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Introducing you to Customer Analytics with Python. In this course, you will learn the fundamentals of marketing, as well as the practical skills to analyze customer data and predict the purchase behavior of clients.
Customer Analytics in Python is where marketing and data science meet. These are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. In addition, 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 course will teach you how to gain authentic insights from the customer's data, as well as how to leverage the power of machine and deep learning to perform customer analytics. This is a highly valuable and rare skillset to have both in data analytics and data science.
In this course, you’ll get to learn theory, as well as acquire practical skills, applicable to a variety of customer analytics areas. You’ll get to meet your instructors and learn about the topics we’ll discuss during the course. This is the place where we’ll discuss the marketing fundamentals, such as the STP framework and Marketing Mix. They will provide us with motivation as to why we take advantage of certain models in the following sections of the course.Course Introduction Free Segmentation, Targeting, Positioning Free Marketing Mix Free Physical and Online Retailers: Similarities and Differences. Free Price Elasticity Free
We will begin with customer analytics, which is a major part of the course. We will perform some initial exploration of our segmentation data set. Using Python, we’ll visualize our data and standardize it to aid in future analysis.Getting to know the Segmentation Dataset Free Importing and Exploring Segmentation Data Free Standardizing Segmentation Data Free
Here, we discuss the theory behind hierarchical and flat clustering. Throughout the course we’ll implement both approaches. In this section, we’ll focus on hierarchical clustering and use it to determine the number of clusters in our data set.Hierarchical Clustering: Background Hierarchical Clustering: Implementation and Results
We continue with segmentation, this time focusing on a flat clustering technique, namely K-means. We’ll divide our customers into clusters using K-means and determine the customer segments. This is a crucial part of the customer analysis, as it is where we discover the most important characteristics, which define our customer groups.K-Means Clustering: Background K-Means Clustering: Application K-Means Clustering: Results
In our final section of customer analytics, we’ll introduce a dimensionality reduction algorithm: Principal Components Analysis (PCA). We’ll combine K-means and PCA to obtain even better clustering results and gain insight about our customers. In addition, we’ll perform the model deployment, which we’ll need in the following sections of our coursePrincipal 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
It’s time for purchase analytics. This is the second major part of the course. We’ll examine our purchase data set and see similarities and differences with the segmentation data. We’ll analyze and preprocess our data and ultimately segment the purchase customers into groups.Purchase Analytics - Introduction Getting to know the Purchase Dataset Importing and Exploring Purchase Data Applying the Segmentation Model
We’ll analyze our customers by filtering our data by brand and segment, and we’ll visualize the findings. In addition, we’ll compute the revenues by brand and total revenues for each segment. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for subsequent modeling.Purchase Analytics Descriptive Statistics: Segment Proportions Purchase Analytics Descriptive Statistics: Purchase occasion and Purchase Incidence Brand Choice Dissecting the revenue by segment
We’re ready to create our first prediction model. We’ll use logistic regression and model purchase probability of our clients. We’ll compute the price elasticity of purchase probability and use it to gain insight into our average customers, as well as each of our customer segments. Furthermore, we’ll explore the effects of promotion on our clients’ purchase behavior.Purchase Incidence Models. 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
Our goal is to understand the brand preferences of our clients. We’ll imagine we work for one of the brands and create marketing strategies for targeting customers to attract them to our own brand. We’ll compute price elasticity of brand choice for our brand, as well as calculate cross price elasticities for a competitor brand to try to stay ahead of the competition.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
We conclude the purchase analytics part of the course with a model for purchase quantity. Here, we’ll use linear regression to determine how many units of our product the customer likes to buy. We’ll examine the effects of promotion on purchase quantity and determine price elasticity of demand.Purchase Quantity Models. The Model: Linear Regression Preparing the Data and Fitting the Model Calculating Price Elasticity of Purchase Quantity Own Price Brand Choice Elasticity
Machine learning and artificial intelligence are at the forefront of the data science revolution. We will take advantage of the TensorFlow 2.0 framework to create a feedforward neural network. This is the part where we will build a black-box model, essentially helping us reach 90%+ accuracy in our predictions about the future behavior of our customers.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
with Nikolay Georgiev and Elitsa Kaloyanova