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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

Top Customer Analytics Interview Questions

Prateek Majumder
Prateek Majumder Last Updated : 15 Dec, 2022
6 min read

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

Introduction

Data and Information about a Customer are important for all businesses and companies. For a business to be data-driven, a Company needs to be highly data-driven and focus highly on customer analytics. Information about customers can be collected from many sources. It may be data collected during online purchase transactions, sales at retail stores, online surveys, etc. All this data can give valuable insights into the customer’s demographics, interests, purchasing power, etc. To understand all this, we take the help of customer analytics.

What is Customer Analytics?

Customer analytics is the process of using analytics to study customer behavior. This is done in order to make better business decisions. As a result, it is a process of studying customer data and information. This leads us to understand and interpret customer behavior in order to make effective business decisions. As a result, many businesses use this information for site selection, direct marketing, and other purposes. Customer analytics can help you attract and retain the most profitable customers.

Customer Analytics

Source: https://blog.aspiresys.com/digital/big-data-analytics/boost-your-organizations-performance-using-customer-analytics/

Customer Analytics is a very important topic for Data Analyst, Product Analyst, and other data-oriented roles. Let us check some top Customer Analytics Interview Questions.

1. What is the Aim of Customer Analytics?

Customer analytics seeks to create a single, accurate view of an organization’s customer base. This can be used to inform decisions about how to best acquire and retain future customers. It can also recognize high-value customers and recommend proactive ways to engage with them.

For example, analytics that has been properly implemented can aid in many aspects of the business. These include forecasting consumer responses to marketing and advertising campaigns, brand adaptation, and comprehension of the customer experience. Customer experiences, customer satisfaction, marketing campaign success, and other factors can also be evaluated.

2. What are the Main 4 Types of Analytics?

There are four types of analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

data analytics

Source: https://www.scnsoft.com/blog/4-types-of-data-analytics

3. What is Clustering?

Clustering is the process of dividing a set of data into groups. This is done so that data in the same group are more similar to other data in the same group and dissimilar to data in other groups. A collection of objects based on their similarity and dissimilarity can be called a Cluster.

For example, in Customer Analytics, Customers can be clustered or grouped on some basis. These include their spending habits, their income, profession, and other demographic data.

4. What is Product Analytics?

Product analytics is a powerful set of tools that enables product managers and teams to evaluate the performance of the digital experiences they create. Product analytics provides critical data for optimizing performance, diagnosing problems, and correlating customer activity with long-term value.
Several examples of applications of product analytics in business include:
  • Identifying the most active clients to increase retention.
  • A/B testing new features to make quick conclusions about the product.
  • Identifying the app’s retention hook to keep users coming back.
  • Tracking users from the landing page to the checkout page.

5. What is Customer Lifetime Analytics?

In a broad sense, a customer’s lifetime overlaps with the customer’s journey and experience. However, the Customer Lifetime Value is valuable in this type of analytics (CLTV). This metric indicates how much revenue you can expect from a single customer throughout the business relationship.The process for calculating this metric may vary depending on your business; in some cases, bringing in a consultant may be more effective in identifying the right formula for your company. However, multiplying the average retention rate by the average number of purchases and then multiplying the product by the average deal total is a simple way to calculate this metric.

6. What are Some of the Best Customer Analytics Tools?

Some of the best Customer Analytics tools are Google Analytics, Adobe Analytics, MixPanel, IBM Watson Customer experience analytics, and so on.

7. Mention Some Differences Between Customer Analytics and Web Analytics?

Customer analytics is primarily concerned with converting customer knowledge into actions for the organization and thus includes a customer-facing department.Web analytics is primarily concerned with online data and website usage optimization and thus does not include a customer service department.

Customer Analytics works with actual and genuine customers who are interested in a product or service.

On the other hand, Web analytics is concerned with anonymous data traffic rather than individual customers.

8. What is Customer Service Analytics?

Customer service analytics is gathering and analyzing customer feedback to gain useful insights. It can assist you in better understanding your customers’ needs and expectations, leading to better customer experience (CX) strategies and increased customer loyalty and retention.

For example, customer service analytics enable us to identify customer pain points and ways to position our business or product as a solution to those issues. Even the effectiveness of the customer service channels can be evaluated.

9. What Teams Contribute to the Customer Analytics Process?

An interdisciplinary group of leaders from various departments, such as marketing, sales, customer service, information technology, and business analysts, frequently manages customer analytics. To obtain meaningful insights, the group must agree on which business metrics can provide a complete picture of the customer experience.

10. What are Some of the Best Customer Analytics Practices?

Some important Customer analytics best practices are as follows:

  • Analyze how to distribute a product or service to customers across all channels.
  • Assess and comprehend customers about the brand and whether or not a customer is satisfied. This can be accomplished by combining quantitative and qualitative surveys.
  • Engage with customers at the appropriate time and through the appropriate channel.
  • Predict churn and take steps to increase a customer’s lifetime value.
  • To increase sales, identify trends in big data, and analyze online behavior.
  • Optimize the customer journey through personalized selling and market segmentation by determining which customers are likely to purchase one product over another.

11. What is an Outlier? Give an example of a Real-life Outlier in a Dataset.

Outliers in a dataset are values that considerably deviate from the average of a dataset’s defining characteristics. We can identify either measurement variability or an experimental error with the aid of an outlier.For example, in a retail dataset of 100 customers, 96 customers have purchased in the range of 1000-2000. The other four entries are 9000, 6800, 7000, and 12000. These four data points are very far away numerically from the other data. Hence, they qualify as outliers.

outlier

Source: https://www.geeksforgeeks.org/types-of-outliers-in-data-mining/

12. What is Collaborative Filtering?

Collaborative filtering (CF) generates a recommendation system based on user behavioral data. It eliminates information by scrutinizing user behaviors and data from other users. This approach assumes that persons who agree to assess specific goods will probably continue to do so. Users, things, and interests comprise the three main components of collaborative filtering.

Customer Analytics

Source: https://towardsdatascience.com/from-vague-to-value-data-science-analytics-practitioner-insights-fed92a4bda08

The field of e-commerce offers one of the best instances of collaborative filtering. An e-commerce website that you browse will present you with some suggested products, as you can see. There are some items there that are an exact match to what you were looking for. Now, you might wonder how the website can discern your interests. Collaborative filtering is solely to blame for everything. Amazon has one of the best Collaborative Filtering models, which shows similar products to all their customers.

13. Explain Normal Distribution:

The Normal Distribution is also called the bell curve or the Gauss distribution. It is a fundamental concept in statistics and the foundation of machine learning. It specifies and quantifies how the means and standard deviations of a variable’s values differ, or how the values are dispersed.

Customer Analytics

Source: https://www.scribbr.com/statistics/standard-normal-distribution/

The above diagram depicts normal distribution.

14. What is Time Series Analysis, and What are Some Fields Where it can be Used?

When working with trend analysis and time-series data, in particular, time-series analysis, or TSA, is a widely used statistical technique. The presence of the data at specific time intervals or predetermined periods is a feature of time-series data. Time-series analysis is a technique for analyzing data points over time. Time series analysts record data points at consistent intervals over a set period rather than intermittently or randomly.

Numerous fields use time series analysis, including statistics, business, economics, and many others. In Customer Analytics, TSA can be used for Sale Prediction, Web traffic prediction, and so on.

Conclusion

The customer journey can be optimized by businesses that have a thorough understanding of their clients’ purchasing patterns and lifestyle preferences. Large quantities of precise data are necessary for accurate analysis. Without it, analysis insights could be completely off and useless.

To summarise:

  • Customer analytics is the process used by businesses to gather and understand customer data in order to improve decisions.
  • It often takes the form of software that gives businesses knowledge of how their users behave.
  • Studies show that businesses that use customer analytics are more profitable, and these insights drive businesses’ efforts in sales, marketing, and product development.

The most well-known brands in the world today take their consumer analytics very seriously. The financial performance of the business can greatly be changed by customer analytics.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Prateek Majumder
Prateek Majumder

Prateek is a dynamic professional with a strong foundation in Artificial Intelligence and Data Science, currently pursuing his PGP at Jio Institute. He holds a Bachelor's degree in Electrical Engineering and has hands-on experience as a System Engineer at TCS Digital, where he excelled in API management and data integration. Prateek also has a background in product marketing and analytics from his time with start-ups like AppleX and Milkie Way, Inc., where he was involved in growth campaigns and technical blog management. Recognized for his structured thinking and problem-solving abilities, he has received accolades like the Dr. Sudarshan Chakraborty Award for Best Student Performance. Fluent in multiple languages and passionate about technology, Prateek continues to expand his expertise in the rapidly evolving AI and tech landscape.

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wilson cristina

Hi Prateek Majumder, Thank you for sharing very informative article! It will useful whoever searching a job in data analytics domian!

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