Geomarketing - find the relevant data for your analysis
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Geomarketing consultancy: You can find the relevant data for your location analysis with correlation analyses
The question at the start of a geomarketing analysis is often that of what data you actually need.
You can statistically check what environmental factors influence the success of a location with a geostatistical correlation analysis. This forms the basis for purchase decisions: What geographical data are relevant?
Market data cost money, the purchase has to be well founded. It is often not clear, when starting geomarketing, which data are required for qualitatively high value analyses. What environmental factors affect the success of locations? What data correctly reflects the relevant environmental factors? A geostatistical analysis such as correlation analysis delivers the appropriate answers and a valid decision basis.
Correlation analysis in three steps: Our procedure
The WIGeoGIS typically carries out a correlation analysis as a service in three steps: The selection of samples at the locations, calculation of variables describing the environment of all locations, determination of the relationship between the environment and the business success of each location.
Step 1: Defining a representative sample
During the 1st step, we define a representative sample in the existing location network. The following applies: The locations must be as comparable as possible with reference to format, area and location classification. You also have to define one or more key indicators for the location success. This indicator is usually the turnover at the location. It can also be subdivided into various product groups. You can thus determine which location environment is most favourable for the sales of a certain product group.
Step 2: Determination of relevant environmental variables
In the 2nd step, the possible, relevant environmental variables are determined for the sample. This can be calculated on the basis of potential data, which are aggregated in the catchment areas of the locations. Example: How many inhabitants, what purchasing force or what age structure are present within 10 minutes walking distance of the location? Relevant variables can also be provided by the distance from competitors or traffic-generating companies and traffic frequency data at the location. You thus obtain a mix of variables for each location that describes the location and its environment.
Step 3: Comparison of environmental variables with location success
In the 3rd step, we compare the individual environmental variables with the location success. A correlation coefficient is determined for each one. This describes the type and strength of the statistical correlation between a variable and the key indicator. Example: A high positive correlation coefficient variable "Percentage of 20 to 35-year-old inhabitants" within a catchment area provides information that this target group has a very positive effect on the turnover at this location within the branch environment. In addition to the calculation of the correlation coefficients, the analysis results can also be visualised by means of a plot.
Making better purchase decisions for geodata
The results from the geostatistical correlation analysis can therefore be used to provide evidence of which environmental factors are relevant for the evaluation of locations. They provide information about how strongly the environment and the success of the location correlate. The correlation analysis leads to a standardised and statistically-based evaluation of which variables are decisive for geomarketing. This allows you to make a better purchasing decision about geodata products. "Correlation analyses are particularly recommended at the start of a process. They offer very good basis for all subsequent GIS models." stated Andreas Marth, GIS Services WIGeoGIS.
Benefits for location decisions and Category Management
The results of the correlation analysis also form the basis for
further analyses. You can make more secure location decisions, for
example regarding new openings, closures or relocations. Positive
influences of environmental factors can also be used to search in a
structured manner for possible white spots on new locations. Negative
factors can be used to critically evaluate locations Product-specific
correlation analysis also permits you to implement Category Management
directly adapted to the location environment. "Correlation analyses are meaningful for every company that has to make location decisions. The prime example is retail trade", says Marius Herrmann, specialist for correlation analyses at WIGeoGIS.
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