. The Model: Linear Regression
• Preparing the Data and Fitting the Model
• Calculating Price Elasticity of Purchase Quantity
• Price Elasticity of Purchase Quantity: Results

Deep Learning for Conversion Prediction

• Introduction to Deep Learning for Customer Analytics
• Exploring the Dataset
• How Are We Going to Tackle the Business Case
• Why do We Need to Balance a Dataset
• Preprocessing the Data for Deep Learning
• Outlining the Deep Learning Model
• Training the Deep Learning Model
• Testing the Model
• Obtaining the Probability of a Customer to Convert
• Saving the Model and Preparing for Deployment
• Predicting on New Data

CUSTOMIZED TRAINING

This training can also be customized for your institution upon request. You can have it delivered your preferred location.

For further inquiries, please contact us on details below: 

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Mobile: +254 725 771 853

REQUIREMENTS

Participants should be reasonably proficient in English.  

TRAINING FEE

The course fees is KES 70,000.00 or USD 1,000.00 exclusive of VAT. The course fee covers the course tuition, training materials, two break refreshments, lunch, and study visits.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

ACCOMMODATION

Accommodation is arranged upon request. For reservations contact us below.

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Mobile: +254 725 771 853

PAYMENT   

Payment should be transferred to bank before commencement of training.

Send proof of payment to Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

CANCELLATION POLICY

• All requests for cancellations must be received in writing.
• Changes will become effective on the date of written confirmation being received.