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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

Segmentation of customers for effective implementation of analytical projects

Kunal Jain
Kunal Jain Last Updated : 27 Aug, 2021
5 min read

According to a survey conducted by Bloomberg in 2011 (on companies exceeding $100 Mn in revenues), 97% of these companies have embraced Analytics in some form. However, only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions.

This gap in investment and desired results might be higher in developing markets like India. While, there are bunch of reasons which contribute to this gap, one of the key reason contributing to this gap is effective customer management.

There are various kinds of customers we interact with, while working on analytical projects. They possess different understanding of analytics and have varied expectations from it. Keeping a similar engagement with these customers is like preparing for a disaster.

Over last few years, I have developed a segmentation which I have found very useful while interacting with various customers. This segmentation not only helps you on how to best interact with various customers, but also lays maps these customers on a journey to convert them into proponents of Analytics.

[stextbox id=”section”]Segmentation of Analytics customers:[/stextbox]

Following is the segmentation I use for engaging with customers (In case you want to read more on Segmentation & how to create one, click here):

customer_segmentation_v1

The customers are divided in 4 categories basis their understanding / maturity about Analytics and the potential they see in Analytics. Following is a brief description of each of this dimension:

  • Analytical maturity of the audience: This dimension represents customer’s understanding about how Analytics can contribute to decision making. A customer with high maturity would not only understand what Analytics can deliver, but also what it cannot.

Some examples of aspects which I expect an analytically mature customer to understand are:

    • What is predictive modeling (from business point of view) and how can it help a business?
    • Does the customer understand what efforts will it take to implement a project?
    • There is a minimum sample size required for performing any analysis and pulling out recommendations.
    • How open is the customer to the idea of testing?
    • Does the customer understand the difference between Analysis and reporting?
  • Expectation from Analytics: By expectation, I mean the point of view a person carries towards analytical projects and thinking. A few questions which can help us map a customer on this dimension are:
    • What kind of benefits does the customer expect from Analytics? How critical are they to his function?
    • What kind of time frame does the customer have in mind for getting these benefits?
    • Is the customer looking forward to project implementation or he is implementing it under some pressure?

Once you map your customers on these 2 dimensions, they can fall in any of these 4 categories:

 

[stextbox id=”section”]Analytics Proponents:[/stextbox]

These are the group of customers who understand what Analytics can deliver and would be ready to advocate the practice in the Organization. These are the people who will come to you with specific and clearly defined problems, would be ready to collaborate / spend time and finally implement the solution. If you have any customer who falls under this category, you should make sure that you deliver / over-deliver on these projects. Hopefully, they will help you increase Analytics penetration in future through their influence.

[stextbox id=”section”]Challengers:[/stextbox]

Challengers are people who understand Analytics, but are not convinced that it can deliver on the projects in Organization. The best way to start engagement with them is to try and have an open discussion with them and understand their concerns. Being aware of these concerns would be the first step in alleviating them.

There are times when it would not be possible to have this discussion and even if it happens, it may not be entirely honest (especially if it is early in the engagement). In that scenario, the best way is to take up small but impactful projects and deliver them. Once the customers see some delivery, they would start their journey to become a proponent.

[stextbox id=”section”]Citizens of wonderland:[/stextbox]

These are often amusing group of customers. They don’t understand nuances of Analytics completely, but carry high expectations from Analytics. While in short term, these customers are easy to interact, the difference in expectation and maturity will cause difficulties in long term.

Some of the situations you will find yourself with these customers are:

  • How can Highest propensity leads of a campaign not convert? (vs. Why is the conversion rate of Highest propensity leads lower than expected?)
  • We have arrived at these recommendations after a lot of analysis and discussions, lets implement it on entire population (vs. we should test our recommendation to size the benefit and understand whether the recommendations work or not?)

How to interact with these customers?

Educating them about Analytics might be the best way for the long term. However, a balance needs to be maintained so that they remain excited about the analytics journey and become more aware of their understanding gaps.

If the customer in this category is really critical to success of your function, you can check with him if he is open to idea of joining a short Executive Programme about Analytics (2 – 3 days is good). If he is not a critical customer, you can ask one of your proponents to educate him over time through regular interactions.

Over time by bringing more education to these customers, you should aim to convert them to proponents.

[stextbox id=”section”]Skeptics:[/stextbox]

These are the most difficult set of customers. Converting them into proponents is a relatively long journey. If you have projects (of similar / more potential impact) from customers in any of the three segments mentioned above, you should focus on them first. The idea is to create enough proof of concept to improve the expectation customer has from Analytics. Another way this can be done is by talking them through case studies from same industry.

[stextbox id=”section”]Analytics implementation Strategy:[/stextbox]

customer_segmentation_strategy

Once you have identified your customers to this grid, I would suggest following in order of priority:

  • Start by making sure that you meet all the commitments / expectations for your proponents.
  • Next, try and convert challengers & Citizens of wonderland in proponents of Analytics
  • Finally, start working on expectation skeptics might have from Analytics.

Following are some interesting set of metrics which can be tracked to measure success of Analytics function:

  • Percentage of customers who are proponents provides a view on success of Analytics journey in an Organization.
  • % of customers who are either proponent or Challenger can give idea of Analytics penetration in an Organization.
  • % of skeptics in an Organization / function can tell you the readiness of Organization to adapt Analytics. If the number is high, the readiness is low.

[stextbox id=”section”]Some additional thoughts:[/stextbox]

  • A few customers might test you for capabilities first by giving small and less impactful piece of Analysis. They may look like skeptic at the outset, while they might actually be Challengers.
  • Converting a customer from one segment to another takes time. Typically, it would take 3 – 6 months for a customer to move from one box to another.

Hopefully, you will find this segmentation useful to create a strategy and roadmap for Analytics journey in your Organization. Do you think this segmentation will be helpful for driving Analytics in your Organization? Please feel free to share your thoughts in comments below.

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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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Responses From Readers

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Ahmad Khalafallah
Ahmad Khalafallah

Very helpful ideas. In some cases there are all kinds of customers in one organizations. If the proponent is capable of sponsoring a project it will be of great help to turn more to a proponent quadrant. Another issue might appear which is the resistance from inside the org. The most hurtful one is that from the data custodians. They see the analytics guys as a threat to their jobs or at least a competition. This could make a well sponsored project to fail.

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