Customer Segmentation Analysis & Predict Consumer Behaviour

Welcome to Customer Segmentation Analysis & Predicting Consumer Behaviour course. This is a comprehensive project based course where you will learn step by step on how to perform customer segmentation analysis on sales data and also build machine learning models for predicting consumer behaviour. This course is a perfect combination between data science and customer analytics, making it an ideal opportunity to level up your analytical skills while improving your technical knowledge in predictive modelling. In the introduction session, you will learn the basic fundamentals of customer segmentation analysis, such as getting to know its real world applications, getting to know more about machine learning models that will be used, and you will also learn about technical challenges and limitations in customer analytics. Then, in the next section, you will learn about predictive customer analytics workflow. This section will cover data collection, data preprocessing, feature engineering, train test split, model selection, model training, model evaluation, and model deployment. Afterward, you will also learn about several factors that influence consumer behaviour, for example, psychological, economic, social, technology, personal, and culture. Once you have learnt all necessary knowledge about customer analytics, then, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download customer segmentation dataset from Kaggle. Once everything is all set, we will enter the first project section where you will explore the dataset from multiple angles, not only that, you will also visualize the data and try to identify trends or patterns in the data. In the second part, you will learn how to segment customer data using K-means clustering to group customers based on their shared characteristics. This will provide insights into distinct customer segments, enabling personalized marketing and tailored business strategies. Then, you will also conduct feature importance analysis using Random Forest to identify the most influential factors in customer behavior. Next, you will build a machine learning model to predict spending scores using a Decision Tree Regressor. This will enable you to estimate customer purchasing potential, helping optimize resource allocation and targeted promotions. Lastly, You will also build a machine learning model to predict customer churn using a support vector machine. This will allow you to identify at-risk customers and develop effective strategies to improve customer retention. Meanwhile, in the third part, you will learn how to evaluate the model’s accuracy using K-fold cross validation method and you will also deploy the predictive model using Gradio. Last but not least, at the end of the course, we will conduct testing to make sure the machine learning models have been fully functioning and generate accurate outputs.

First of all, before getting into the course, we need to ask ourselves these questions: why should we learn about customer segmentation analysis? Why should we predict consumer behaviour using machine learning? Well, let me answer those questions from the perspective of sales managers. Customer segmentation analysis helps to identify key customer groups, enabling more effective targeting and the tailor marketing strategies to specific needs, which ultimately boosts conversion rates. Predicting consumer behavior using machine learning helps forecast trends and anticipate future actions, allowing businesses to make data-driven decisions, optimize resources, and improve customer satisfaction. This approach empowers businesses to better understand their customers' needs and preferences, allowing them to deliver more meaningful experiences, build stronger relationships, and achieve sustained competitive advantage in the market.

Below are things that you can expect to learn from this course:

• Learn the basic fundamentals of customer segmentation analytics, technical challenges and limitations in customer analytics, and its use cases in marketing industry

• Learn about predictive customer analytics workflow. This section covers data collection, preprocessing, feature engineering, train test split, model selection, model training, prediction, model evaluation, and model deployment

• Learn about factors that influence consumer behaviour, such as psychological, economic, social, technology, personal, and culture

• Learn how to find and download customer spending data from Kaggle

• Learn how to clean dataset by removing missing values and duplicates

• Learn how to segment customer by age and gender

• Learn how to segment customer by education level

• Learn how to calculate average customer spending by country

• Learn how to find correlation between purchase frequency and customer spending

• Learn how to find correlation between customer income and customer spending

• Learn how to conduct feature importance analysis using random forest

• Learn how to conduct customer segmentation analysis using k means clustering

• Learn how to build customer spending prediction model using decision tree regressor

• Learn how to build customer churn prediction model using support vector machine

• Learn how to handle class imbalance with synthetic minority oversampling technique

• Learn how to evaluate model accuracy and performance using k fold cross validation method

• Learn how to deploy machine learning model and create user interface using Gradio