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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

What is the role of analytics in E-Commerce industry?

Tavish Srivastava
Tavish Srivastava Last Updated : 27 Aug, 2024
5 min read

If you are preparing for an interview into role of analytics, you need to do your ground work to get a basic understanding of domain. Also, you should know what is the role of analytics to do smarter business in this domain. But such information is not available in public neither is it available on Job Descriptions. Mostly one of your interview round will be to assess your capability to analyse a problem in their domain. If you know domain before hand, it will be a jackpot. In this article, I will introduce you to a few roles analytics plays in E-Commerce industry.

analytics, ecommerce industry

Let’s talk about the industry first!

E-Commerce is a very dynamically evolving industry and this is primarily because of its underlying ever-changing technology. Companies like Amazon, E-bay are capable of building predictive algorithms being executed in real time on big data environment.

I just spoke 3 big words, which when combined delivers something unmatchable and uniquely executed by E-Commerce industry:

  1. First, e-commerce players have evolved significantly in their decision making over time. In early days simple basket analysis was used to make recommendation, today we have customer specific predictive algorithms being executed.
  2. Second, recommendation systems and other technology which used to take days to execute is now executed in seconds, which make them even more effective.
  3. Third, every thing has now moved to a big data environment. We no more see tabular format data stored in CSV formats. To churn millions of activities of billions of customers, we need parallelization of processes. Everything is done seamlessly by E-Commerce industry and customer doesn’t even notice.

Role of Analytics in E-Commerce

If you have worked in financial industry, you will probably be aware of analytics playing a crucial role into risk and marketing strategy. However, E-Commerce industry goes beyond these two pillars. The primary job of E-Commerce industry is to make user experience on their website is delightful. Other than that they are simply a platform between sellers and buyers. With such focus on user experience, analytics itself becomes a product instead of just being business enabler. For instance, Recommender Engines you see on Amazon sidebar is a classic product. Now, you can appreciate the much broader role of analytics in E-Commerce industry. In the following section, we will talk more about broad functions where analytics is being actively used.

Functions supported by Analytics in E-Commerce Industry

This list is no way exhaustive but will cover broad roles in E-Commerce industry

1. Supply Chain Management : This includes managing data for products right from warehouse to the customer. E-Commerce industries use analytics extensively to manage Inventory.  Also a significant portion of work is into optimising transportation and pricing of delivery.

supply_chain

2. Merchant/Customer Fraud Detection : I recently read a post on Facebook that someone found a show in the delivery box when he ordered a MAC for INR 90,000. This is what is known as Fraud. Even though the E-Commerce company might have nothing to do with this fraud, they are the one who pay for it. However, frauds are not always from the merchant side. Even though it is rare, customers also make false claims in frauds. Initially all these frauds were handled manually, but with time E-Commerce is moving towards developing predictive algorithm to detect frauds and avoid them if possible.

fraud

3. Merchant Analytics : Merchants form the core of E-Commerce industry. If the merchant grows, E-commerce provider also grows. So E-Commerce players do extensive analysis for Merchants to get into new markets or set the right price for their goods. For instance, Amazon can recommend a Cricket Bat vendor to keep Hockey sticks because of a growing demand in his locality. Such decisions would have been much more expensive for the vendor, had they not partnered with E-Commerce players.

merchants

4. Recommender Systems : As soon as I hear Recommender engines, I imagine YouTube. Recommender systems in E-Commerce industry is not very different from YouTube. These engines serve as blueprint for customer to navigate through the store of this virtual environment. Recommender engines have been the strongest contribution of analytics to technology.

recommendation engines

Credit : TIME Magazine

5. Product specific analytics : These teams generally work on product specific details for example – Satisfaction rate of customers for a product, forecast of sales for a product etc. Their work cut across verticals and are specific for a family of product or a single product.

apple_products

6. Online Marketing Analytics : As E-Commerce provides you a virtual environment to buy stuff, they have to market on the virtual environment extensively. The online marketing team generally works on bidding for ads on Google or other websites. They analyse the funnel of new prospect customers and maximize the likelihood of a customer clicking an ad .

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CRH7EN concepts of online advertising, with message on keyboard.

7. User Experience Analytics : This probably is the biggest task for analytics in E-Commerce industry. It’s all about customer centricity because of the ease to shift from Amazon to Flipkart. This team primarily works on creating the right architecture of the website. This will include how is product searched across portfolio, what decides the rank ordering of products for a particular search, what is the best landing page of a customer coming from Facebook etc. They also test what type of layout is better for what type of customers.

user_experience

Image Source : Elliance

This list is not exhaustive as I can imagine a number of other areas where analytics can play a role. However, this list of 7 roles cover majority of analytics resources in E-Commerce industry.

End Notes

I hope this article gave you a sense on how E-Commerce industry leverages analytics to make customer experience delightful. I will encourage people working in this domain to add to this article and comment on the roles identified. Also in next few articles I will cover few more industries to provide a more holistic view of how analytics is shaping different industries.

Did you enjoy reading this article? Do you think we missed calling out any role in E-Commerce industry? Are you inspired by any of the above mentioned roles?

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

Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

Wasifur
Wasifur

Hi Tavish, Real time pricing analytics needs to be added. Its very important for online retailers to maximize profit. Today users can compare prices from different portals before submitting any order. How good it would be if the E-Retailer could know whats the max price user can pay for that product at that time. This price needs to be in real time and context driven based on user's historical data, sentiment analysis data from social media, competitors price data (through crawling).

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E commerce supply chain
E commerce supply chain

Amazing information. E commerce is constantly changing, forcing retailers and transportation & logistics businesses worldwide to think outside of the box. Thanks for sharing the above information.

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Uday Padhye
Uday Padhye

Hi, Hope you are doing fine... Do you have any idea on where I can learn about Python Programming for checking out what's happening on the website... I would appreciate if you can provide a link...that gives me all the information....a downloadable file would also do... Regards, Uday Padhye [email protected]

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