Most people are very comfortable using and interacting with digital maps on web browsers and smartphones. As a result, most users understand common terms. For clarity, in this chapter, we will go through the most important concepts.
Components of a good map visualization
You are familiar with maps, both from your school atlases and from the web and mobile applications, but have you ever stopped to consider what goes into making good visualizations?
First, the map is a diagrammatic or graphical representation of an area that shows physical features from that area. Second, there are the features represented on the map. Maps usually contain three types of features:
- Those displayed as points, such as addresses or customer locations
- Those shown as lines, such as roads or rivers
- Those shown as polygons, such as boundaries of regions like counties or sales territories
Add a Title to a map to explain the map's purpose to the viewer. Similarly, you might want to add annotations to add context or provide further information about the map and the visualized data. To bring your maps to life, you can add a graph or chart, making it easier for viewers to understand your data story. Including a data table that shows some of the data displayed on the map in a tabular format is good.
Depending on the data you have used, it might be necessary to display a copyright notice.
Preparing the map
To prepare a map, you must first consider which data you want to visualize. You have your sales and marketing data, such as customer and office locations or sales territories. You can use a base map to give the data context.
Next, you can include a geographic boundary dataset such as Postcodes, Zipcodes, County, State, or Sales Territory boundaries. It is helpful to have your data by region to summarize your findings. Finally, add third-party data, like demographic or census information, to understand or analyze your market and data.
Most mapping software products will already include base map options from Azure, Google, Bing, Open Street Maps, or other data providers. They will contain a library of boundary and third-party datasets.
To prepare your map visualization, you must pick the base map style you want to use and if you wish to add any boundary or third-party datasets.
Load your datasets first to the software to create maps. Organize your data as a table with rows representing each unique feature you want to display (for instance, one row for each customer location) and columns representing the attributes of each feature.
One or more columns will need to include address data to enable the software to plot your point locations. Load your data with a longitude/latitude location assigned to each feature based on its address using a process known as Geocoding.
Organize data so each feature type (customers, offices, outlet, distributor) loads as a separate dataset. It allows you to add it to maps as separate layers and to add different colors or symbols for each different dataset. For instance, when viewing your map, you can distinguish office locations from customer locations. You will also interact with the map more effectively by switching on and off which layers to be displayed as required.
When you have added your data to the map, you will need to decide how you want to display each feature. Good software will usually assign a default style and color and ensure that each dataset or layer represents it differently. You have the option to change your defaults.
For point data, you will need to consider the symbol to use, do you want to use one already available in the software library, or do you want to add a custom symbol? You might want a simple dot or a pushpin for each point; you might prefer a meaningful symbol such as a factory icon; you may use a company's corporate logo.
Point display size depends on the volume of points you display. Choose the size that makes the most sense. A clustering option may also be appropriate for high volumes or dense data. The cluster displays the number of points, and you can show all points by double-clicking. It makes it easier to display a high volume of locations. The color and transparency of the point.
The best mapping software will also allow you to vary the symbol, size, or color of points depending on some attribute value associated with the points. For instance, you would be able to show higher value customers with larger symbols or with different colors. The map legend would contain information to explain what the different sizes, colors, or symbols represent.
For polygon data, think about the line style and thickness of the boundary, the fill pattern or color of the polygon, and whether you want the fill to be transparent or not. You can change the polygon style depending on attribute values like point datasets.
Labeling your data on the map is another important consideration. Good software lets you automatically generate labels for each element based on one or more feature attributes. Labels add information about each feature. However, if you have many features on the map, labels can become cluttered and unusable.
For label styling, you need to consider size, text font, color, and whether to include a box or frame. Your mapping software should allow you to control the positioning of the labels relative to the feature and whether you want to display all labels or just those that don't overlap with others. The best software will have sophisticated algorithms to optimize label placement to avoid clashes.
In the last section, we discussed the best way to style the data on the map. In this section, we will discuss analyzing that data for insight. You can use a basic map style and display every feature individually or deploy a thematic map, where the map shows the correlation of datasets to regions. Thematic maps allow you to quickly identify your data's hot spots, clusters, and gaps. There are two main types of map analysis, summary analysis, and proximity analysis which we will discuss in this section.
1. Summary analysis
Summary analysis refers to summarizing your data to visualize coverage, gaps and highlight areas of opportunity. It is done on a regional basis.
2. Heat map analysis
Heat maps use a color gradient to indicate increasingly higher data density areas in a geographic area. Heat maps are one of the best visualization tools for dense point data and can quickly identify clusters with a high activity concentration.
3. Regional heat map analysis
A regional heat map uses graded differences in shading or color to indicate some property's aggregate or average values or quantity in particular areas. For instance, the aggregate sales value or volume for each state or territory is color-coded into ranges, with each color representing a value.
4. Bubble map analysis
Another map places bubbles or other symbols in the regions to represent the displayed values. Using two data sets on a bubble map may also compare data sets linked to one another. An example would be to have one dataset showing college rankings with the average salaries of graduates of each college.
5. Proximity analysis
Use proximity analysis to identify locations close to your chosen central location. It is helpful for sales call planning, site location, event planning, and performance reporting.
6. Radius analysis
Radius maps, also known as buffer maps, are helpful when you need to understand your data in relation to its proximity to other features. For instance, you may need to visualize how many customers you have within a 10-mile radius of your office locations or how many customers you could serve in a particular region if you signed a new franchisee.
You should be able to generate buffers one at a time or for multiple relationships. And run multiple buffers of 5, 10, 15 miles from a single point or all points in your dataset. Each buffer is shown on the map as a polygon for further analysis.
7. Nearest neighbor analysis
Another proximity map analyzes relationships between two datasets based on nearest neighbors. This map type allows you to identify the nearest set of features in one dataset compared with a starting point in the other dataset. For instance, find the nearest ten customers to my hotel.
8. Drive time analysis
Drive time analysis is perfect for field sales teams and event planning as it allows you to identify data points within a particular drive time of a center location. For example, you are planning a training event for your sales reps. You have a shortlist of venues, and you know attendance is higher if you limit travel time to an hour. You plot the potential venues on your map and your sales rep locations and run a 60-minute drive time buffer around each event. The location with the higher number of reps within that zone is the best venue for the event.
Improving your map visualization
So your map is created, and now you want to add tables, charts, titles, or annotations. These can enhance the meaning of the map and help communicate your message more effectively.
1. Creating multiple map views
It is often necessary to create several map visualizations from the same data. For instance, you might have imported all of your sales data from your CRM but want to prepare separate maps for individual sales regions or reps. The mapping software allows you to filter the data to see a sub-set of the dataset. To produce multiple viewpoints, you can produce a simple pin map, regional heatmap, and buffer map on the same dataset. You can easily create and save multiple maps linked to the original dataset, making all maps readily available to launch as more up-to-date data becomes available. Like in Excel, each map should form part of a larger 'Mapbook' that can be managed together or separately for styling, sharing, printing, etc.
2. Using route optimization
The image above shows an optimized 5-day sales route.
Field employees are expensive and time-constrained. You should optimize their time in the field to get the most from your team. One way to do this is to reduce the time needed to complete an itinerary by planning routes using mapping software with built-in route optimization. The software optimizes routes for up to 20 days. It can add up to one extra sales call a day or an additional 30% selling time. And that reduces travel time or fuel consumption.
3. Using maps for territory alignment and management
Territory management is the process that optimizes sales workloads, allocates customers or products to representatives, and assigns personnel to territories. Territory Alignment and Management unlocks hidden revenue and inefficiencies in your territory designs. Organizations can boost revenue by 12% without additional sales resources with smarter territory design.
Territory Management is a crucial component of the Sales Performance Management process, essential for salesforce planning, resource deployment, incentive compensation, and financial reporting. Yet, much of the work in designing Territory alignments is done using spreadsheets. The fact is that most territories incorporate real-world locations better understood with a data visualization tool.
Territory visualization is a critical aspect of the alignment process. Others include:
- Hierarchical representation of territories
- Territory re-alignment capabilities
- Territory optimization
- Balance with a workload
- Weighted balances
- The ability to use other data sources to help balance assigned territories, collaboration
- Sharing results for ongoing reporting