Visualize and Map SalesForce Leads with SpatialKey - Part II
Create powerful interactive maps and dashboards to visually analyze the location and time information in your SalesForce.com data. Use interactive heatmaps and timelines to visualize and understand your CRM data better than ever before. SpatialKey provides true geographic business intelligence for your SaleForce data.In Part I, we showed how to export a SalesForce tabular report as a CSV, and import that file as a dataset in SpatialKey. In Part II, we’ll show you some reports that you can create in seconds using this dataset.
Part II. Creating Report with the Leads Dataset Generated from your SalesForce report.Sit back and watch how quickly you can analyze your SalesForce data in SpatialKey. In this video, we will create a report using dataset we imported in Part I. We’ll quickly uncover and visualize trends based in geography and time.
Once your data is uploaded, it appears as a dataset in SpatialKey.
To create a report from a dataset, click “Create Report.” You’ll be prompted to select a report template to use. For this tutorial, we’ll use the Dashboard Creator template.
The Dashboard Creator template is a blank canvas, allowing you to select any number of pods, each based on a representation of a column (attribute) from your SalesForce dataset. Notice that an icon appears next to each attribute indicating the type of data - and therefore the type of pod available for that attribute.
The map pod is a logical place to start. By selecting this pod, we’re presented with a heatmap of the geographic distribution of the SalesForce leads in this dataset. We can mouse over any area to see the number of Leads represented in that area.
Next, we’ll add a categorical pod representing Lead Source attribute. At first glance, we can quickly see the frequency of leads by Lead Source. In addition to being able to see this distribution, we can use this pod to filter the leads in the report by Lead Source. In this case, we can see the 755 “Seminar - Internal” leads by double-clicking that value.
By adding a timeline component, we can quickly see the distribution of leads over the entire time period of the dataset. We can quickly ascertain that the number of leads per week is relatively consistent, with a notable spike for a particular week in July.
By selecting that week in the timeline, we filter the dataset (and therefore the Lead Source and Map pods) to only show leads for that particular week. This quickly exposes the fact that the majority of leads for that week came from the “Trade Show” Lead Source. It also shows that most of the leads for that week (and likely for the Trade Show) came from the Southwest portion of the country.
With the addition of the timeline, we can quickly see some temporal trends related to Lead Source. For example, Lead Sources from Partners were strongest in the first quarter, decreased but stabilized in the second and third quarter, but dropped significantly in the fourth quarter. One could easily select time spans using the timeline to see if there were related geographic trends.
Leads from the Web were opposite. The first quarter was relatively weak, with a sharp uptrend in the second quarter.
A report can contain any number of pods. A filter applied to any pod filters the entire dataset, and therefore effects all other pods providing a view of that same dataset. In some cases, you may want to use a pod to filter out particular values or ranges of values, but not show the pod. By simply closing the pod with the filter still in place, the pod is placed in the “filters” list at the top, and remains active. From the pod list, you can reopen the pod or delete the filter.
Just like the other pods, the map can act both as a visual reporting component and a filter. By panning and zooming the map (with the “Map Extend Acts as Filter” setting enabled), the dataset (and all pods) are filtered to only show data within the extent of the viewable map.
When you’re ready, you can save a report. Include a report description and tags. By default, a report is private and only viewable by the owner. By marking the report “shared” any user with access to the dataset can see the report.
This brief tutorial just scratches the surface of SpatialKey’s data analysis and visualization capabilities. Learn more about how SpatialKey provides geographic business intelligence at SpatialKey.com
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