How to Use Store Locator Data to Identify Where Customers Want Your Products

Quick Answer

Store locator behavior can point to product level demand when it is read alongside order data, not just location searches on their own. Comparing which regions search for stores against which regions actually order specific products can reveal where a particular item is wanted but not yet available locally. SC Store Locator Map supports search tags and custom fields that let a brand note which locations carry which products, which is what makes this comparison possible. This guide covers how to connect the two data sets.

Introduction

A store locator is usually built around where a brand has locations, not which products those locations carry. But many brands, particularly wholesale and multi-location retailers, do not stock the exact same range everywhere. That difference matters, because a shopper searching for a nearby store might really be asking about one specific product, not the brand in general.

This guide is for merchants who sell a range of products through different retail locations and want to understand which products customers are searching for locally. It focuses on connecting store locator information with order and product data already available inside Shopify, rather than relying on a locator feature built specifically for product level reporting.

What Is Product Level Demand From Location Data?

Product level demand from location data means using search tags, custom fields, and order history together to see whether interest in a specific product is concentrated in a region where it is not currently available in person. A location might carry only part of a brand's range, so a customer search for a nearby store does not automatically mean the specific product they want is stocked there. Reading these two layers together gives a more accurate picture than location data alone.

Who Needs Product Level Demand Reading?

This approach is most relevant for businesses such as:

• Brands whose product range varies by store, dealer, or stockist
• Wholesale brands whose retail partners carry different portions of the catalog
• Multi-location retailers introducing a new product to only some locations
• Brands with regional product variations tied to climate, regulation, or local preference
• Consumer goods brands managing a growing number of SKUs across a distributed retail network

Why Connecting Product and Location Data Matters for Your Business

Reading product demand this way has a few practical benefits:

• It shows where a specific product might be wanted locally, not just the brand in general
• It helps prioritize which products to add to which locations first
• It reduces the chance of assuming a region wants the brand broadly when it actually wants one item
• It gives wholesale teams a more specific starting point when discussing range with retail partners
• It supports better product allocation decisions for locations with limited shelf space
• It uses data a Shopify store already generates, rather than requiring new tracking

How to Connect Store Locator Data With Product Demand

Step 1: Tag Locations by What They Actually Carry

Install SC Store Locator Map and use search tags or custom fields to note which product categories each location carries, if the range varies. This step is what makes product level reading possible later.

Step 2: Pull Product Level Order Data From Shopify

Review which products are ordered most from customers in a given region, using Shopify's own reporting. This gives a baseline of what shoppers in that area actually want, independent of what any nearby location currently stocks.

Step 3: Compare Product Demand Against Local Coverage

Cross-reference regions with strong demand for a specific product against the location tags created earlier. A region with consistent orders for a product that no nearby location carries is a clear candidate for further investigation.

• Focus first on products with the clearest regional order patterns
• Check whether the gap is about the product or about location coverage generally
• Note whether nearby locations carry a similar but not identical product

Step 4: Watch for Product Specific Customer Questions

Support messages that ask where to buy a specific product in person, rather than a general nearby store question, are a strong and direct signal worth logging separately from general location requests.

Step 5: Bring Findings to Retail and Wholesale Planning

Share the combined picture, region, product, and current coverage, with whoever manages product allocation or retail partner range decisions, since this is the level of detail those conversations need.

Step 6: Recheck the Pattern Over Time

Product demand can shift with seasons, trends, or new launches, so revisit this comparison periodically rather than treating one review as a permanent conclusion.

Examples and Use Cases

Industry: Outdoor apparel brand
Problem: A cold weather product line sold steadily online in a region where no nearby retailer stocked it in store.
Setup: The team tagged locations by product category and compared regional order data against store coverage.
Result: The gap became a clear talking point for the wholesale team when approaching new retail partners in that region.

Industry: Home fragrance brand
Problem: Customers in one area repeatedly asked where to smell a specific scent in person before buying.
Setup: The brand logged these product specific requests separately from general store questions and reviewed them quarterly.
Result: The pattern helped prioritize which product to send to a nearby stockist first.

See more examples from Shop Circle of brands connecting product and location data.

Best Practices

A few habits keep this kind of analysis practical:

• Tag locations by product category so gaps are easy to spot later
• Separate product specific questions from general location questions in your logs
• Confirm a pattern with order data before assuming a product gap is real
• Prioritize products with the clearest, most repeated regional demand first
• Share findings directly with product allocation and wholesale teams
• Revisit product demand patterns each season rather than only once

Summary

Connecting store locator data with product level order history gives a brand a clearer picture of which products customers want in which regions, not just where they want a store in general. Tagging locations by what they carry and comparing that against order data turns a simple locator into a useful input for product and range decisions.

the app supports the search tags and custom fields needed to track what each location carries, which is the starting point for reading product demand this way.