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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 apply web analytics for e-Commerce websites?

Kunal Jain
Kunal Jain Last Updated : 26 Aug, 2021
4 min read

E-commerce is a dynamic and developing industry and so is web analytics. Mix the two and you can expect a world dynamic to its core. In such a dynamic and competitive industry, analytics is set to become a differentiator in how you get those extra customers and maximize their life time value.

Ironically, I get asked “How is web analytics applied in an e-Commerce website?” too frequently. At times, I come across people with in analytics industry ignorant about developments and applications of web analytics. This post aims to provide an overview of how web analytics can help an e-Commerce websites. Following are some benefits which you can realize by use of web analytics:

  • Deep understanding of prospect behavior resulting in higher customer conversion
  • Happier customers as they are served with what they would be looking for
  • Higher profit margins as you get higher share of wallet from your customers

So, how do we achieve these benefits? Simply, keep reading and you’ll get a good overview.

Following diagram shows a typical customer journey framework:

web analytics framework

Applications of web analytics on e-Commerce sites can be divided in three parts:

  • Bringing more prospects through various acquisition channels
  • Converting more prospects to customers
  • Maximizing life time value of customers

Lets look at how web analytics can help in each of these areas:

[stextbox id=”section”]Bringing more prospects to the website [/stextbox]

How do you get more prospects to your site in a cost efficient manner? Which channels are more efficient in bringing customers who are more likely to make a purchase? Which channels bring customers with higher life time values?

These are some questions which can be answered through web analytics. Following are some more examples  where web analytics can help:

  1. Compare performance of various banner placements and creatives. Which ones have higher bounce rates? Which banner animation brings more relevant traffic?
  2. Which search terms bring traffic more likely to convert? Are there any low competition niche keywords which can bring you high conversion traffic?
  3. Which subject line in an Email campaign has higher open rate? Higher click through?

[stextbox id=”section”]Converting more prospects to customers[/stextbox]

Within this domain, the first area where web analytics helps is in understanding your prospects and customers. Do they like interactive site or they prefer to have a simple and directed journey? Do they expect multiple options or they want to  just go ahead with the most popular options? What kind of advertisements are non-intrusive? Understanding  these critical and specific questions ultimately help in converting more prospects into customers.

The second area where web analytics helps is understanding the journey of your customers. Are there particular pages or fields where a lot of people drop off? Following is a screenshot of how this journey page typically looks:

google visitor flow

This particular screenshot tells that there is a huge drop which happens between home page and the next pages. It also tells which are the routes which customers take more often and which are the pages where customers end up going to and fro. Further, you can create different journeys for different traffic sources. Typically, prospects from display ads will have higher conversion chances if you ask bare minimum information from them. On the other hand, if the customer is coming from Search, they would expect more product information before purchasing.

Third area where web analytics helps is to understand overlap of various traffic sources. How many customers are influenced by your affiliates in order to convert prospects to customers? Do you give them due credit? Are you sure you don’t pay them for Sales where they had limited role to play?

[stextbox id=”section”]Maximizing life time value of customers[/stextbox]

Once a person has become your customer, you have some more information about him. Here are some examples:

  • What kind of city and geographical locality did he come from?
  • What mode of payment did he use?
  • Which Credit card did he use?
  • What product did the person buy?
  • Is he likely to be a young person? Professional middle aged Salaried person?

All this information can help you to pitch the right product for the customer, next time he visits your site. Here are some questions you can answer basis the information you have gathered till now:

  • What kind of products can you cross-sell or up-sell to these customers?
  • If a customer has not responded to a promotion in last ten visits, there is a little chance he would respond now.
  • If a customer bought a printer 6 months back, can you offer him a ink cartridge now? Or may be a discount on purchasing multiple cartridges?

All these are ideas and areas where web analytics can help a e-Commerce website. Each of these areas deserve  more detailed posts in future. Hopefully this post would have helped with various applications of web analytics.

In case you know of some more interesting and exciting applications of web analytics, please feel free to share.

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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