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Embark on a journey through the Amazon India marketplace, where our report sheds light on the dynamic FMCG sector. Armed with advanced analytics, we decode complex consumer data into strategic business insights. This document is more than just data; it's a narrative that shapes decision-making for brands eager to succeed in a bustling online retail environment.

This concise report, employing Principal Component Analysis and K-Means Clustering, offers a detailed perspective of the market's undercurrents. Tailored for both new entrants and established players, it's designed to guide you through the nuances of e-commerce, helping you to refine your strategies and propel your business in one of the world's most vibrant shopping hubs.

What can I find in the Amazon India FMCG Market Products - Unsupervised Learning Report?

  1. Introduction:

    • The introduction section discusses the e-commerce landscape, emphasizing the importance of customer data in Amazon's India marketplace. It mentions the use of the Helium 10 tool to acquire datasets.

  2. Methodology:

    • The report employs two main analytical methods:

      • Principal Component Analysis (PCA): This is used for dimensionality reduction to focus on the most influential data variables.

        • The report uses an analogy comparing PCA to taking pictures from different angles and selecting the most distinct ones to understand a sculpture.

        • An e-commerce example is provided to illustrate how PCA simplifies data analysis by combining related variables into principal components.

      • K-means Clustering: This technique is used for segmenting the data into distinct clusters based on similarities to uncover hidden patterns.

        • An analogy is used to explain how K-means clustering works, comparing it to grouping guests with similar tastes at a party.

        • An e-commerce example explains how clustering can help an online store offer personalized deals by grouping users with similar browsing habits and purchase behaviors.

  3. Goals and Applications:

    • The report aims to provide a roadmap for brands and sellers to understand critical variables and products in the Amazon India ecosystem. This information is intended to help businesses make informed decisions, optimize their product offerings, and align their strategies with market demand.

  4. Conclusion:

    • By the end of the report, readers are expected to have a robust understanding of the Amazon India marketplace and the analytical tools to navigate it successfully.

Amazon India Marketplace FMCG Unsupervised Learning Report - table of contents

  • Unsupervised Learning Report

  • Introduction

    • Data

    • Methodology

  • Principal Component Analysis

    • Average for index values

    • Variance for index values

    • Output of PCA

    • BiPlot

    • Graphics and Evaluation

    • Insights from the Cumulative Proportion of Variance Explained

    • Selecting the Components

    • Scenarios

  • K-Means Clustering

    • Scenario 1

      • Output of K-means

      • Plotting the Result Sales and Review Count Output of K-means

      • K−Means Clustering with K = 2

        • Plotting the Result

        • Output of K-means

      • K−Means Clustering with K = 2

        • Output of K-means

      • K−Means Clustering with K = 2

    • Scenario 2

      • Output of K-means

      • K−Means Clustering with K = 2

        • Output of K-means

      • K−Means Clustering with K = 2

      • Review Velocity, Images, and Review Count Output of K-means

      • Plotting the Result Scenario 3

      • K−Means Clustering with K = 3

        • Output of K-means

      • K−Means Clustering with K = 2

        • Revenue and BSR

        • Plotting the Result

      • K−Means Clustering with K = 2

      • K−Means Clustering with K = 3

      • Review Count and Ratings Output of K-means

      • K−Means Clustering with K = 2

  • Conclusion

  • Bibliography

Amazon India Marketplace FMCG Unsupervised Learning Report - table of figures

  • Figure 1: Biplot of Variables

  • Figure 2: Proportion of Variance Explained

  • Figure 3: Cumulative Proportion of Variance Explained

  • Figure 4: FBA Fees and Price Graphic

  • Figure 5: Sales and Review Count Graphic

  • Figure 6: Review Velocity and Active Sellers Graphic

  • Figure 7: FBA Fees and Weight Graphic

  • Figure 8: Revenue and Active Sellers Graphic

  • Figure 9: Review Velocity, Images and Review Count Graphic

  • Figure 10: Sales and Revenue Graphic

  • Figure 11: Revenue and BSR Graphic

  • Figure 12: FBA Fees, Review Velocity and Images Graphic

  • Figure 13: Review Count and Ratings Graphic

Distribution of Word Counts and Character Counts in Product Details

Adequacy Analysis Interpretations:

  1. Average Length:

    • The average word count for product details is approximately 19.5 words, and the average character count is around 115.7 characters.

  2. Distribution:

    • The histograms show a relatively wide distribution for the length of product details, both in terms of words and characters.

    • Most product details seem to have between 10 to 30 words and between 60 to 160 characters.

  3. Range:

    • The shortest product detail contains only 3 words (or 14 characters), while the longest one has 44 words (or 274 characters).

Word Cloud for Product Details

Text Analysis Interpretations:

  1. Dominant Words: Words like "Baby", "Pants", "Diapers", "Large", and "Pack" prominently stand out in the word cloud. This suggests that a significant portion of the products in the dataset might be related to baby care, particularly baby diapers.

  2. Descriptors: Words like "Care", "Premium", "Size", and "Protection" are also quite visible. These are likely descriptors used by sellers to highlight the quality or specific features of the products.

  3. Brand and Product Names: Some words might be brand names or specific product lines. Recognizing these can provide insights into the most popular or frequently sold brands or products in the dataset.

FMCG in Amazon India Marketplaces industry statistics

  1. Product Diversity: The FMCG sector on Amazon India boasts a wide range of products, from everyday essentials like soaps and detergents to premium personal care items.

  2. Top Brands: Brands like Dettol, Himalaya, and Pampers are among the top sellers, indicating their strong market presence and consumer trust.

  3. Sales & Revenue: High-demand items like soaps, face washes, and baby products often dominate the sales charts. The revenue range for these products can vary significantly, with some premium or high-demand products generating substantial monthly revenue.

  4. Price Range: FMCG products on Amazon India have a broad price range, catering to both value-conscious and premium-seeking customers.

  5. Consumer Feedback: Products with a high number of reviews, such as baby diapers and cosmetic items, are likely popular and widely purchased. The average rating across products gives brands an indication of consumer satisfaction and areas of improvement.

  6. Fulfillment: A significant majority of FMCG products on Amazon India are fulfilled by Amazon (FBA), underscoring the benefits of Amazon's logistics and storage solutions for sellers.

  7. Growth Potential: With the increasing penetration of internet and smartphone users in India, the potential for growth in the online FMCG sector is immense. The convenience of home delivery, frequent discounts, and the vast product assortment are driving more consumers to shop for their daily essentials online.

  8. Challenges: While the online FMCG market is growing, challenges like logistics in remote areas, competition from local kirana stores, and the need for quick delivery slots persist.

  9. Digital Influence: Even if the final purchase happens offline, a considerable segment of consumers uses online platforms to research products, check reviews, compare prices, and make informed decisions.

  10. Private Labels: Amazon, like other e-retailers, has been focusing on private labels in the FMCG segment to improve margins and offer competitive pricing.

Why buy this report on the Amazon India FMCG Market Products - Unsupervised Learning Report?

Buying the "Amazon India FMCG Market Products - Unsupervised Learning Report" would be beneficial for several reasons:

  1. Data-Driven Insights: The report offers data-driven insights derived from sophisticated unsupervised learning techniques like PCA and K-means clustering. This can help businesses understand the complex patterns within consumer behavior and product performance.

  2. Competitive Advantage: By understanding the underlying trends and segments within the Amazon India FMCG market, businesses can gain a competitive edge by tailoring their strategies to meet market demands more effectively.

  3. Product Optimization: The report provides an analysis of customer reviews, product specifications, and other critical variables that can inform product development, positioning, and optimization for better sales performance.

  4. Market Understanding: With detailed methodologies and interpretations, the report serves as an educational tool for brands and sellers to deepen their understanding of the e-commerce landscape, particularly in the FMCG sector.

  5. Strategic Decision Making: The insights from the report can guide brands in making informed strategic decisions regarding inventory management, marketing, pricing, and customer engagement.

  6. Targeted Marketing: The clustering analysis can help in identifying distinct customer segments, enabling businesses to design targeted marketing campaigns and personalized offers.

  7. Resource Efficiency: By highlighting the most influential variables in the Amazon India marketplace, the report allows businesses to focus their resources on areas with the highest return on investment.

  8. Visual Analysis: The inclusion of figures like biplots and graphics aids in the visual interpretation of complex data, making the insights more accessible and understandable.

  9. Future Trends Prediction: Understanding current market dynamics can help predict future trends, allowing businesses to stay ahead of the curve in the rapidly evolving e-commerce space.

  10. Robust Methodology: The report details the robust methodology used in the analysis, which adds credibility to the insights and recommendations provided.

Purchasing this report can be a strategic investment for sellers, marketers, product managers, and business strategists looking to enhance their presence and performance in the Amazon India marketplace.

Top Sellers in Buy Box

The "Buy Box" column has various sellers with varying counts. The top sellers with the most buy boxes are:

  • RK World Infocom Pvt Ltd: 239 entries

  • Cocoblu Retail: 64 entries

  • Amazon Retail: 44 entries


There are also 31 entries with no seller information (NaN).


Given the long tail of sellers with a few entries, it would be best to visualize the top sellers (e.g., top 10) and group the others into an "Other" category to avoid cluttering the bar plot.


  1. Dominance of a Few Sellers: The seller "RK World Infocom Pvt Ltd" has a significant dominance in the Buy Box, with the highest number of products. This suggests that they might be a major seller or distributor for these FMCG products on Amazon India.

  2. Presence of Amazon Retail: "Amazon Retail" itself also holds the Buy Box for a considerable number of products, which indicates Amazon's direct involvement in selling these FMCG products.

  3. Diverse Seller Base: Beyond the top few sellers, there is a long tail of sellers with a smaller number of products in the Buy Box. These are grouped under "Others" in our visualization.

  4. Missing Data: There are 31 products for which the Buy Box seller information is missing. This could be due to various reasons, like the product being currently unavailable, or it might be a data collection issue.

the Amazon India FMCG Market Products - Unsupervised Learning Report

The methodology of the Amazon India FMCG Market Products - Unsupervised Learning Report is based on sophisticated data analysis techniques that aim to uncover patterns and relationships within the FMCG market data on Amazon India.


Here's a detailed look at the methodology based on the information extracted from the report:

  1. Data Acquisition:

    • The dataset was acquired using the Helium 10 tool, which presumably provides detailed data on customer reviews, product specifications, and other pertinent details relevant to the FMCG products on Amazon India.

  2. Principal Component Analysis (PCA):

    • Purpose: PCA is used as a dimensionality reduction technique to simplify the dataset by transforming the original variables into a new set of variables (principal components) that are uncorrelated and that retain most of the variability present in the original dataset.

    • Process:

      • Identification of the most influential variables by reducing the number of dimensions without losing significant information.

      • Representation of data in a way that highlights similarities and differences, thereby simplifying the dataset.

    • Visualization: Tools like biplots are used to visualize the results of PCA, helping to understand the data structure and identify patterns.

  3. K-Means Clustering:

    • Purpose: K-means clustering is used to segment the dataset into distinct clusters based on similarities among the data points. This segmentation helps to discover inherent groupings within the data.

    • Process:

      • Determination of the optimal number of clusters (K) by analyzing various scenarios.

      • Iterative refinement of cluster centroids until the most cohesive groupings are found.

      • Analysis of different variables, such as sales, review count, review velocity, and others, to form meaningful clusters.

    • Visualization: The results of the K-means clustering are plotted to visually represent the segmentation of the market into different clusters.

  4. Insight Generation:

    • Based on the results of PCA and K-means clustering, insights are drawn regarding the market structure, customer preferences, and product performance.

    • The report discusses how these insights can be used to tailor business strategies and optimize product offerings.

  5. Application to Business Strategy:

    • The final step involves translating the analytical insights into actionable business strategies. This includes recommendations for product development, marketing, inventory management, and customer engagement based on the identified market segments and consumer behavior patterns.

The report's methodology section likely provides a more detailed explanation of each step, including the criteria used for data selection, the statistical tools and software employed in the analysis, and the specific interpretation of the results. This methodology is crucial for any business looking to apply data-driven decision-making to improve their competitive position in the Amazon India FMCG marketplace.

What will I receive extra if I buy this report?

You will be granted access to the comprehensive and original dataset that served as the foundation for our in-depth analysis. This dataset offers a detailed view of the metrics and insights we derived, ensuring a transparent and thorough understanding of our research process and conclusions.


We are providing this dataset as a gift, given sincerely and wholeheartedly, from the depths of our hearts, to express our gratitude and commitment to transparency.

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