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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

How to Use Data Science for Marketing?

A
Analytics Vidhya Last Updated : 18 Jun, 2023
6 min read

Data science is a game-changer for marketing professionals in today’s digital age. With vast amounts of data available, marketers now have the power to unlock valuable insights and make data-driven decisions that drive business growth. Data science for marketing is a discipline that combines statistical analysis, machine learning, and predictive modeling to extract meaningful patterns and trends from customer data. By understanding customer behavior preferences, and market dynamics, marketers can create targeted campaigns, optimize marketing strategies, and deliver personalized experiences to their audience. This article will explore the exciting world of data science for marketing and discover how it revolutionizes how businesses connect with their customers.

Table of contents

What is Data Science in Marketing? 

Marketing involves selling or reaching the desired audience with the product or service of a company. The process requires information about the audience, such as their interest, behavior, payscale, requirements, and past experiences concerning their needs.

Data collection is coupled with scientific study and analysis to make it usable and worth making decisions. This is where data science comes into the picture! Data science for marketing analytics helps find the right direction for a company to work on and accurately reach its audience. It removes bothering the unintended audience and saves expenditure of the company, helping them earn profit.

9 Ways to Use Data Science in Marketing

Here are some ways in which Data Science supports Marketing:

1. Collecting and Managing Marketing Data

The initial step in leveraging data science for marketing analytics is crucial, as it lays the foundation for analysis and guides future business actions. Before data collection, it is essential to define the purpose of data gathering, provide a clear direction and eliminate unproductive efforts. Depending on the needs, data sources such as demographics, website traffic, sales data, and social media engagement are identified.

Once the sources are determined, the next step involves implementing data collection mechanisms. This includes incorporating tracking codes on websites and landing pages, designing surveys or forms, integrating APIs to retrieve data from external platforms, and more. Subsequently, the collected data undergo cleaning and preprocessing, ensuring it is organized and stored appropriately for interpretation based on specific requirements. This meticulous process sets the stage for effective data analysis and insights into data science for marketing.

2. Exploratory Data Analysis for Marketing Insights

Data science in marketing involves extracting meaningful insights from data through various processes. This includes examining and summarizing data to identify relationships, patterns, and trends. Descriptive statistics help understand data distribution and identify patterns.

Exploratory data analysis in data science and marketing analytics encompasses data visualization, segment-based analysis, correlation analysis, and time series analysis. Segmentation analysis categorizes data based on customer behavior or campaign performance. Correlation analysis calculates the correlation coefficient to understand marketing outcomes. Time series analysis focuses on temporal patterns, trends, and seasonality.

Text mining is beneficial for analyzing captions and comments for social media data. Hypothesis testing evaluates the effectiveness of different marketing strategies using statistical tests for unbiased answers. Summarizing these methods provides a holistic view, guiding the extraction of valuable insights from data and enabling the development of effective marketing strategies.

3. Predictive Analytics for Marketing Forecasting

Using historical data and statistical analysis, predictive analytics in marketing aims to achieve desired outcomes. This involves capturing people’s responses, viewpoints, challenges, and expectations related to products and services. In marketing forecasting, key elements include data collection from diverse sources and thorough preparation. Essential features are selected, and predictive modeling techniques such as regression, time series analysis, random forests, decision trees, and machine learning algorithms are applied.

The data is divided into training and validation sets, with training focusing on parameter adjustments and performance enhancement. Validation measures accuracy, identifies potential issues or biases, and assesses predictive power. Then the validated data predict various scenarios. Scenario analysis and continuous data integration complete the predictive analysis process, enabling data-driven decision-making, optimization of marketing strategies, and effective resource allocation.

4. Customer Segmentation and Targeting

Customer segmentation and targeting in digital science and marketing involve dividing customers into groups or categories to enhance targeting efforts. This approach ensures that businesses meet specific customer needs while avoiding unwanted advertisements. By leveraging segments, businesses can improve effectiveness, save resources, increase customer engagement, and stay true to their brand’s purpose. The process begins with data collection and progresses to identifying segmentation variables that define the segments. These segments can be demographic, geographic, or psychographic.

Segments are created by identifying patterns and similarities, followed by segment profiling. Evaluating segments involves considering growth potential, alignment with company objectives, size, and profitability. The targeting strategy outlines how customer segments will be addressed, and actions such as personalization, communication, and adaptation are taken based on customer needs. This comprehensive approach enables businesses to optimize their marketing efforts and reach their target audience.

Also Read: 10 Must-Have AI Customer Segmentation Tools for Effective Marketing

5. Marketing Attribution and ROI Analysis

Data science for marketing analytics involves two crucial components: digital marketing attribution and ROI analysis. Digital marketing attribution focuses on measuring the impact of various marketing channels and strategies, identifying the most effective ones in terms of customer engagement, conversions, and sales. This understanding helps optimize marketing strategies and resource allocation. The collaboration between data science and marketing analytics drives these insights.

On the other hand, ROI analysis evaluates the profitability and efficiency of marketing investments. It primarily focuses on financial aspects, comparing generated revenue to marketing expenses. This analysis enhances financial performance, guides budget allocation, and aids resource planning. It exemplifies the budget-driven influence of data science and marketing analytics. Together, these components provide valuable insights to enhance marketing effectiveness, optimize strategies, and achieve better returns on investment.

6. Sentiment Analysis and Social Media Monitoring

Data science plays a pivotal role in digital marketing by providing insights into the brand’s position and the customer’s perspective. Leveraging social media, sentiment analysis or opinion mining uncovers public sentiments expressed in chats, comments, reviews, and other texts. Natural Language Processing (NLP) is employed to classify text as positive, neutral, or negative, enabling applications like customer feedback analysis, reputation management, and market research. Social media marketing focuses on platforms such as Linkedin, Twitter, Facebook, and Instagram, using API tools for real-time or historical data analysis. It tracks brand mentions, campaign performance, customer behavior patterns, and feedback, facilitating engagement, competitor analysis, and a deeper understanding of customer needs.

7. Marketing Automation and Recommendation Systems

Data science in digital marketing automation uses software to automate the workflows, marketing and processes. They improve operational efficiency by generating automatic campaigns, text messages, email and communication systems for social media. It helps in timely and personalized connection. Further, besides connectivity, it also aids in tracking and generating customer data. 

The recommendation system in data science and marketing is based on algorithms and techniques to suggest products or services as per customers’ timely and updated needs. It helps in personalized recommendations and provides cross-selling and upselling opportunities. The consequence is an increase in order value and customer satisfaction. The recommendation systems work on continuous updates and learning of machine learning algorithms and adaptation to customer needs. 

8. Ethical Considerations and Privacy

The marketing field relies heavily on the abundance of data generated, and it is crucial to handle this data ethically and responsibly to avoid legal issues. Respecting the privacy rights of individuals is of utmost importance. Transparency plays a key role in data collection, ensuring that users are accurately informed about how their data will be used, the benefits it brings, and any sharing with third parties. Obtaining informed consent is essential to ensure a complete understanding of the purpose behind data collection.

Data science and marketing analytics must prioritize privacy and data protection. This includes safeguarding data against unauthorized access and breaches through encryption and regular security audits. Data anonymization is also important to prevent individuals from being personally identifiable. Adhering to regulations such as CCPA, GDPR, or country-specific laws is essential to maintain ethical data practices and privacy standards.

By upholding ethical considerations and privacy principles, marketers can effectively leverage the power of data science and analytics while protecting individuals’ data and complying with legal requirements.

9. Voice of Customer (VoC) Analysis

Customer feedback is a valuable asset in marketing data science. It represents the voice of the customer, offering insights into their experiences with a brand. Gathering feedback is effortless, yielding a wealth of information from numerous customers. Analyzing this data provides deep insights into understanding the target audience, their needs, and market trends. The process involves collecting data from diverse sources like social media, surveys, customer reviews, live chat, and sales calls. Analysis can be quantitative (using metrics like net promoter score) or qualitative (through sentiment analysis). Leveraging the voice of the customer empowers businesses to address issues, prioritize product development, and engage their audience through targeted marketing campaigns, ultimately fostering growth and loyalty.

Ready to Take Data-Backed Decisions

The growing popularity and utility of AI have raised concerns about job displacement, but there are promising opportunities that come with its emergence. While data science traditionally involves programming and coding, there is now a possibility to leverage AI without extensive coding knowledge. Introducing our No-code AI for Business Professionals course, which empowers you to master AI and marketing without delving into complex coding. Regardless of your background in computer science or understanding of programming, you can confidently navigate the realms of data science and marketing analytics. Explore the course today!

Frequently Asked Questions

Q1. How is data science used in marketing?

A. Data science is used in marketing to analyze and interpret large volumes of data, uncover patterns and trends, and make data-driven decisions for targeted marketing campaigns, customer segmentation, personalization, and performance measurement.

Q2. Can data scientists work in marketing?

A. Yes, data scientists can work in marketing roles. They play a crucial role in leveraging data analytics, predictive modeling, machine learning, and other data science techniques to optimize marketing strategies, improve customer insights, and drive business growth.

Q3. Should a digital marketer learn data science?

A. Learning data science can greatly benefit digital marketers. It equips them with the skills to analyze and interpret data, gain deeper customer insights, make data-driven decisions, and implement effective marketing strategies. It enhances their ability to leverage data for improved campaign performance and ROI.

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