Master Marketing Analytics & Data Science for Growth

Executive Program in Marketing Analytics and Data Science

Gain a strong grasp of data science principles and analytical techniques that empower marketing leaders to make informed, evidence-based decisions.
Learn to convert complex data into actionable insights for pricing, campaign optimization, customer segmentation, and marketing ROI improvement.
Understand how regression, Bayesian models, tree-based methods, and neural networks can be applied to real-world marketing challenges.
Use predictive, time series, and NLP-based models to forecast demand, measure attribution, and analyze brand perception from social data.
Discover how Generative AI can enhance marketing innovation—from content creation and chatbots to customer persona generation.
Gain practical exposure to the evolving landscape of AI, ensuring readiness to lead marketing innovation in a data-first business environment.

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Dates
Start Date: Saturday, 23 May 2026
Register By: 10 May 2026
Early Bird Deadline: 30 Apr 2026
Special Offer Deadline: 23 Feb 2026

Program Fee
Regular: Rs. 45,000 + GST
Early Bird Fee: Rs. 40,500 + GST
Special Offer Fee: Rs. 36,000 + GST

Duration
Live-Online, 8 weeks
2 Sessions/Week
Saturday: 10:00 am to 12:45 am IST

Click below to “Enroll and Pay” for the program –

Program Overview

The marketing analytics market size has grown rapidly in recent years. It is expected to grow from $5.35 billion in 2024 to $6.2 billion in 2025 at a compound annual growth rate (CAGR) of 15.9%.The growth in the historic period can be attributed to increased emphasis on data-driven decision making, rising competition and market saturation, shift towards customer-centric marketing, evolving consumer behavior and digital transformation, demand for measurable ROI and accountability. AI adoption among marketers surged from under 10% in 2018 to nearly 70% by 2024, driven by generative AI, personalization engines, and predictive analytics tools, as shown below.

AI adoption in Marketing — Source: Influencer Marketing Hub and SurveyMonkey summaries for AI-in-marketing adoption

This is a definitive course designed to transform your marketing intuition into data-driven power. Marketing decisions have long been using a mixture of intuitive hypotheses and supporting market research data to empirically justify/motivate those actions. Today, every click, every conversion, and every customer interaction generates massive, complex data. The challenge is no longer accessing or storing this data, but translating that data into predictable, scalable business growth. Marketing managers also need to build intuition about the right nature of data for specific inference and tease out confounding factors. This intensive program bridges the gap between marketing strategy and cutting-edge data science, enabling you to harness the true potential of advanced analytics.

This program will equip marketing leaders and professionals with the analytical mindset, tools, and frameworks to harness the power of data science for strategic marketing decisions. It enables participants to understand how data-driven insights can uncover customer behavior patterns, optimize marketing investments, and drive measurable business growth.

By the end of the program, participants will be able to confidently integrate data science principles into marketing strategy and execution, driving smarter, faster, and more customer-centric decisions.

For further reading, please visit these HBR articles: Analytics for Managers and How to Leverage Marketing Analytics To Improve Customer Relationships and Business Decisions

Program Structure

This program is structured to provide hands-on experience across the entire spectrum of marketing data applications. The program is organized into five comprehensive modules combining conceptual foundations, analytical techniques, and hands-on applications relevant to marketing decision-making. We move beyond basic dashboards to explore sophisticated models that drive real value:

Foundational Techniques for Predictive Power: We will start with core principles like Regression to quantify relationships and the essentials of Classical Machine Learning (ML) to build predictive models. We will then escalate our capabilities to understand modern Neural Networks, which form the backbone of advanced customer intelligence.

Revolutionizing Marketing Applications: We will apply these techniques to solve the most pressing marketing challenges, including:

Financial Optimization: Developing Pricing strategies and building Marketing Mix Models (MMM) to optimally allocate budgets across channels.
Customer Journey Mapping: Implementing Multi-Touch Attribution (MTA) models to credit channels and content that influence conversions accurately.
Customer Lifetime Value (CLV): Building Customer Retention Probability Models to identify and engage at-risk customers, maximizing long-term value proactively.
Personalization & Engagement: Designing and deploying Recommender Systems to boost cross-sell, up-sell, and overall customer engagement.
Voice of Customer (VoC): Utilizing Natural Language Processing (NLP) and Generative AI to create Generative Summarization of customer reviews and feedback, turning thousands of comments into concise, actionable insights for Branding and product development.

Tools and Platform Focus: Hands-On Case Studies

Crucially, this program is intensely practical. Every core concept is immediately followed by a hands-on case study focused on implementation. You will be building, training, and deploying models using the industry’s most in-demand platforms and languages:

Python: The core language for data manipulation, modelling, and deep learning.
Azure: Leveraging cloud infrastructure and services for scalable model deployment and orchestration.
Databricks: Utilizing the unified data and analytics platform for big data processing and collaborative ML development.
Kafka: Understanding real-time data streams and integrating them into models for immediate decision-making.

By the end of this course, you won’t just be using marketing technology; you will be building the analytical assets that define your competitive edge using real-world platforms and code. Prepare to elevate your strategic role and lead your organization’s transition to truly intelligent, predictive marketing.

Who is it for?

This program is designed for marketing and business professionals who want to advance their capability to make data-driven strategic decisions using modern data science and AI tools. It is particularly suited for executives who bridge the worlds of marketing strategy, analytics, and digital transformation — seeking to understand, evaluate, and apply advanced analytical methods to real business challenges. There is no prerequisite with respect to years of experience or seniority – this is open to anyone who wants to learn.

Participants can be:

Marketing and Brand Managers responsible for campaign effectiveness, pricing, and market mix decisions.
Digital Marketing Leaders driving customer acquisition, personalization, and media optimization using data.
Customer Analytics and CRM Professionals leveraging customer data for segmentation, churn prediction, and loyalty analytics.
Product and Category Managers who want to integrate predictive insights and forecasting into business planning.
Marketing Strategists and Consultants advising clients or internal teams on data-led growth strategies.
Business Leaders and Entrepreneurs seeking to understand the business applications of AI and data science without deep coding knowledge.

While prior exposure to analytics or marketing data is beneficial, participants are not expected to have a formal background in programming or statistics. The program emphasizes conceptual understanding, business interpretation, and application of data science tools for marketing decision-making.

Program Content

Module 1 – Classical Machine Learning (Regression/ Bayesian Regression)

Session 1 – Descriptive – Statistical Analysis that does not require modelling but helps build hypothesis (90 Minutes)
Session 2 – Regression Technical Fundamentals – intuition, evaluation of regression models (90 Minutes)
1. ANOVA F-statistics, R-squared, Holdout MAPE
Session 3 – Price elasticity using Regression (90 Minutes)
1. How to do feature engineering for explainable models like price elasticity
2. Role of variable transformations
3. Communicating inference – simulations
4. Case studies – SKU price elasticities and store-level price elasticities
Session 4 – Bayesian Estimation of Regression (90 Minutes)
1. Priors and Posteriors, Bayes Theorem
2. Role of Hyperparameter Priors
3. Model validation – Effective Sample Size, R-hat, BIC, LOOCV
Session 5 – Marketing Mix Models with Bayesian Estimation
1. Ad Stocking, Saturation in Demand Model
2. Using Priors for Model training
3. Simulation – Response Curves
4. Optimization
5. Hands-on with Marketing Mix Models data for a CPG Brand

Module 2 – Classical Machine Learning II (Supervised learning methods and Neural Nets)

Session 6: Predictive Models using Trees (90 Minutes)
1. Model Training – objective, loss functions
2. Holdout and Cross Validation, and Hyperparameter Tuning
3. Case Study – Predictive Models for customer promotion uptake
Session 7: Customer Upselling and Customer Churn (90 Minutes)
1. Logistic Regression models (explainable model)
2. ROC Curves
3. Classification using Random Forests, Gradient Boosting methods
4. XGBoost as an off-the-shelf predictive model Versus other methods – trade-offs
5. Case Study – Customer Churn Banking Datasets

Module 3 – Sequence Models (Time Series as well as Neural Nets)

Session 8: Time series forecasting (90 Minutes)

1. Forecasting via time series versus XGBoost like methods on multiple forecast data
2. Case Study – Price/Time forecasting at a food delivery app

Session 9: Multi-touch attribution (90 Minutes)

1. Neural Network sequence Models – LSTM, GRU
2. Common problems – vanishing gradients, overfitting, data quality and quantity
3. Case Study – Marketing spends on a lower funnel and upper funnel marketing mix using multi-touch attribution data

Module 4 – Natural Language Processing

Session 10: Branding, Brand Perceptions (90 Minutes)
1. Traditional role of surveys and substitutes!
2. Case Study – Perception Maps using social data to understand brand parity, differentiation and white spaces
Session 11: Word of mouth, social data (90 Minutes)
1. Idea of Language – word, sentence embeddings
2. Making sense of multi-dimensional data of many word embeddings – dimension reductions using manifolds – t-SNE
3. Case Study – Using review data to understand product preferences and feedback
Session 12: Recommender Systems (90 Minutes)
1. Collaborative Filtering and Content based filtering
2. Hybrid recommenders
3. Recommenders with multi modal inputs
4. Case Study – text-based recommenders

Module 5 – Generative AI

Session 13: Generative Summarization, RAG based chatbots on Marketing brand health or such survey data
Session 14: Optimizing Creatives using Generative AI models
1. Case Study – Generating Static Ads/ Display ads using generative AI models
Session 15: Using Generative AI to augment existing marketing data
1. Case Study – Creating Personas using Generative AI and existing customer segmentation data

Click below to “Enroll and Pay” for the program –

Key Takeaways

Executive Program in Marketing Analytics and Data Science

Faculty Profile

Avadhoot Jathar brings Data science experience of over 10 years and currently works as a Senior Data Scientist at the Data Science Team of Kantar. He’s previously worked at Marketing Analytics Consulting firms and has advised several CPG, Hospitality, and Retail clients on various marketing decisions. He has worked on pricing, branding, marketing mix models, category management, customer relationship management decisions using a variety of Classical Machine Learning techniques, probability models and econometric models. Currently, he works on various Natural Language processing and Generative AI-based use cases around customer feedback, reviews and qualitative data in surveys.

He is a Fellow in Quantitative Methods (Doctorate) from the Indian Institute of Management Bangalore. He likes teaching and has previously taught analytics courses/guest lectures at IIM Bangalore, IIM Trichy and IIM Udaipur full-time MBA programs. He has been a regular guest faculty at IIM Bangalore for Analytics/Data Science executive programs.

Certificate

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Program Schedule

Schedule: Two sessions of 75 minutes each every week – Saturdays between 10:00 am and 12:45 pm (two days a week).

The program starts on Saturday, 23 May 2026 and will run for 8 weeks. The program has a total of 15 sessions.

Delivery Mode: Live-Online through Zoom

Application and Enrollment Flow

To enroll and apply for the program, click on the button below –

The payment and enrollment happen through our enrollment and learning management website – https://lms.labinmotion.com . We offer easy installment plans for all our programs. This can help plan your cash flows. We offer an additional 5% discount on group registrations for 3 or more people from a single organisation. Please email support@labinmotion.com for group registrations, and we will guide you through the enrollment process.

Labinmotion application flow

Contact For Queries

Parthasarathy S
Ph. +91 9110614400
Email: partha@labinmotion.com