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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How To Solve Customer Segmentation Problem With Machine Learning

Mrinal Singh
Mrinal Singh Last Updated : 19 Apr, 2023
6 min read

This article was published as a part of the Data Science Blogathon

Customer segmentation ordinarily relies on enormous data sets and especially demands to be designed in an appropriate fashion. Because of this, in today’s tutorial, we will learn about customer segmentation in the marketing domain and how to tackle this problem with the help of machine learning.

Index

  • Customer Segmentation & Its Types
  • Effect Of Customer Segmentation In Marketing Domain
  • Keypoints To Remember For Customer Segmentation In Marketing Domain
  • Steps To Perform Customer segmentation with Machine Learning Algorithms.
  • Conclusion

 

image 1 | customer segmentation

Customer Segmentation & Its Types

Customer segmentation is the method of distributing a customer base into collections of people based on mutual characteristics so organizations can market to group efficiently and competently individually.

The purpose of segmenting customers is to determine how to correlate to customers in multiple segments to maximize customer benefits. Perfectly done customer segmentation empowers marketers to interact with every customer in the best efficient approach.

In marketing, a corporation might segment shoppers or buyers according to the standards of Segmentation associated & a broad array of causes such as:

Demographic Segmentation that includes:

  • gender
  • age
  • occupation
  • marital status
  • income

Geographic Segmentation that includes:

  • country
  • state
  • city of residence
  • Specific towns or counties

Technographic Segmentation that includes:

  • technologies
  • software
  • mobile devices

Psychographic Segmentation that includes:

  • personal attitudes
  • values
  • interests
  • personality traits

Behavioral Segmentation that includes:

  • actions or inactions
  • spending/consumption habits
  • feature use
  • session frequency
  • browsing history
  • average order value

Effect Of Customer Segmentation In Marketing Domain

customer segmentation in market domain

By segmenting users, marketers can obtain the most maximum of their operations budgets by targeting the appropriate audiences. You can converse straight to customers who are most assuring to transform without spending money on impressions or users who aren’t inclined to purchase the following product.

And you can decorate marketing messages & make them appealing to sustain prospects down the duct more productively. That work can associate with both intelligence and merchandise development.

Definitely, Segmentation promotes a corporation in the following ways:

  • Design and deliver targeted marketing advice that will resonate with particular customer associations but not with others (who will accept notifications according to their requirements and importance, preferably).
  • Decide the most reliable communication course for the segment, anything from email, social media posts, radio advertising, or a different procedure, depending on the feature.
  • Distinguish methods to promote products or new merchandise or assistance opportunities.
  • Build more trustworthy consumer relations to enhance customer assistance.
  • Analysis of pricing selections to concentrate on the most influential customers.

Keypoints To Remember For Customer Segmentation In Marketing Domain

keypoints | customer segmentation

Most companies, when they commence with customer segmentation, they lack a clear vision and a goal. You can try the subsequent measures to obtain segments in customer support on a universal level.

  1. Examine the present customers: Knowing the geographic distribution, shopper preferences/beliefs, analyzing website search page analytics, etc.
  2. Acquire knowledge of every consumer: Plotting an interactive chart for each customer to an assortment of decisions to explain and foretell their response like the commodities, assistance, and content they would be engaged in.
  3. Explain segment possibilities: Once the sections have been established, they should implement a fit business understanding of every segment and its difficulties and possibilities. You can map the entire company’s marketing policy to provide to diverse niches of consumers.
  4. Research the segment: After comparing the description and marketing significance of distinct customer segments, a corporation must realize how to transform its products or assistance to more valid aid. For illustration, it may determine implementing higher cuts to some buyers than others to develop its existing consumer base.

Steps To Perform Customer segmentation with Machine Learning Algorithms

Machine learning, a class of artificial intelligence, can investigate data sets of similar customers and interpret the most beneficial and most inadequate performing customer segments.

The subsequent actions are one of many strategies to tackle customer segmentation over machine learning. You can utilize your favorite tools, partners, and skills to handle these methods conveniently.

Step 1: Design A Proper Business Case Before You Start

 

design | customer segmentation

In the case research, we need to visualize consumer habits and styles from different perspectives. You don’t need to go into this method recklessly. Otherwise, the result will be dirty and disordered.

Alternatively, you require a good business case to start with. The prospect of applying machine learning and artificial intelligence can be thought of with:

  • “Can the consumer support be organized into groups to generate customized connections within them?“
  • “Is determining the most vital customer gatherings within the entire pool of consumers worthy? “

To fully appreciate customers’ spending and regulation, you can practice with the latter points in mind:

  • Amount of commodities ordered
  • ordinary return rate
  • cumulative spending

Once you’ve prepared the business case, proceed to the next step.

Step 2: Collect & Prepare The Data

 

prepare data | customer segmentation

The next step is to assemble the data to discover more different patterns and biases inside the datasets.

You will also necessitate setting complex characteristics depending on the most relevant metrics for your organization. It may involve:

  • Medium lifetime value
  • Consumer purchase cost
  • Consumer pleasure
  • Maintenance rate
  • Net earnings

You will need to scale, preprocess and fill the missing values using the open-source tools available in python, such as pandas, NumPy, etc. This step needs to be fixed because they add to the visualization step later.

The more extra customer data you have, the more precise decision you will perform in customer segmentation with machine learning.

That leads us to the next step.

Step 3: Performing Segmentation Using k-Means Clustering

k- means

K-means clustering is a famous method of unsupervised machine learning. This method obtains all of the diverse “clusters” and clubs them collectively while maintaining them as tiny as attainable.

Algorithms works in this manner:

  • First, we randomly initialize the value of k as the number of clusters or n- centroids.
  • Next, we allot each data points to the nearest centroid forming separate groups while relocating the center to the middle of all cluster employing euclidian distance.
  • While working through the preceding steps, the algorithm checks and tries to reduce the sum of squared distances among clustered-point and middle for all clusters.
  • When all data points unite, repetition ends.

Step 4: Tuning The Optimal Hyperparameters For The Model

optimal hyperparameters | customer segmentation

Determining the most beneficial kit of hyperparameters for an algorithm is the subsequent measure in customer segments with Ml because it assists us in attaining the most genuine and satisfying customer crowds.

While choosing the k value, we will select upon the optimization principles of the K-means, inertia, practicing the elbow method.

With the elbow method, we will decide the k value wherever the drop in the inertia sustains.

Step 5: Visualization Of The Results

visualization

At last, we visualize the decisions applying the open-source Plotly-Python, a plotting library in python for making interactive graphs, plots, and charts. Then we understand the charts and various graphs to develop our enterprise.

Possessing genuine consumer profiles at your fingertips will help enhance marketing operations targeting, innovation launches, and the merchandise roadmap.

It will provide your organization exceptionally more evident thoughts about which customers have the most effective retention rate, contracts, and additional metrics you initially planned.

Conclusion

Customer segmentation is essential. Machine learning can get control over the complete process. Discovering all of the different groups that build up a more meaningful customer base permits you to get into customers’ brains and give them precisely what they crave, enhancing their participation and expanding profits.

The source for all the images used is wikipedia.

Thanks for Browsing my Article. Kindly comment and don’t forget to share this blog as it will motivate me to deliver more quality blogs on ML & DL-related topics. Thank you so much for your help, cooperation, and support!

About Author

Mrinal Walia is a professional Python Developer with a computer science background specializing in Machine Learning, Artificial Intelligence, and Computer Vision. In addition to this, Mrinal is an interactive blogger, author, and geek with over four years of experience in his work. With a background working through most areas of computer science, Mrinal currently works as a Testing and Automation Engineer at Versa Networks, India. My aim to reach my creative goals one step at a time, and I believe in doing everything with a smile.

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Mrinal Singh
Mrinal Singh

Data Scientist and a Technical Writer! I will give you the best of Open-Source and AI.

Talks about #chatgpt, #opensource, #contentcreation, #communitybuilding, and #artificialintelligence

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