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TCCC New Beverage Product Analysis

Intro

FMCG (Fast-Moving Consumer Goods) companies frequently launch new products—it's normal for them to release new items every month. For large corporations, every action must serve a clear purpose. A product that costs millions of dollars to develop and launch must fulfill at least one strategic goal:

(1) Stay Competitive: The market is highly saturated. Even top companies must constantly evolve just to maintain their position.

(2) Meet Changing Consumer Needs: Trends, preferences, and lifestyles shift rapidly. Many FMCG products are designed to be temporary, catering to seasonal or short-term demands.

(3) Drive Attention and Revenue: Whether short-lived or permanent, new products can bring significant profits or create jobs during clearance efforts. Many experts believe it's impossible to predict which products will succeed, so companies often follow a high-launch-volume strategy—despite the risk of wasting millions.

From my perspective, product innovation is essential for FMCG companies to stay competitive and resilient in this fast-changing market. The real question is: which product should be launched?

Tools Used

  • Python (pandas, matplotlib)
  • Jupyter Notebook

Steps

  1. Data Cleaning – Handle missing values and inconsistencies.
  2. Exploratory Data Analysis (EDA) – Understand the shape, trends, and relationships in the data.
  3. Insight Visualization – Plot patterns across demographics, regions, and innovation types.

Results

  • Identified key demographic segments.
  • Highlighted trends in purchasing behavior.

Dataset

This dataset simulates 1,000 FMCG beverage products to explore:

  • Innovation Type: New Flavor, Upsize, Downsize, etc.
  • Segment, Category, Region, Sales Channel
  • Sales data: Volume, Price per Liter, Sales Value
  • Performance: Trial Rate, Repeat Rate, Market Share
  • Launch context: Distribution, Out-of-Stock Rate, Time

Notes:

  • There are some missing values in the dataset, which will be addressed during the analysis phase.

Key Results

Uncovered high-potential product types based on performance KPIs.

Isolated demographic and channel trends to inform launch strategy.

Identified innovation types most associated with repeat purchases and market share growth.

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