The Visual Crossing Location Analysis Story is an example SAC dashboard used to convey the results of the location analysis done through Visual Crossing Location Analysis Designer. Through the designer tool, a user can upload or manually place their locations onto a map as well as competitor locations and proposed new locations. The system will utilize all of these locations and complete a competitive landscape analysis using a Distance-Attraction model that is based upon a population demographic dataset. Based on an ‘Attractiveness’ measure supplied in the locations dataset, the tool will use that measure combined with the population and a distance degradation formula to determine the probability for a given set of customer to visit any location.
The result of the analysis is a single dataset that contains all of the locations, available populations before and after the proposed location is analyzed. It includes probabilities, distance and direction of the population areas around each store. This example story is designed to show how the Location Analysis dataset can be used for further analysis. The resulting analysis in this example will inform the user about the expected population gravity to every location in the model.
The Story Sections
There are 3 main sections in our Story: Overview, Comparison and By Location.
The ‘Overview’ tab of the Story give us an initial overview of the ‘Current Population’ landscape. All calculations here are filtered either upon Locations (aka My Locations) and Competitors. It does not include information about the placement of the Proposed Location.
The first two sections detail the current population that is attracted to My Locations and Competitors for a side by side comparison of performance. Near the bottom right portion of the Story, the user can get a Donut Graph view of Market Distribution by percentage.
The graph at the bottom outlines the gravity attraction for each individual location so that users can easily see the top and bottom performers in the area.
Two slider input controls exist at the right of the Story to allow the user to translate population numbers into revenue estimates. These numbers are provided directly by the user for this analysis and are not part of the input data. The user can select the average revenue expected per customer visiting their location as well as an expected penetration of the total population that can be expected to utilize their business service. We recommend testing a baseline value here to match current performance numbers to acquire a good estimate.
In the numeric indicator control under the sliders, the user will see a result of the measure formula to estimate revenue. NOTE: All estimated revenue numbers on the Story are derived from these input controls.
The ‘Comparison’ tab provides a before and after picture of a new location placement. All of the measures on this page are dedicated to showing the effects of adding a new location. Users will often see diminished values to current locations (Cannibalization) as well as the effect on customers and the overall net effect to their business as a whole.
The bar chart at the left shows specific population delta effects due to the new location. This will help users determine if particular stores are disproportionately affected as well as if the new location has the desired effect on the competition.
The middle section of numeric indications show side by side the population gravity currently vs the new gravity with the placement of the new location. The right column has additional data to show overall net effects and an estimated revenue affect to the whole business.
The bar chart at the upper rights shows how the current locations and competitors are affected by the population pull from the newly proposed location.
The donut graph is similar to the overview donut graph but has the added addition of the new location so users can see the overall market percentage changes from adding this location.
This tab of the Story will help users to analyze individual locations by Current Population as well as Estimated Revenue for that location.
A filter selector is provided to let the users isolate the location of their preference.
Finally, a bubble chart is provided to let users see the distances that customers can be expected to travel to this location. This distance profile can help users to understand how much a location depends upon near or distant populations.
Also, by using the probability color a user can determine if their high probability population centers are only isolated around them or if they have a clear path with no competition. This scenario typically appears for locations on the outskirts of a competitive area.
The Location Analysis Story is based upon a powerful geographic engine that uses population demographics and sophisticated distance-attraction algorithms to provide users with a clear look at how they compete in an area as well as give them the tools to see the result of actions to both open new and as well as close existing locations.