The complete guide to heat mapping your data
A heat map is a color-coded geographical representation of your data, highlighting the data's density.
So, for example if I wanted to see where my leads were, the concentrations of leads using a hotspot heatmap. It's a bit like a weather map. I can see where the red areas are the highest concentrations.
Or with a Regional heatmap, by state. The darker states, the darker colored states show the highest density. And the lower densities in red.
When generated in eSpatial, the correct heat map can be critical to better business decisions for you and your sales team. Here are a few quick examples:
It is an excellent application for sales and marketing departments. In the map below, you can see a sales organization in Kansas that has used our heat map generator to map their potential customers (state population) against their office locations.
Using heat maps to identify customer density can also be crucial when planning and analyzing your distribution or service network.
Based on your findings, you can look at your heat maps and identify where to locate each center best so it's accessible to as many customers as possible. If a distribution center is too far away, placing it closer to clusters of customers may make sense.
Many marketing teams use heat maps to analyze third-party data and determine where to run effective marketing campaigns. For example, a fashion retailer in Texas is planning to run a campaign involving eye-catching billboards.
Mapping demographics with a regional heat map, the marketing department identifies the ZIP codes with most of their target audience. It allows the marketing team to use their budget and resources effectively by planning and executing their campaigns in the high-value areas they found on the map.
Franchises searching for areas to expand their brand can also put regional heat maps to good use.
In the situation illustrated below, an organization mapped its existing locations (shown as stars) compared to the number of customer inquiries (represented by the heat markers). Any area where the franchise gets a high volume of inquiries far from a store location will be an ideal spot for the next franchisee.
Earlier, we showed how regional heat maps are helpful for location-based comparisons. The sample below shows a regional heat map of customer satisfaction rates nationwide: Green indicates a high level, and red indicates a low level of customer satisfaction. It highlights which territories have the most dissatisfied clients, so management can immediately start making necessary changes to improve the situation and avoid any severe loss of business.
Any company must understand where it is generating the most profit. Heat maps are a powerful tool for determining where your profitable customers are based and examining how these locations compare.
Understanding where your customer inquiries are coming from and matching that to current sales coverage is typical in heat mapping for sales. As you can see below, this helps to visualize the team's addressable market easily.
With this information, you can decide where current levels of sales coverage are appropriate. Then, you can work to eliminate gaps and address overlap to allocate resources as efficiently as possible.
A common challenge for any field salesperson is choosing the best place to begin. Heat maps can help sales reps find pockets of opportunity and use them as starting points.
Sales managers can use regional heat maps to assess sales team performance. The visuals transform the coaching process as you can analyze multiple metrics to gain a complete picture of sales performance.
Last but hardly least, you can also use regional heat maps to compare the performances of specific reps. While looking closely at raw revenue is tempting, evaluating salespeople purely on one metric will give you a partial picture as a manager.
Heat map data allows you to judge your salespeople while understanding the market realities they face: competitors, excessive drive time, and other factors.
You first have to add data to your map – it will form the basis for all of your analysis and be displayed as points on your map. This could be sales data, service data, demographic information and other KPIs, with one row for each unique address.
Click the Add Data option from the control panel. Then click Upload new data and follow the instructions.
The boundaries of your regional heat map will define which groups your data points will be included in. This is often done by state or three-digit ZIP code. You can select various types of boundary data from our extensive data library.
To add your boundary, simply select Add Data and then Add from Library. Choose the eSpatial data store tab and then pick your boundary. In this example, we’ve chosen US states. (For more boundary options, visit our data set library.)
From the Control Panel on the left side of your screen, select Analyze from below your dataset. From the options, select Regional Heat Map.
Next, select the points and boundary data sets. In this case, the points data is Client Accounts, and the region data is US states. Then, click Complete to generate your regional heat map.
Below, the regional heat map you created shows the states with the most client accounts. You can see what each color represents by checking the legend on the right side of the workspace.
The default color range is from yellow to red, but this can be changed in the styling options.
Upload a data set (such as an Excel spreadsheet or CSV file) that contains one unique address per row as well as any other value fields you want to add to the map. In your new Workspace, select the Add Data option from the control panel. Then click Upload new data and follow the instructions.
If you wish to see the originally mapped data expressed as pins on top of your heat map, tick Overlay Pins from the bottom of the menu. Color Snapping removes the noise around the edge of your heat map.
Exit the Style Panel to view the map you have created using our heat map generator. The legend in the top right of the screen will highlight the color range from low to high data density.
You've now created your own hot spot heat map.
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