GeoWorld Articles

Beyond Location, Location, Location: Retail Sales Competition Analysis


Further Reading for

Map Analysis

 Understanding Spatial

 Patterns and Relationships

(Berry, 2007 GeoTec Media)


Feature article for GeoWorld, March 2006, Vol. 19, No. 3, pgs. 22-25


<Click here> right-click for a printer-friendly version of this paper (.pdf).



Beyond Location, Location, Location: Retail Sales Competition Analysis


by Joseph K, Berry and Kenneth L. Reed





For years businesses have generally ignored spatial relationships in data analysis.  While common sense considers “location, location, location” a cornerstone of business, traditional analysis procedures force spatial information, such as customer location, to be aggregated into large generalized reporting units.  Pockets of sales on one side of a trade area are not differentiated from those on another side. 


Geo-Business describes an emerging discipline that uses Geographic Information Systems (GIS) technology to visualize and analyze spatial patterns and relationships within and among mapped data.  Many GIS-based websites link maps, multiple listing information and images for real estate properties.  These systems utilize spatial data-base technology for geo-query and display of existing information.


Site location is an early example of extending these basic mapping capabilities to map analysis.  The application uses GIS to identify the best location for a store based on demographics of the surrounding area, customer access, nearby competitors, as well as site-specific permitting, acquisition and construction considerations.  More recently targeted marketing, retail trade area analysis, competition analysis and predictive modeling provide examples applying sophisticated spatial analysis and statistics to improve decision making and ensure sound investment decisions.


The latter two of these emerging Geo-business applications, Competition Analysis and Predictive Modeling, serve as the focus of this paper.  The discussion summarizes the primary steps involved in generating predicted sales maps for various products. The approach is based on competitor locations to model relative travel-time advantage and existing customer data to model sales patterns and travel-time sensitivity.  While the experience described is the result of an ambitious prototype model involving a large metropolitan area and tens of thousands of customer records, the graphics shown have been altered and the details of implementation and results are curtailed.


Deriving Travel Time Maps


Most GIS users are familiar with travel time maps that accept starting and ending locations and then determine the best route between the two points along a road network.  However the complexity of retail competition analysis with thousands of customers and dozens of competitor locations makes a navigational solution impractical.  A more viable approach uses grid-based map analysis involving continuous surfaces.


Figure 1. Grid-based travel time analysis involves calculating effective proximity as a series of propagating waves that respond to various speeds along the road map resulting in access zones from close (blue) to far (red).  


For example, the extent of project area shown in figure 1 is an 18 by 23 mile swath of a major metropolitan area capturing hundreds of thousands of residences.  The inset in the upper left corner shows primary and secondary roads defining an analysis frame of the area comprised of four-hectare grid cells within a matrix of 228 columns by 153 rows (34,884 sample locations).  This spatial resolution is appropriate for strategic-level competition analysis; however a higher resolution is easily imposed by specifying a smaller grid size during the vector to raster conversion of the road map.


The large map in the center depicts the calculated travel time from “Our Store” to all other grid locations in the project area.  The blue tones identify grid cells that are less than twelve minutes away assuming travel on the highways is four times faster than on city streets.  Note the star-like pattern elongated around the highways and progressing to the farthest locations (warmer tones).  Competitor locations are indicated by the red dots. The tan areas are parks and natural or industrial locations not served by the road network.


The grid-based procedure is not as exacting as point-to-point solutions as it ignores one-way streets, left-turn waits, and other navigational factors; however the relative access surface it generates is appropriate for competition analysis (see Authors’ Note 1).  In a similar manner, competitor stores are identified and the set of their travel time surfaces forms a geo-registered map stack supporting analysis.


Determining Relative Gain for Stores


The travel-time surfaces summarize relative customer access to our store and competitor stores for all grid locations in the trade area.  Figure 2 shows 2D and 3D displays of the surfaces for our store and competitor #4.  Note that a travel-time surface is shaped like a bowl with the bottom of the bowl at the store’s location (map value= zero away) and the walls formed by increasingly larger travel-time values.  For any given location, the two travel-time values for a pair of surfaces can be easily retrieved and compared by subtracting.  In the example, the subject location in the figure is 17.2 minutes (34.4 cells away * .05 minutes/cell) from our store versus 42.1 minutes for competitor #4 store—a significant 24.9 minute advantage for our store.


A more useful Travel-Time Gain factor for modeling can be derived by evaluating the grid-math equation,




for two travel-time maps at every grid location in the project area.  The result is a map indicating the relative cost of visitation choices between the two stores for every grid location.  A Gain of less than 1.0 indicates the competition has an advantage with larger values indicating increasing advantage for our store.  For example, a value of 2.0 indicates that there is a 200% lower cost of visitation to our store over the competition. 


Figure 2.  The Travel-time value stored at each grid location specifies how far away it is from a store—location 103c, 86r is 17.2 minutes away from Our Store and 42.1 minutes away from Competitor #4 Store.


The Gain factor is a stable, continuous variable encapsulating travel-time differences that is suitable for mathematical modeling (see Author’s Note 3).  A Gain calculation summarizing travel-time advantages between our store and a competitor store is made for each cell in the project area.  These Gain calculations are used as input to the sales prediction models reported below. 


Overview of Processing Steps


Figure 3a summarizes the spatial modeling steps involved in competition analysis.  The first and second steps use grid-based map analysis procedures to construct travel-time maps for all stores of interest within the market area.  The third step generates mapped data of the relative gain for our store and each of the competitor stores.


Figure 3a. Spatial Modeling steps derive the relative travel time relationships for our store and each of the competitor stores for every location in the project area and links this information to customer records.


Critical throughout these steps is the use of geo-coding through address-matching to map each person in the region to a discrete grid cell based on latitude and longitude of their home street address.  This provides a universal key for transferring and integrating spatial information of Travel-Time and Gain to and from a database of non-spatial customer information that can be summed, averaged, counted or otherwise described for each grid cell and surrounding groups of cells defining town boundaries or sales districts. 


The Travel-Time Gain data is the cornerstone of competition analysis.  It provides a consistent measure of store competition that can be used in data mining along with traditional non-spatial variables such as customer sales activity, demographics, economics, life-style and life-stage information.  This link between spatial and non-spatial information supports the development of predictive models that consider detailed “location, location, location” information within traditional data mining procedures. 


For the predictive modeling, we used a regression approached using a specialized data mining technology, KXEN K2R, based on Vapnik Statistical Learning theory (see Authors’ Note 3).  This commercially available technology is similar to linear or logistic regression except that the convergence algorithms are not based on least-squares techniques. A main advantage is that non-linearity in the continuous input variables are represented by piece-wise linear terms, rendering hand transforms to add power terms (e.g. y = a + bx + cx2) unnecessary.


The tool also can automatically detect and handle multi-colinearity and works with ordinal and nominal input data as well as continuous variables.  It is capable of building excellent models from very large data sets, with thousands of columns and millions of rows of data in a single step.  These very large models can be automatically reduced to the minimal set of independent variables in a second modeling pass where information contribution criteria are applied to eliminate variables from the equation, in a manner loosely analogous to stepwise linear regression.


Figure 3b.  Predictive Modeling steps use spatial data mining procedures for relating spatial and non-spatial factors to sales data to derive maps of expected sales for various products.


Figure 3b summarizes the predictive modeling steps involved in competition analysis of retail data.  For the examples in this paper, a dataset was developed containing sales history for more than 80,000 customers in the trading area. Using latitude and longitude of each street address, customers were geo-coded to the map and the grid row and column coordinates assigned by membership within the latitude and longitude coordinates of a grid cell.  Thus each consumer was assigned to a grid cell location, for which the travel-times to all stores in the region were known.  The regression hypothesis was that sales would be predictable by characteristics of the customer in combination with the travel-time variables.


A summary of the dataset records used to develop the predictive models is shown in Table 1.  The first five entries identify a customer and their derived grid cell location in the analysis grid.  The next several entries are derived factors including non-spatial Customer Segment assignment and spatial Travel-time and Gain factors that are used as drivers by the prediction model.  The remaining entries are target variables.



Table 1.  Description of record types in the combined spatial and non-spatial data set for predictive modeling.


In Step 5 of figure 3b, a series of mathematical models are built that predict the probability of purchase for each product category under analysis.  This provides a set of model scores for each customer in the region.  Non-customers could be scored with these models, but that was beyond the scope of the demonstration project.  Since a number of customers could be found in most grid cells, the scores were averaged to provide an estimate of the likelihood that a person from each grid cell would travel to our store to purchase one of the analyzed products. 


Specifically, purchase probabilities for seven product categories were modeled for each grid cell.  The final step in the process simply maps the scores for visual analysis or subsequent analysis, such targeted marketing.


Example Results


The upper right inset in figure 4 summarizes the processing flow for mapping the probability of purchase.  An analysis dataset is constructed containing customer data, geographic location and derived spatial and non-spatial variables used in predictive modeling.   The probability of purchasing a specified product is derived for each customer and the individual probabilities in a grid cell are averaged, then the resulting map is displayed. 


Note in the example that the non-spatial summaries identifies that the most important variable in the model is the derived Customer Segmentation grouping.  The next most import variable is the Gain Factor for competitor #5, identifying that for Computer Sales this store represents our greatest competition.  This is somewhat counter intuitive as the map of computer sales probability shows this store fairly far away and in an area of only moderate TV sales probability.  It suggests that customers are somewhat more elastic toward travel for computer purchases than Phone or TV purchases as shown in the two smaller maps on the right. 


Figure 4.  Mapping the results of the prediction models provides insight into the level and geographic distribution of probable sales, as well as the competitor environment. 


The information on the level of probable sales, its distribution throughout the trade area and competitor environment can be invaluable in marketing decisions and developing strategies for product mix and sales emphasis.  




Several summary and concluding points can be made—


-          Historically spatial information has been ignored or at least aggregated to effectively non-spatial levels

-          Grid-based map analysis provides a consistent base unit (grid cell) for calculating spatial context and facilitating the linkage of GIS and traditional database systems

-          Travel-time and its derivative Gain Factor summarize the relative access cost for customers between stores

-          Spatial Modeling provides a new dimension to customer understanding

-          Sales can be related to Competitors as well as Customer Segments and groups

-          New customer acquisition targeting can take spatial factors into account

-          Specific product categories have very different spatial distributions

-          Competitive factors can be discovered and managed

-          Sales potential modeling/mapping identifies opportunities by product supporting target marketing, and

-          Store placement can leveraged through better understanding of competitive and customer positioning.


“Location, location, location” is a cornerstone of the business environment.  The potential of the convergence of spatial and non-spatial information in data mining and predictive modeling is great.  As business interests and GIS specialists become more aware of the informational value of digital maps and the unique characteristics of business applications, the rapidly developing field of Geo-business will alter our paradigms of maps, mapped data and their use in understanding and predicting the business environment.



Authors’ Notes:


1) For more information about travel-time calculations and applications in geo-business see, select the Map Analysis online book, Topic 14, “Deriving and Using Travel-time Maps.” 


2) For some purposes, it might be useful to transform the Gain equation to a logarithmic form (log(Gain)) that would linearize the function and perhaps better represent areas where competitor has an advantage.


3) For more information on the Knowledge Extraction Engines (Kxen) spatial data mining technology used in developing the predictive models see




Joseph K, Berry

Keck Scholar in Geosciences, University of Denver


Kenneth L. Reed

Senior Director of Business Analytics,