Summary of DS C20 | BA Track | [Capstone] Pre assignment- E-Commerce & Marketing

Summary of the Video:

DS C20 | BA Track | [Capstone] Pre-assignment - E-Commerce & Marketing

Main Ideas and Concepts:

  1. Introduction and Participant Backgrounds:
    • The session begins with introductions by the instructor and participants.
    • Instructor has a background in machine learning, AI, and extensive industry experience.
    • Participants have varied experience in analytics, product management, and data science, aiming to deepen their understanding of data science applications in marketing.
  2. Overview of the Capstone Project and Business Context:
    • The project focuses on Market Mix Modeling (MMM), a technique to analyze the impact of marketing efforts on sales.
    • Key objective: Understand and quantify how different marketing levers (advertisement types, pricing, promotions) affect revenue.
    • The project involves performance driver analysis using Key Performance Indicators (KPIs) to optimize marketing spend and maximize ROI.
  3. Marketing Fundamentals Covered:
    • The 4 Ps of Marketing: Product, Price, Place, Promotion.
      • Product: Features developed based on market/demographic analysis.
      • Price: Determining optimal pricing.
      • Place: Distribution channels (online/offline, store locations).
      • Promotion: Advertising and communication strategies.
    • The 4 Cs of Marketing: Customer, Cost, Convenience, Communication.
      • Emphasizes customer-centric marketing, cost-effectiveness, convenience of access, and targeted communication channels.
  4. Project Scope and Data Description:
    • Focus on three product categories: Camera Accessories, Gaming Accessories, Home Audio.
    • Data timeframe: July 2015 to June 2016.
    • Data files provided:
      • Consumer order details (daily).
      • Media investment details across various channels (print, digital, radio, etc.).
      • Sales calendar with promotional offers.
      • Net Promoter Score (NPS) data.
      • Weather data (to consider external factors influencing sales).
      • Stock value data.
    • Goal: Build models to recommend optimal marketing budget allocations.
  5. Methodology and Workflow (Following CRISP-DM Framework):
    • Understand Business and Data: Familiarize with objectives and datasets.
    • Data Preprocessing & Feature Engineering:
      • Handle missing values (replace nulls with zero).
      • Treat incorrect values (e.g., gross minimum value errors, negative values).
      • Remove duplicates (after standardizing case).
      • Outlier detection and removal.
      • Convert categorical variables to numerical (one-hot encoding, dummy variables).
      • Generate additional features (e.g., week number from order date).
      • Scale/normalize data as needed.
    • Exploratory Data Analysis (EDA) and Visualization: Identify trends, lag effects, and relationships.
    • Model Building:
      • Build regression models (linear, additive, multiplicative, lag models).
      • Evaluate models using train-test splits, select best performing models per category.
      • Understand and incorporate Adstock effect (lagged impact of advertising on sales).
    • Model Evaluation and Selection: Choose models with best predictive accuracy (e.g., R-squared).
    • Recommendation: Suggest marketing budget allocation based on model insights.
  6. Model Types Explained:
    • Additive Model: Sales as a linear sum of marketing variables.
    • Multiplicative Model: Sales modeled as product of variables; often transformed via log for linear regression.
    • Distributed Lag Model (RDT Model): Incorporates lagged effects of advertising (adstock).
    • Different categories may require different models depending on best fit.
  7. Challenges and Clarifications:
    • Participants raised concerns about the lack of hands-on teaching on multiplicative and lag models in Python.
    • Instructor acknowledges the gap and offers to provide guidance and code snippets on request.
    • Emphasis on self-learning supplemented by instructor support.
    • Discussion on how to merge and use multiple data files effectively.
    • Confirmation that regression remains the core modeling technique.
  8. Industry Relevance:
    • Market Mix Modeling is a common real-world problem handled by analytics professionals.
    • Channel attribution (understanding the contribution of different advertising channels) is a related but more complex topic beyond the course scope.
    • Machine learning and deep learning models are used depending on data volume and problem complexity.
    • Deep learning preferred for large datasets or complex tasks like computer vision.
  9. Communication and Collaboration:
    • Instructor encourages participants to communicate doubts via email.
    • Plans to create a group for better peer interaction and support.
    • Emphasis on collaborative problem-solving and iterative learning.

Detailed Bullet Points of Methodology and Instructions:

Notable Quotes

— 54:50 — « For the camera accessories we use multiplicative model, we use the rdt model with the lag. Now multiplicative model gives me better accuracy, so for the camera I chose the multiplication model as my better model. »
— 56:13 — « If you don't use the lag model that means you are ignoring the advertisement effect of the part. So this is not exactly you are ignoring but most of the portions you are ignoring. »
— 72:42 — « Don't be afraid seeing the river. »
— 74:42 — « Depending on the volume of the data, if the data is huge, we go for deep learning models because traditional machine learning models will give you the result but it will take longer time. »

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Educational

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