Where are your customers close together, and where are they spread farther apart? That distribution matters when you're planning visits or reviewing local coverage. Individual addresses tell you where each customer is, but a long list can be hard to assess as a whole. A geographic view lets you look at those customer locations together. You can still check the people or accounts behind each concentration.
Choose what each customer row represents
Look at the source export before you upload it. A customer file and an order file can contain the same addresses but produce different density maps. If a customer placed several orders, an order-level export gives that location several contributions to the heat. That's useful for order activity, but it changes the answer when you want to count customers.
For customer density, combine the order rows by Customer ID in your spreadsheet first. Keep one row for each customer you intend to count. Add the fields you'll need to inspect a concentration, such as customer type, status, and the period of activity. If the business question is about accounts or households, use the corresponding ID and prepare that unit instead.
Choose one address type. A service address tells you where work happens. A delivery address tells you where goods go. A billing address may place the same customer somewhere else entirely. Label the field so a colleague knows which geography they're reading.
Check repeated IDs without removing every repeated address. Two customers can live in one apartment building or share a business address. Those are legitimate separate records. An accidental second copy of the same customer ID is a different problem and needs correction before it contributes heat twice.
Keep the scope small enough to explain. A first map of active customers during a stated period is easier to read than a mix of old customers, prospects, and every transaction you've retained. Save the source total so you can compare it with the mapped records later.
Put the list on the map
Choose Create New Map in Maptive, name it, and click Continue. Under Your Data, add your headered file with Add File or drag it into the upload area. You can paste a table with headers instead. Click Map Now after the data is ready.
Review the location matches if Maptive prompts you. Categorize Data separates address fields in Section 1 from titles and contact information in Section 2. Correct the column choices and select Done. If you later discover that a whole column was matched incorrectly, you can reopen Categorize Data from the Data tab.
Inspect a few addresses you know well, then check points far from the rest of the customers. Keep unresolved addresses in a separate source list until you can correct them. An address mapped only from a ZIP code has less location detail than a street address, which matters when you're examining a small neighborhood.
Before adding heat, confirm that the pins describe the intended customer population. A misplaced point can look like a new concentration once the heat layer spreads around it.
Add the customer-density heat layer
Open Map Tools, choose Heat Mapping Tool, select Marker Density, and click Add Heat Map. This style uses the concentration of mapped records. You don't need to supply a sales amount to create it.
For one category, use Select Sample and choose Specific Group. Select the category column and value, then add the layer with Add Heat Map. This lets you examine a customer segment without treating every row in the file as part of it. Use category names that tell you which population you selected.
Adjust the radius to change how broadly the heat spreads around locations. A wider radius can join nearby concentrations into a larger area. A smaller radius separates local patches more tightly. Neither setting adds customers to your file. Pick a radius that suits the scale of the question, then keep it consistent while comparing similar views.
Opacity, the intensity threshold, colors, and Gradient affect how easily you can read the layer. Change one setting at a time so you can see its effect. Keep the map at a comparable extent when reviewing another segment or period. You'll read a close neighborhood view differently from a broad regional view.
Marker visibility is another display choice. Keep pins visible while you're checking the source records. You can hide them for a less crowded presentation after inspection, but that choice doesn't remove customer data or control who can access it.
The other heat style, Represents Numerical Data, uses a selected numeric-only column. Use it when the question concerns a supplied value, such as sales during a stated period. A density map gives each customer record a contribution, while the numerical style gives locations the weight of that value. Many small accounts and a few large accounts can produce different patterns.
Check Unlink From Other Tools before relying on filters to change the heat. When the heat layer is unlinked, filtering markers can leave it unchanged. Keep that behavior deliberate and describe the heat population if it differs from the visible pins.
Check dense and shared markers
Inspect stacked records at one address
A numbered marker can contain several records at the same address. Click it and use the arrows to move through those records. Apartment or suite addresses can be mapped to the same building, so one pin may represent several customers who belong in your count.
Read the IDs and address details inside the marker. Confirm that the records are separate customers, then check whether missing unit information needs repair in the source. You don't need to force legitimate shared-address customers into separate locations just to give each one a visible pin.
This view helps you inspect a shared location. It doesn't establish that customers form a statistically distinct geographic group.
Use display clustering separately
For crowded pins across a wider area, open the Map Settings gear, choose Map Markers/Graphics, and find Cluster Dense Markers. It's on by default. The threshold controls when visible markers separate from clusters as you inspect the map. Grouped clusters can appear as pie charts, which helps show the mix of categories in a crowded view.
Display clustering organizes markers so they're easier to read. It has a different job from the heat layer, which shows a smoothed concentration. Check the individual records when you need their addresses, and keep the density view when you want to inspect the broader distribution.
Neither display clustering nor a bright heat patch supplies a statistical significance result. A formal clustering test needs its own method and comparison. You can still use these views to choose areas for further customer analysis.
Compare the intended customer segments
Use a field already in the table to compare customer types. Open the Grouping Tool / Colored Pins Tool from Map Tools and select the category as Primary Group. You can select or deselect groups to inspect the pins. For dated activity, open the Filter Tool, choose Create Filters on first use, and apply a Date filter to the relevant field. Group filters handle category values.
Keep the record unit, address type, and time period consistent when comparing segments. If one file counts customers and another counts orders, an apparent difference in concentration may come from the table design. Check the number of records in each segment as well as the colors on screen.
Make the linked heat behavior part of that check. If the heat remains unchanged after you change a filter, inspect Unlink From Other Tools. Don't read one customer segment's pins against a heat layer containing the entire customer file unless that comparison is intentional.
A dense customer area can help you plan visits, communications, or a local review. Its color alone doesn't tell you the area's profitability or the likelihood that prospects will buy. Those questions need the relevant sales, costs, and prospect evidence.
Check the customers in a concentration
Use the lasso icon on the map's right to select the area you want to inspect. Click its polygon points, then close it at the start or double-click. Choose Export location data to a file from the popup. Open the file and review the customer IDs, category values, and addresses. Check how many unique customers the selection contains.
For counts within a standard geography, open the Boundary Tool, choose the Boundary Set, select Marker Count / Location Density, and click Load Boundaries. The chosen boundaries give you a consistent way to compare area counts. Keep the same customer population while you do it.
If you need the whole filtered segment, open Export Data and choose Export Filtered Data. Select the file format and click Export Now. Export Data Visible on Map follows the current viewport, so it can leave distant customers out of a file for the whole segment.
Keep the map description with the export, including the customer unit, address type, period, filters, and heat settings. Give the exported file to the person responsible for the next decision. They can check the actual customers in the selected area before planning a visit list or changing local coverage.
