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Resume Work and Project Details

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 Conceptual / MMM Basics Q1. What is Media Mix Modeling (MMM)? MMM quantifies how different marketing channels and external factors drive business outcomes such as sales or conversions, using regression-based or Bayesian models on historical time-series data. Unlike attribution models, MMM doesn’t require user-level data and works even with aggregated spend data. Q2. What problem does MMM solve? It helps marketers measure ROI by channel and optimize future spend allocation while accounting for carryover, seasonality, and external effects. Q3. What is the dependent variable and what are the independent variables in your models? Dependent variable: Sales or revenue. Independent variables: Marketing touchpoints (TV, Digital, Print, Radio) and control variables (Price, Competitor, Macro factors, Seasonality). Q4. How is MMM different from digital attribution models? Attribution models rely on user-level tracking (cookies, clicks). MMM uses aggregate, privacy-compliant data and...
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Time Series and MMM basics

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🕒 1. What is Time Series Data? Definition: Time series data is a sequence of observations recorded at specific time intervals (daily, weekly, monthly, etc.). Each data point has two key components: Time (t): a timestamp (e.g., date, hour, quarter) Value (y): a measurement (e.g., sales, ad spend, temperature) In Media Mix Modeling: time series data might include weekly sales, ad impressions, or marketing spend across channels. Example: Week TV Spend Online Spend Sales 1 1000 500 15000 2 1200 600 16200 3 900 550 14800 4 1100 700 17000 Here, Sales is the time series variable we want to analyze or forecast. 📊 2. What is Time Series Analysis? Definition: Time series analysis is the study of data points collected over time to identify patterns , trends , seasonality , and dependencies —and to model or forecast future behavior. In MMM, this helps us understand how marketing efforts (TV, digital, print) influence sales over time and fore...
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LINEAR REGRESSION

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Machine Learning Machine Learning (ML) is a branch of artificial intelligence that focuses on building systems that learn from and make decisions based on data. There are several types of ML, but let’s focus on the two main categories:  Supervised  and  Unsupervised Learning . Supervised Learning Definition : In supervised learning, the algorithm is trained on labeled data. This means the input data is paired with the correct output, and the goal is to learn a mapping from inputs to outputs. Examples : Classification : Predicting whether an email is spam or not spam. Regress...
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