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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

Unleashing the Power of Amplitude Maps in Product Management

Aashish
Aashish Last Updated : 07 Feb, 2023
5 min read

Introduction

Are you ready to unlock the hidden key to success in product management? With the power of amplitude maps, you can now leverage data-driven decisions to take your products to the next level. Transform the way you approach product management and unlock unprecedented success. It’s time to unlock the secret! Amplitude Maps are visual representations of product usage data that help product managers to better understand their customers and their behavior. They display the frequency and intensity of actions taken by users within a product.

Learning Objectives:

  1. Understand Amplitude Maps and their applications in product management.
  2. Learn about the importance of amplitude as a term in product management.
  3. Understand the benefits of using Amplitude Maps as a tool to visualize product data.

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

Table of Contents

  1. What are Amplitude Maps?
  2. Amplitude Maps in Product Management
    2.1 Measuring User Engagement
    2.2 Understanding User Segments
    2.3 Improving Product Development
  3.  Examples of Amplitude Maps
  4. Conclusion
Amplitude Maps
Source: Envato Elements

What are Amplitude Maps?

Amplitude Maps are graphical representations of user behavior data that show how frequently and intensely users interact with a product. They provide product managers with a quick and clear understanding of the most important usage patterns and metrics, including event frequency, distribution, and session length.

The term “amplitude” in product management refers to the intensity or strength of an action taken by a user within a product. This can include things like the number of times a user clicks a certain button or the amount of time spent on a particular page. They help product managers to see the distribution of these actions across their user base and to identify trends and patterns.

Amplitude Maps are a powerful tool for product managers because they visually represent complex data. This makes it easy to understand how users interact with a product and identify improvement areas. With Amplitude Maps, product managers can track the success of new features, optimize user onboarding, and make data-driven decisions about product development and marketing.

The importance of Amplitude Maps in product management lies in their ability to provide product managers with a quick and clear overview of their products’ most important metrics and usage patterns. This information is crucial for making informed decisions about product development, marketing, and customer support.

Amplitude Maps in Product Management

Measuring User Engagement

A. Tracking user behavior and interaction with the product: Amplitude maps can help product managers track user behavior and interaction with the product by visualizing the data collected from user interactions. This data can be collected from various sources, such as website analytics, mobile app usage data, and customer feedback. The data can be used to understand how users interact with the product, which features are used most and least, and where users are encountering issues.

B. Identifying the most used and least used features: By visualizing the data collected from user interactions, they can help product managers identify their product’s most and least used features. This information can then be used to prioritize the development of new features, improve the user experience, and increase customer satisfaction.

C. Measuring user satisfaction and churn rates: User satisfaction and churn rates are important metrics that can impact the success of a product. Amplitude maps can help product managers measure these metrics by tracking the behavior of users over time and comparing it to the desired outcomes. This information can be used to make data-driven decisions to improve the product and retain users.

Understanding User Segments

A. Segmenting users based on behavior and demographics: User segmentation is a powerful tool that can help product managers understand the needs of different groups of users. They can help in this process by visualizing user behavior and demographic data. This information can be used to create user segments based on common behaviors and characteristics, such as usage patterns, location, age, and more.

B. Customizing user experiences based on segment data: Once user segments have been created, amplitude maps can help product managers customize the user experience for each segment. This can be done by making data-driven decisions on the features and functionality that are most important for each segment. For example, a product may offer a more streamlined experience for infrequent users while offering more advanced features for power users.

Amplitude Maps

Source: Envato Elements

Improving Product Development

A. Prioritizing feature development based on user engagement data: They can help product managers prioritize the development of new features based on user engagement data. By visualizing how users interact with the product, product managers can identify which features are most important to users and prioritize their development accordingly. This information can also be used to make decisions on when to sunset or update underutilized features.

B. Making data-driven decisions in product road mapping: Product Road mapping is an important part of product development and management. Amplitude maps can help product managers make data-driven decisions in this process by providing a clear understanding of user behavior and engagement with the product. This information can be used to inform product roadmaps and make decisions on which features and functionality to include in future releases.

Examples of Amplitude Maps

1. Example of tracking user engagement in a mobile app: In this example, a mobile app could use amplitude maps to track user engagement by visualizing the data collected from user interactions with the app. This information could be used to understand how users use the app, which features are the most popular, and where users encounter issues.

2. Example of understanding user segments in a B2B SaaS product: In this example, a B2B SaaS product could use amplitude maps to understand user segments by visualizing user behavior and demographic data. This information could be used to create user segments based on common behaviors and characteristics, such as usage patterns, location, industry, and more.

3. Example of improving product development in an e-commerce platform: In this example, an e-commerce platform could use amplitude maps to improve product development by visualizing user behavior and engagement data. This information could be used to identify which features and functionality are most important to users, prioritize the development of new features, and make data-driven decisions in product road mapping. For example, the platform could use amplitude maps to understand the most popular categories of products and prioritize the development of features related to these categories.

Amplitude Maps
Source: Envato Elements

Conclusion

Amplitude Maps are an invaluable tool for product managers when it comes to understanding user behavior, segmenting users, and making data-driven decisions. With them, product managers can track user engagement and usage patterns, identify the most used and least used features, measure user satisfaction and churn rates, and prioritize feature development. By leveraging the power of Amplitude Maps, product managers can unlock unprecedented success in product management. 

Key Takeaways: 

1. Amplitude Maps are visual representations of user behavior data that help product managers better understand their customers and their behavior. 

 2. They help product managers identify the most used and least used features of their product and prioritize the development of new features. 

 3. By leveraging the power of Amplitude Maps, product managers can unlock unprecedented success in product management.

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

amplitude mapsblogathondata visualizationgraphsProduct Management
Aashish
Aashish

Product Manager with 10+ years of experience in driving product innovation, managing product development and leading agile product teams. Experienced in developing product strategies and executing on tactical plans to ensure successful product launches and profitable product roadmaps. Skilled in developing product roadmaps, managing product portfolios, and creating product requirements.

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