# Segmented models for personal income estimation at Mercado Libre

DevFeed: [Segmented models for personal income estimation at Mercado Libre](<https://devfeed.tech/articles/the-silver-shotgun-a-bunch-of-silver-bullets-22558.md>)

Original publisher: [Read original article](<https://medium.com/mercadolibre-tech/the-silver-shotgun-a-bunch-of-silver-bullets-4a51c57a0fd9?source=rss----5011f85401f0---4>)

Author: Samuel Vazquez

Published: 2026-01-08T20:44:23Z

Content type: article

Language: en

Sources: [Mercado Libre Tech](<https://devfeed.tech/sources/mercado-libre-tech.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [personalization](<https://devfeed.tech/topics/personalization.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [big-data](<https://devfeed.tech/topics/big-data.md>)

Tags: [big-data](<https://devfeed.tech/tags/big-data.md>), [business](<https://devfeed.tech/tags/business.md>), [classification](<https://devfeed.tech/tags/classification.md>), [data](<https://devfeed.tech/tags/data.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [estimations](<https://devfeed.tech/tags/estimations.md>), [personal-income](<https://devfeed.tech/tags/personal-income.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [regression](<https://devfeed.tech/tags/regression.md>)

## AI overview

Mercado Libre describes a "silver shotgun" approach for heterogeneous real-world data: segmenting data across contexts, training specialized models for each segment, and combining their predictions. The article presents personal income estimation as an application supporting more personalized financial products and discusses how geographic scale changes the importance of features such as home GPS location.

## Source excerpt

Before we dive in, let's give a huge shout-out to Julio Avila Torreblanca and Alejandro Hernandez Cuevas for their exceptional work in developing this system. Thanks also to Enrique Miranda for reviewing the text and providing valuable feedback. Introduction Real-world data is rarely homogeneous. The same feature can mean different things depending on the city, neighborhood, urban density, or user segment. When we force a single global model to explain all those regimes, we usually pay with lower performance. At Mercado Libre, we take a different approach: instead of looking for a "silver bullet," we use a "silver shotgun." This pattern segments the data in several ways, trains specialized models per segment, and combines their predictions into a single estimate. Motivation: Financial inclusion is a significant opportunity In Mexico, only 63% of adults have a formal bank account, only 37% have a credit product, and less than 16% have a bank credit card [1]. About 45% of employed people have a formal source of income, while the rest work in the informal sector [2]. This gap presents a great opportunity for Mercado Libre to enter a growing market. Hyper-personalization is key to our mission to democratize financial inclusion across Latin America (LatAM). To offer great credit products, such as credit cards, we need a reliable estimate of a person's income. Big data, big problems Imagine we want to build a personal income estimation model for people visiting a very small local park. Every day, our dataset would grow by a few hundred entries for children playing, people exercising, and dog walkers, among other activities. Because people in the park live just a few blocks away, home location would have a limited effect (same neighborhood means low GPS variance). The features with the biggest impact on estimated income would likely be age and occupation. If we scale the problem to an entire city, home GPS location will have a bigger effect on the estimate. Each neighborho