• Bhagyeshwari Chauhan

The Indian Washing Machine Market: Product Category Analysis and Insights

Updated: May 19



The Indian washing machines market is expected to grow between 3 - 4 CAGR from 2021 to 2025. For a country with over a billion people, that represents a huge opportunity for domestic players to cater to both rural and urban markets which are hugely dominated by multinational companies like LG and Samsung. With the demand for consumer durables like washing machines is ever-increasing, this product segment has tremendous growth potential in the coming years.


At Datahut, we’re conducting an analysis of the market using the data we obtained through our cloud-based web data extraction platform.


In this blog, we use this market data to provide insights into the washing machine industry covering the following aspects:


  1. Data collection and preparation

  2. Which brands have more inventory in the washing machine market

  3. Pricing insights from the washing machines market

  4. Promotional insights from the washing machines market

  5. The most desired products in the washing machines category

  6. Insights based on the load capacity of washing machines


This report is an important read for entrepreneurs who plan to develop a brand strategy and foray into the ever-growing washing machine industry.



A download link for the Indian Washing Machine Market data used for the analysis is provided at the end of the blog.


Data collection


For gathering initial information on various washing machine brands, we collected publicly available data from Flipkart using our own proprietary data scraping platform. The initial data set contained 696 records ( This includes duplicates as well).


Data Attributes


The following data fields were extracted for each washing machine.


  1. Product_name: We will call it as product name but some people refer to it as the title of the product. Eg: ( Haier 6.2 kg with Ariel Wash Feature Fully Automatic Top Load Brown, Grey )

  2. Product_url: The Product URL is the URL to the page which contains the product information. Eg ( https://www.flipkart.com/haier-6-2-kg-ariel-wash-feature-fully-automatic-top-load-brown-grey/p/itmfg2h7psh2whjn?pid=WMNFG2H7HWJCUKAR )

  3. Brand: Brand represents the brand name, for example Samsung. Under the brand Samsung, they sell 101 models. Some of them are variants of the same model but for the sake of analysis we consider them as two distinct models and they have different UPC’s.

  4. Sale_price: Sale price is the price at which the product is available on Flipkart. It is the price after the discounts.

  5. MRP: MRP is the maximum retail price of the product.

  6. Discount_percentage: Discount percentage is the discount the brand offers for that particular product.

  7. Load: Load represents the loading capacity of the washing machine. For eg how much kgs of load it can handle

  8. "Number_of_ratings: Number of ratings is the total number of star ratings given to that particular product.

  9. Number_of_reviews": This is the total number of reviews given to the product by all users.

  10. Type_of_washing: is the mode of wash available with the model. For example, Automatic, Semi-automatic, etc

  11. UPC: UPC or universal product code is the unique identification for a model.

  12. Star rating: Star rating is the average star rating of the product.


Data Validation and Deduplication


One of the major problems with scraping product information is the duplicates. After the data extraction process is complete, deduplication is a must and we use the links as a reference to deduplicate. The deduplication is a feature built-in on our web scraping platform and it spits out the data in seconds.


The final data output after filtering out the duplicates contained 602 products implying that 26% of the scraped data was in fact duplicate. If you’re collecting web data using web scraping make sure that you perform Q&A on the data. There could be both coverage issues and data issues when you are scraping the data.


The Tools for Analysis of the Scraped Data


For analyzing the data scraped above, we loaded the data into a MySQL database and connected it to Metabase. Metabase is an open-source and unbelievably user-friendly tool to work with databases. You can use their UI for simple tasks and the native SQL editor to perform complex tasks.


This helped us infer the following analysis:


1. Which washing machine brands lead the market by inventory


There are 34 companies selling washing machines on the Indian Market. Samsung leads the category with 87 unique listings which are 14.45% of the total inventory. LG, Panasonic, and Whirlpool are closely trailing behind with 12.45%, 10.79%, and 14.11 %.


Check out the visualization below.





To view the interactive Tableau visualization, visit this link



2. Category wise distribution per brand


Let us now take a look at how washing machines are distributed across different price segments. For the sake of analysis let’s put them into 3 price buckets and see how the products are distributed


  • Priced between Rs 0 and 25,000 - The economy category

  • Priced between Rs 25,000 and 50,000 - The premium category

  • Priced above 50,000+ -The super-premium category



Note that:

  • Out of a total of 602 washing machines, 453 machines are sold within the economy category

  • 133 washing machines are sold in the premium category.

  • 5 brands cater to the super-premium washing machine category selling a total of 16 washing machines.


The Indian Washing Machine Market: Product Category Analysis and Insights


To view the interactive Tableau visualization for Economy category washing machines, visit this link


The Indian Washing Machine Market: Product Category Analysis and Insights


To view the interactive visualization for Premium category washing machines, visit this link



To view the interactive tableau illustration for Super-premium category washing machines, visit this link


3. Distribution by Sales price


Pricing has a lot to do with the way brands position themselves in the market. Let’s take a look at the average sales price across different brands.


Observing the inventory by sales price, here’s the analysis of the percentage of

Washing machines falling under each price category:


  1. Percentage of washing machines priced between Rs 0-20,000: 57.80% (348 machines)

  2. Percentage of washing machines priced between Rs 20,000-40,000: 37.87% (228 machines)

  3. Percentage of washing machines priced between Rs 40,000-60,000: 2.82% (17 machines)

  4. Percentage of washing machines priced above Rs 60,000: 1.4% (9 machines)



The Indian Washing Machine Market: Product Category Analysis and Insights
Percentage of washing machines for different ranges of sales price

4. Discount Percentage offered


Discounts are a great way to entice consumers into buying a product. Let us see which brand offers more discounts.


Here is a distribution of the discount percentage offered


  • Out of 602 items, around 287 washing machines, which was the maximum number of washing machines, provide a discount within the range 15-22 %.

  • The second-highest number of washing machines (143 machines) provide a discount within the range of 7.5-15 %.

  • The highest range of discount is about 45-52.5% which is provided by only 5 washing machines.


Here’s a visualization of the discounts offered by different washing machines.


The Indian Washing Machine Market: Product Category Analysis and Insights
Percentage of washing machines per discount range

5. Type of washing


Out datasets analyzed 7 types of washing machines:

  1. Air dresser

  2. Dryer

  3. A fully automatic front load

  4. Fully automatic top load

  5. Semi-automatic top load

  6. Washer only

  7. Washer with dryer


We inferred the following:


A huge amount of inventory fell under 3 major categories: fully automatic front load, fully automatic top load and semi-automatic top load. Out of 602 machines, the maximum inventory was in fully automatic top load machines, occupying 43.5% of the total inventory.


-> Number of fully automatic top load machines: 262 (43.5% of total inventory)

-> Number of semi-automatic top load machines: 171 (28.4% of total inventory)

-> Number of Fully automatic front load machines: 144 (23.9% of total inventory)


The Indian Washing Machine Market: Product Category Analysis and Insights

To view the interactive Tableau visualization, visit this link.



6. Star Rating for Washing machines


Start ratings are one of the most crucial metrics used by brands to sell themselves in the market. Consumers often refer to star ratings before making a purchase decision.


We analyzed the star ratings of 602 washing machines and found the following analysis:

  • The highest star ratings (between the range of 4.5-5.25) were achieved by 87 washing machines.

  • Around 73.2% of washing machines had the second-highest star rating in the range of 3.75-4.5. These turned out to be 441 machines in number

  • Approximately 12.9% of washing machines received a star rating of less than 3.75


The Indian Washing Machine Market: Product Category Analysis and Insights
Percentage of washing machines categorised by star ratings


7. Review analysis: Number of Reviews


We analyzed 602 washing machines from 34 brands to see the distribution of review count. Here’s our analysis:

  • Approximately 83.3% of all washing machines (502 machines) received less than 1000 reviews.

  • Only 16.7% of all washing machines (100 machines) received more than 1000 reviews on their product page.


The Indian Washing Machine Market: Product Category Analysis and Insights
Percentage of washing machines categorized by number of reviews


Download the Data: Download the data we used for the analysis from this link


The above analysis is just the tip of the iceberg. At Datahut we help market research companies get all kinds of data in every industry for conducting competitive research. If you need web data from another category for analysis, Contact Datahut


Also Read:


1. The Indian Smartphone Market Analysis & Insights

2. How to Scrape Product Information from Walmart using Python beautifulsoup

3. How To Scrape Amazon Data Using Python Scrapy

4. How to Build a Web Crawler in Python from Scratch

5. Washing Machine: 5 Spectacular Audience Insights Based on Customer Experience



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