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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

Learn Analytics using a business case study : Part III

Tavish Srivastava
Tavish Srivastava Last Updated : 18 Apr, 2015
5 min read

last episode

Data based analytics and intelligence practices typically continue to grow complex over time. This is because we get more data over time, computational power is becoming cheaper by the day and businesses are facing situations where they can either innovate or perish.

Same applies to our case study. There are various levels of complex analysis which can be applied to this business once we start getting more and more customer information / data. In this concluding part of the case study, we will talk about framing targeted marketing strategies through propensity modelling or simple segmentation.

cust centric 2

[stextbox id=”section”]A quick Recap  :  [/stextbox]

You have recently started a Video CD rent shop. After 2 months you realize that there is tough competition in the market and you need to make a more customer-centric strategy to stand out in the market. You have already built all the datasets and collected data for first 2 months (Read part 1 here). After 2 months, you used insights from portfolio data to define your mass market strategy and acquisition target localities (Read Part 2 here).

Now, you are in 5th month and business has grown to 750 customers. You have 4 months of data now and are wondering how to make your strategies even more customer-centric?

[stextbox id=”section”] Month 5 :  [/stextbox]

Here are some high level metrics for your business :

table4

Till this point, all your analysis has been on portfolio level. You, want to create a more granular optimized marketing strategy. Here, we will use one the best industry approach to classify our customer portfolio into meaningful segments and then define targeted strategies for each of them.

Predictive modelling is still out of reach because of relatively small size of portfolio and data points.

cust_centric1

Targeted engagement Strategy :

Step 1 : Form group using RFM approach :

This is one of the best and most commonly used technique. It is very quick to implement and its flexibility makes it easy to apply it across industries. This technique can be used in multiple ways. Following is a demonstrative example:

THE R : R stands for measuring Recency. In this case, we will take into account the number of days since the customer took the last CD. We will score each customer based on the band he qualifies.

Recency

THE F : F stands for frequency. In this case, we will want to know the total number of CDs a customer has rented in his entire lifetime with us. Again we will band the frequency and score accordingly

freq band

The M : M stands for Monetary. Because, in our case each product costs the same, monetary parameter will behave very similar to frequency, there is no use of this metric.

Let us consider a case, customer X has bought 8 CDs in total and bought his last CD 6 days before. His total score becomes 8/10. Similarly, we score each of the customers. First thing you need to check is the distribution and the stability of the score.  Following is the distribution of scores as on month 4 and month 5:

month5

month4

Both the curves are approximately normally distributed and have similar proportion of population in each score band.

Step 2 : Find the profile of each customer using scores of 2 months :

For example, a customer with high score in both the months fall into loyal bucket (1st quadrant).

score trends

Step 3 :  Find the right strategy for each of the groups

Loyal customers : These are the customer who were highly valuable and are still highly valuable. This segment is our loyal base and it is important to delight these customers because they give us a constant stream of revenue. All our customer delight / engagement strategies need to focus on these customers. Let’s list down a few of these possible engagement for “Up-selling” :

1. Based on the preferred genre, send updates of new releases or newly acquired CDs.

2. Send weekly updates of star rating on new movies of preferred genre.

3. Send festive promotional offers to up-sell.

4. Give free delivery services.

Grow customers : These customers are showing a growing pattern and are becoming more and more engaged with us.  The right strategy with these customer is to delight them and grow their engagement further. We again add these customer to the up-sell group. A similar strategy as of Loyal customer might be helpful in making them more engaged.

Win back customers : These customers are showing a declining pattern in terms of engagement/revenue. Here we need some different set of strategies to arrest their likely attrition and bring them back. If we think about it, there are possibly 2 reasons for their possible attrition:

a) They are now shopping from other shops

b) They have declined their overall requirement

Let’s try to list down some strategies to increase their wallet share or transfer their demand from other vendors to our store :

1. Win back offers : Give promotional offers to get them back on books. Offers like ” Buy 2 CDs a month and get third 50% off” will differentiate us from other vendors.

2. Customer specific engagements : Customer all wants to be treated deferentially. A simple promo offer like ” Get 25% off on next CD” might not work as good as ” We know you like watching Romantic movies. Here is “If Only” one of the best in genre 15% off only for you”.

Other strategies :

There can be many other data driven strategies, which can be implemented at this point in time. Some of these strategies are as follows :

1. Inventory optimization model based on past demand trends

2. X-sell of other rental services to loyal customers like renting home theatre.

3. Mutually share data with Home theater store and bring off-us customers with profile similar to loyal customer on board.

[stextbox id=”section”]  End Notes :  [/stextbox]

Strategies discussed in this article are meant to be food for thought for the viewers. These are by no means comprehensive. If you have examples of techniques, which you implemented in your industry and can be useful for this case study, please share them for benefit of wider community.

At some point in future, I will also cover how can this business use predictive modelling once it starts operating on a larger scale.

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

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Kumar Avinash
Kumar Avinash

Hi, Nice case studies to begin with. As i am working for a telecom sector and very much interested in knowing some case studies related to that, like in south India the vodafone basket their profit to 100 crore in 2013 financial year for Q3-Q4. Now he wants to increase his profit from 100 crore to 110 crore in Q2-Q3 for financial year 2014. So for this case studies it will be helpfull if you can focus on what are the KPI's that we should look for, how to go for customer centric approach. Thanks & Regards, kumar

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Tavish
Tavish

Avinash, Thanks for reading . To make a customer centric strategy, you should give higher weigtage to customer preference metrics. Say, even if you find the expected profit of pushing product X to a customer higher than product Y, but the customer actually prefers product Y. Customer centric apporach will bring you to the solution of offering product Y to the customer even at a cost of not selling a higher profitable product. But in general customer centric approach will converge to the same solution as that of maximizing the profit. We have recently published an article on customer centricity, which include some relevant case studies. Click here (http://www.analyticsvidhya.com/blog/2014/03/analytics-helps-organization-customer-centric/) to view this article. Hope this helps. Tavish

123
Swapneel
Swapneel

Very nice case study and well explained with details..

123

Tavish
Tavish

Thanks Swapneel

123
Akshaya
Akshaya

Hi Tarun, Very Interesting case study!

123

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