AI analyzing city data for retail locations

Location, Location, Location: How AI and Google Maps Can Find Your Next Retail Goldmine

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Rhea Morgan in Tech & Innovation August 2025 • 5 min read.

"Unlock the secrets to smarter retail expansion using artificial intelligence and readily available location data."


Finding the perfect spot for a new retail store is a high-stakes game. Get it right, and you're looking at booming profits. But a wrong choice? That can mean wasted investment and missed opportunities. Traditionally, retailers have relied on gut feelings, limited surveys, and comparisons to existing stores. However, these methods often fall short, failing to accurately gauge true potential or account for the unique characteristics of each location.

Imagine if you could take the guesswork out of the equation. What if you had a tool that could analyze countless location factors and predict the financial performance of a store before you even sign the lease? That's the promise of combining Artificial Intelligence (AI) with the power of Google Places API, a software library that provides a wealth of information on nearby establishments and environmental features.

This approach is not about replacing human intuition entirely. Instead, it's about layering data-driven insights on top of expert knowledge to make smarter, more informed decisions. This article explores how this innovative combination can transform retail location strategy, offering a powerful advantage in today's competitive market.

The AI Edge: Predicting Success with Data

AI analyzing city data for retail locations

The core idea is simple: existing retail stores don't exist in a vacuum. Their performance is influenced by their surroundings – the number of nearby restaurants, the proximity to public transportation, even the density of local businesses. By analyzing the relationship between these environmental factors and the financial success of existing stores, an AI can learn to predict the potential of new locations.

Feed-forward neural networks, a type of AI, are particularly well-suited to this task. They can handle a mix of different types of data (integer, real, and binary) and model non-linear relationships. This means they can uncover complex patterns that traditional statistical methods might miss. The AI learns from the data of existing stores, and then applies that learning to new locations.

Here's how it works:
  • Data Collection: Google Places API is used to gather data on a wide range of environmental factors around existing stores. This includes the number of nearby restaurants, bus stops, schools, ATMs, and other relevant points of interest.
  • Financial Ranking: Existing stores are ranked based on their financial performance using methods like k-Medoids clustering, which groups stores with similar sales patterns.
  • AI Training: A feed-forward neural network is trained on the collected data, learning the relationship between environmental factors and financial rankings.
  • Prediction: The trained AI is then used to predict the financial ranking of potential new store locations based on their environmental data.
This approach offers several advantages over traditional methods. It reduces reliance on potentially unreliable survey data or comparisons to dissimilar stores. Also, it provides a cost-effective way to estimate the potential demand for a non-existing store.

From Data to Decisions: Putting AI to Work

The true power of this method lies in its ability to provide actionable insights. By combining AI predictions with expert opinions, retailers can make more informed decisions about where to open new stores.

Imagine a retail chain considering three potential locations. Using the AI-powered approach, they can forecast the financial ranking of each location and identify the one with the highest potential. This data-driven insight, combined with the expertise of local market analysts, can significantly increase the chances of success.

While this approach offers a powerful advantage, it's important to remember that it's not a crystal ball. The accuracy of the predictions depends on the quality and completeness of the data used to train the AI. However, by continuously refining the data and incorporating new insights, retailers can create a powerful, data-driven location strategy that drives growth and profitability.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.5539/ijsp.v3n1p67, Alternate LINK

Title: Selecting Location Of Retail Stores Using Artificial Neural Networks And Google Places Api

Subject: General Medicine

Journal: International Journal of Statistics and Probability

Publisher: Canadian Center of Science and Education

Authors: Mehmet Hakan Satman, Mustafa Altunbey

Published: 2014-01-21

Everything You Need To Know

1

How does AI help in determining the ideal location for a retail store?

AI, specifically feed-forward neural networks, analyzes various environmental factors gathered using Google Places API around existing retail stores, such as the number of nearby restaurants, bus stops, and schools. This data is then used to predict the financial performance of potential new locations by identifying patterns between these environmental factors and the success of current stores. The financial performance is ranked using k-Medoids clustering to group stores with similar sales patterns.

2

What kind of data does Google Places API provide that's useful for retail location strategy?

Google Places API provides data on the surroundings of a location, including the number of nearby restaurants, bus stops, ATMs, schools, and other points of interest. This data is crucial for AI models to learn the relationship between environmental factors and financial performance.

3

What role does k-Medoids clustering play in predicting retail success?

K-Medoids clustering is used to group existing retail stores based on similar sales patterns, creating financial rankings. This method helps the AI to understand which environmental factors contribute to different levels of financial success.

4

Why are feed-forward neural networks used in this approach?

Feed-forward neural networks are particularly well-suited for this task because they can handle a mix of different types of data (integer, real, and binary) and model non-linear relationships. This means they can uncover complex patterns that traditional statistical methods might miss. The AI learns from the data of existing stores, and then applies that learning to new locations.

5

What are the advantages of using AI and Google Maps together for retail location decisions?

The combination of AI and Google Places API helps retailers make more informed decisions by providing data-driven predictions about the potential financial performance of new store locations. This reduces reliance on potentially unreliable survey data or comparisons to dissimilar stores and offers a cost-effective way to estimate the potential demand for a non-existing store.

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