Mercado Libre Argentina Seller Intelligence for a Market-Intelligence.jpg
                     Mercado Libre Argentina Seller Intelligence for a Market-
Intelligence SaaS: A Mercado Libre Seller Data Scraping
At a glance
Item Detail
Client Early-stage e-commerce market-intelligence 
platform
Market Argentina first, then Mexico and Brazil
Platform Mercado Libre
Scope Listings, prices, sellers, seller reputation, sales 
indicators and category rankings
Frequency Daily for top categories, weekly for the long tail
Delivery Parquet files and API
Mercado Libre seller data scraping by Product 
Service Data Scrape
The client
The client was building a SaaS platform for Mercado Libre sellers, 
"focused initially on Mercado Libre Argentina", which needed "product 
and market data for broad discovery." A second company building a SaaS 
for "mid to large Mercado Libre sellers" made a similar request in the 
same month. Both wanted to help sellers answer three questions: what 
sells, who sells it, and at what price.
 The challenge
Breadth. Mercado Libre Argentina lists millions of products across 
thousands of categories. The client needed broad discovery, not a short 
SKU list.
Seller-level insight. Sellers wanted to see competitors' catalogues, 
prices and reputation, not just product prices.
Sales indicators. Mercado Libre shows sold-quantity indicators on many 
listings, but these are cumulative and often rounded.
Inflation and currency. Argentina's high inflation means prices change 
quickly, so comparisons over time need care.
 The solution
Step 1: Category map
Product Data Scrape mapped Mercado Libre Argentina's category tree and 
agreed with the client on 400 priority categories for daily collection, with 
the rest collected weekly.
Step 2: Listing collection
For each category, collectors captured listing ID, title, price, original 
price, discount, condition (new or used), free-shipping flag, fulfilment 
type, installment offers, sold-quantity indicator, rating and review count.
Step 3: Seller profiles
For each seller found, public reputation data was collected: reputation 
level, sales-level badge where shown, number of active listings, and 
location (province). Only public business-level seller information was 
collected.
Step 4: Sales estimation
Daily changes in the sold-quantity indicator were used to estimate units 
sold per listing per period. Where indicators were rounded, a range was 
calculated. Estimates were labelled clearly as estimates.
Step 5: Price normalisation
Prices were stored in Argentine pesos with a dated exchange rate to US 
dollars, and an inflation-adjusted series was calculated using the monthly 
consumer price index, so trends were not distorted by inflation.
Top 10 Sellers' Share of Median Price Change (30 Days, 
Category (Illustrative) Active Sellers Estimated Units Inflation-Adjusted)
Smartphones 2,140 46% -2.1%
Running Shoes 1,380 31% +0.8%
Kitchen Appliances 3,050 27% -0.5%
Kids' Footwear 960 38% +1.4%
Results
Metric Value
Listings Collected per Day 1.8 million
Sellers Profiled 210,000
Categories with Daily Seller Rankings 400
Time to Launch First Dashboard 7 weeks
The client launched three modules: a category explorer showing estimated 
demand and competition, a seller tracker that lets users follow competitors' 
catalogues and prices, and a price-position tool showing where a seller's 
price sits in the category. After Argentina, the same pipeline was extended 
to Mexico and Brazil.
What sellers see in the platform
Module Built From Example Question Answered
Category Explorer Listings, estimated units, seller Is this category growing, and counts how crowded is it?
Seller Tracker Seller catalogues, prices, What did my top competitor reputation change this week?
Price distribution per product Am I priced in the top, middle 
Price Position type or bottom of the market?
Opportunity Finder Demand estimates vs seller Which products have high count demand and few strong sellers?
Fulfilment View Full vs non-full listings Do fulfilment-backed listings win more sales here?
Lessons learned
Seller data is the differentiator. Users valued competitor seller 
tracking 
more than product prices alone.
Estimates need ranges. Rounded sold indicators mean point estimates 
can mislead; ranges are more honest.
Inflation adjustment is essential in Argentina. Without it, every price 
looks like it is rising.
Start with priority categories. Daily collection on 400 categories and 
weekly on the rest kept costs manageable.
How this differs from market-entry research
Market-entry studies, such as assessing whether a footwear brand 
should launch on Mercado Libre, usually need a one-time snapshot of 
prices and competitors. A SaaS product needs continuous daily data, 
history and seller-level detail. Product Data Scrape supports both, but 
the pipeline design is different.
Frequently asked questions
Does Mercado Libre seller data scraping include seller contact 
details?
No. Only public business information such as seller name, reputation and 
listing data is collected.
Can it cover other Mercado Libre countries?
Yes. Mexico, Brazil, Chile, Colombia and other Mercado Libre sites can be 
added.
Are units sold exact?
No. Sold indicators are converted into estimated ranges and labelled as 
estimates.
Build on Mercado Libre data
Product Data Scrape delivers Mercado Libre seller data scraping, 
category rankings and price feeds for SaaS builders, sellers and brands 
across Latin America. Request a free 1,000-record sample for your 
categories.