How many of your customers are within the same number of miles of each store? You may be comparing the customers around several branches, using the same distance for each. Both the addresses in your customer list and the store locations affect who belongs in that comparison.
Customers near neighboring branches may be close to more than one store. You may want to know how many customers are near each location and which customers are close to several stores. This comparison uses your recorded customers, so you need to know who each record represents and which address it uses.
Prepare customer and store rows
Give each record an ID, a location, and a Record Type value. Use consistent values such as Customers and Stores so you can select the store origins and inspect the customer records separately. Keep a store name beside each store ID so the exported results are easy to recognize.
For a count of distinct customers, prepare one row per customer ID. If your source is a purchase export, combine the transactions by customer before importing the counting list. Otherwise, one person who made several purchases will contribute several rows.
Choose the location that describes the customers you want to count. A residence-based list answers a different question from one using business service addresses. If one customer has several locations, decide whether this analysis counts customers or customer locations. Preserve that decision in the name of the measure.
Add a numerical CustomerCount column to the source. Enter 1 on every intended customer row and 0 on every store row. This is a field you prepare, and its reliability depends on having the right records in the list. It won't remove duplicate customers by itself.
Check the sum of CustomerCount against your intended customer total before importing. Inspect missing addresses and make sure store locations identify the correct branches. A count around a misplaced store can look reasonable while describing the wrong area.
Import the prepared file into Maptive and verify a few customer and store positions. Keep both record types in the map for the radius build. You will use customer-only metrics to handle the count rather than simply hiding every store.
Add the same radius around every store
Choose the mileage threshold before drawing the circles. Use the same threshold for every store if your purpose is a like-for-like comparison. A 5-mile count at one location and a 10-mile count at another answer different questions.
Open Map Tools and choose Distance Radius Circles. Enter the store address in Starting Location. You can also start from a store pin by clicking the marker, opening its tools icon, and choosing Add Radius.
Enter your distance under Proximity Within and choose miles or kilometers. For a 5-mile analysis, enter 5 and select miles. Click Add Proximity Radius to create the circle around that store.
This circle selects by straight-line proximity. It doesn't establish the road miles a customer would drive or the time needed to reach the store. A river, highway layout, or limited crossing can matter to access even when a point is inside the circle. Keep that distinction attached to the mileage measure.
To create circles around the store group, change Apply To from Individual Location to Group. In Select Group Column, choose Record Type. In Select Group, choose the Stores value. Set the shared distance and unit, then click Add Proximity Radius.
Check that the circles are centered on the intended store records and use the same distance. If you need clearer names, open a circle's three-dot menu, choose Edit Proximity, set Proximity Name, and click Save. Use names that will also make sense in the exported results.
Count customer rows inside each circle
Add a customer-only total
Click inside a circle and choose Customize Metrics. Select Spreadsheet data, choose CustomerCount, and select Sum (total). Click Add to Proximity Details to add that measure to the circle's details.
Each intended customer row contributes 1, and each store row contributes 0. The resulting sum answers how many of those customer rows fall inside the area. It remains a distinct-customer count only if you prepared one row per customer.
Open several circles and compare the metric with the records around each store. Pay attention to a location near the circle's edge and a store close to another branch. Those are useful places to check whether the point placement and counting rule match your purpose.
Changes to the metrics in one radius update the radius tool's other instances. Use this shared behavior to keep the comparison consistent, then check that each store shows the intended customer measure.
Inspect record types and other measures
You can also use Group Count on Record Type to see the category counts for Customers and Stores. This gives you a way to inspect the mix of records inside a circle without treating every marker as a customer.
Add a sales measure only if it helps the store comparison. Use Sum for total sales in the area. Average, High Value, and Low Value answer different questions about a numerical column. Before adding a measure, check that its source values use the same period and row definition across the customer list.
Be careful with a filter that leaves only Customers. Data filtering can also filter the group-generated circles. If those circles came from the Stores group, hiding the store records can affect the circles you're trying to analyze. Confirm that the intended origins remain present after any filter change.
Export the records behind the counts
Check one store's customer list
Open a circle's three-dot menu and select Export Locations. Choose a suitable format from XLSX, TSV, CSV, or Clipboard. Retain the customer ID, record type, location, and fields needed to understand the result, then complete the export with Export Now.
Compare the exported customer IDs with the circle's customer metric. Keep store rows out of this exported customer count using Record Type or CustomerCount. A raw count of all exported rows may include a store or another record type.
Check repeated IDs and inspect edge locations in the list. If the count differs from what you expected, first check whether you are comparing customers, locations, or transactions. Then check whether the export includes all qualifying markers or a limited nearest-marker selection. A nearest-record shortlist won't represent the full circle count.
Create a unique network output
Use Export File in the radius tool to export across the circles. For a unique output, choose the option that returns each row only once for its closest circle. This handles repeated circle membership for a row; your source preparation still determines whether several rows represent the same customer.
Choose whether to return all qualifying markers or a limited number of nearest markers. Use the full qualifying set for a network coverage total. Keep a nearest-marker limit for a shortlist where that limit is part of the question.
You can select All Markers outside the Results to inspect records beyond the circles. Review Record Type in that output too. If you're looking for uncovered customers, count the customer rows rather than every record returned.
Choose straight-line or driving-time and driving-distance columns according to the comparison you need, then click Export Now. Road measurements in the output add travel information about the records. They don't change the straight-line rule that selected the circle's contents.
Read overlapping reach correctly
Keep each store's reach count separate from the network total. An individual store can count every customer inside its circle, including a customer who also falls inside another store's circle. Adding those store counts together will count that shared customer again.
The closest-circle output supplies an assignment rule for the exported rows. It doesn't prove where a customer shops, which branch they prefer, or which store recorded their purchases. Use actual purchase-store data if that is the question you need to answer.
Prepare your per-store comparison table from the customer-only metrics. Include the store ID, radius in miles, customer count, and any sales measure you chose. Label the source period and customer location type so someone reading the totals knows what they describe.
Keep the network count in a separately named result, traced to distinct customer IDs. Compare covered IDs and uncovered IDs with the source list, and resolve any repeats or missing records before using that total in the store review.
