About
You are here because you want to learn the fundamentals of Customer analytics This project provides insight into the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, price elasticity, and neural networks
Customer Analytics
🚀 Click to Code for Customer Segmentation 🧑💻
The first part of the project focuses on how to perform customer segmentation, using a hands on approach It involves the application of hierarchical and flat clustering techniques for dividing customers into groups It also features applying the Principal Components Analysis (PCA) to reduce the dimensionality of the problem, as well as combining PCA and K means for an even more professional customer segmentation
Purchase Analytics
The second part of the project explores both the descriptive nad predictive analysis of the purchase behaviour of customers, including models for purchase incidence, brand choice, and purchase quantity Not only that, but it also covers the application of state of the art deep learning techniques to make predictions using real world data
Introduction
In this project we used Python for data Preprocessing, Transforamtion and for deep learning techniques.
The first step in this project to start with Segemntation of customers. The process of dividing a population
of customers into groups that share similar characteristics. We used hierarchical and flat clustering techniques for dividing customers into groups.
Once we have our segmnets it is time for targeting The process of evaluating potential profits from each segment and deciding which segments to focus on
and it is Advertising area.
Once we have our segments and targets, it is time for positioning What product characteristics do the
customers from a certain segment need?. In fact, this process is so important, that it
has a framework of its own called: Marketing Mix Develop the best product or service and offer it at the right price
through the right channels .
visualization
