Geomarketing for the Retail Industry

GIS Contributor


This guest submission by Michael Fabing from the company Mapfusion, a web-software company specialized in GIS mapping and geomarketing.

Geomarketing and Geocoding

Given steady advances in location reporting engines, several years ago two additional problems (ungeocoded data and inaccurately geocoded data) were surmounted enabling large retail organizations to not only to track their customers both demographically and physically, typically via exit surveys and loyalty programs, but also to identify and attract them. To increase competitiveness, most national organizations have begun using geocoding and new mapping technologies, complementing existing location-based analysis tools and creating major efficiencies in-house.

Geocoding is deemed vital nowadays to governments that require socio-economic data for decision- making; their agencies and various departments have spent millions refining this process. In fact, technological advances in geocoding have clearly demonstrated a positive impact on several industries over the past decade. For instance, fleet managers and dispatchers have access to geographic information made possible by geocoding initiatives, and emergency personnel such as police, firefighters and ambulances, depend on mobile GPS devices to more accurately and easily respond to crises.

Early adopters of GPS technology included taxi companies, using it to more easily move between addresses. Whereas wildlife biologists also use GPS technology to track and map the movements of animals, birds and fish. Today consumers use GPS-enabled devices to quickly obtain real-time directions while operating their motor vehicles.

The focus of this paper is geocoding of location data and, when combined with analytics and mapping technologies, its impact on Retail. Geocoding is the process of deriving the position of a location in any standard geographical notation. The most well-known form of geocoding is latitude and longitude.

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Address information is one of the most common attributes of data stored in a business data repository, and examples of such data can be addresses of employees, store locations of retail chains, customer addresses (via catalogues) and sales of a product. Sales of a product can be linked with an address through a loyalty card, on which they collect points for each product purchased. This card contains the address of the customer and so geographic data on sales is collected.

This data facilitates data mining using algorithms such as market-basket analysis by geography, and using the same data in spatial form, one can analyze consumption of any particular product in an area.

The basic elements of geospatial data, all map elements (reflecting objects, physical and virtual) on the surface of the earth (and sometimes beneath it) can be stored and expressed as points, lines, or polygons. It is the role of the geospatial software to combine these in an eye-readable expression (first on the screen, and then on paper). Complementing yet another application for Fleet and Logistics managers, geospatial technology also enabled sophisticated analysis using these elements, like calculating shortest distance routing.

Most maps are composed of several kinds of geographical objects. As the objects are digitized, they are stored in distinct tables, one table for each class of objects. Geographic Information System (GIS) software takes the data in these tables, and constructs a “layer” containing just those objects. The sequencing of layers can be significant; objects in higher layers often visually cover part of objects on lower layers.

Information about business relationships (customers, vendors, outlets, competitors) includes a variety of traditionally tabular facts: age, first contact date, scope of business, and potential for future revenue. Where they are located is also valuable information. The proximity of a customer to a retail outlet (and to a competitor’s location) is very valuable information for business analysts. Geospatial data and GIS tools can tell retailers where things are, instantly, and how close they are to each other.

Understanding Customers Using GIS

Understanding where their customers are enables retail managers to better serve them. Retail analysts gain a deeper understanding of customer behaviour and purchasing preferences by including location technology and demographic data. This intelligence is leveraged to increase profitability and realize greater ROI from other initiatives, and to launch expansion strategies.

To get the most out of a location-based geocoding system, retailers must first collect at least the most basic geographic information from customers, such as a postal code, at the store level. Preferably the information includes a name and address, the products they purchased and the costs of the products. This information can then be linked with geodemographic information to bring new and meaningful insight into these purchases.

During the development of any retailersʼ marketing plan, topping the wish list is a desire to know what specific groups of customers were the most historically profitable and therefore deserve a stronger focus and higher marketing budget. Retailers already using customer-modeling applications can apply geodemographic data to tie existing information to supplement neighbourhood demographic data. This provides an ideal understanding of where the most profitable customer clusters are located and what they are like in terms of demographics, based on census data, and lifestyle habits.

Purchasing power in Germany, 2011.
An example of geomarketing: Purchasing power is determined according to consumers’ place of residence and is therefore an indicator of the consumption potential of the population living in a particular area. Source GfK GeoMarketing, 2011.

After investing in sophisticated Customer Relationship Management (CRM) systems, retail businesses typically understand their customers’ purchasing habits via their postal codes, income and other demographic characteristics. But location affects customer behaviour as much, if not more than demographic characteristics. To fully understand the customer, retailers must understand where they live and what their location means.

Knowledge is power. Combining CRM and geocoded retail analytics facilitate better commercial decisions – based on historical consumer and relevant store data. Senior management at Retail are now handily equipped with demand and sales forecasting tools, and trend analysis by customer segment related to product sales. For them, technology such as the Mapfusion data-driven engine is identifying customer segments with similar sales pattern, and segments with declining sales or those generating growth, and simply measure performance.

This technology is enabling forward-thinking companies to evaluate the effectiveness of promotional campaigns, and determining the impact of marketing events (coupons, discounts, specials, advertisements), as well as customer retention and acquisition.

Collecting customer information in a CRM database is a necessary step, but retailers understand they can only begin to reap profits from that data by analyzing it. The numbers themselves can only go so far, the best way to truly analyze the data is through location-based intelligence. Only then can retail managers understand their customers in a real-world context, providing the information needed to better serve them.

Income, environment, means and some others (whether by race and ethnicity) define neighbourhoods, but when it comes to any neighbourhood, they are, in fact, the sum of their parts. The object of effective marketing is to look at all of these factors and determine community clusters and their buying habits in order to allocate budgets appropriately. One method comes from combining the spatial information of a given neighbourhood with its demographic data: geodemographics.

With the dynamics of immigration and upward mobility factors, people with similar cultural backgrounds and perspectives gravitate toward one another or “cluster” to form an identifiable community. Once settled in, people naturally tend to emulate their neighbours, adopt similar social values, tastes and expectations. Most importantly to retailing organizations and their managers, these neighbours share similar patterns of purchase decisions and predictable consumer behaviour.

Soon after synergistic location technologies were introduced, retailers began to match obvious characteristics within neighbourhoods. Knowing their marketers would never target two distinct audiences with similar products, certain patterns have emerged that have fostered intense competitive battles for marketshare. Executives, for example, in the automobile industry realized each community might have several similar characteristics and enjoy homogeneity when it comes to buying, but purchase choices ranged from luxurious European cars to inexpensive Asian cars.

With geodemographics, retailers can create far more effective marketing programs that attract targeted customers.

Discovering drivers of product sales, and quantifying the impact of each driver is crucial to all retailing organizations. Determining why sales in reality differed from a well thought-out plan and how to quickly act in order to maximize marketing efforts, whether by cross-selling and up-selling, or in particular offering the right product to the right customer segment at the right time is critical to retailing success.

Location-based data mining and mapping services help retailers dramatically increase revenue and profitability, develop lifelong relationships with the most profitable customer segments, enhance new customer retention and acquisition campaigns, and increase targeted cross-sell and up-sell promotions.


For retailing organizations, retaining a customer is cheaper than attracting a new one. While it has been stated word of mouth is the best form of advertising, it can also prove to be a nightmare for marketers when something goes wrong. Consumers are fickle.

Take the example of a wireless carrier that is trying to understand the difference between customers who have terminated their service and those who remain loyal. The carrier has two data sets filled with demographic information: one of the churned customers and another of those who are loyal.

When you look at all the data points associated with churn, such as length of contract, age, income, gender, and monthly usage, it turns out that whether people live in an area with poor signal strength is the most predictive variable. If people are not able to use their phones, they will leave the service.

Gaining new insights is one thing, but the real power comes from using that insight throughout the organization for better, more profitable decision making. Knowing that the signal strength affects customer loyalty may determine where the carrier puts new towers. It may also have an impact on where it focuses direct marketing and sales campaigns, and by giving a sales office this information–with the real-time ability to utilize it – the organization can be sure to offer a mix of products and services that will enable the company to be competitive even in the face of a high churn risk.

Although the physical distances of customers from retail locations are important, where customers live provides clues regarding other significant characteristics, not gleaned directly from them. In the early days of online mapping, plotting the “centre of gravity” of a community cluster may have indicated it was actually west of a current store. To take advantage of the intelligence the map provided, a decision was required to relocate, open a new outlet, or shift the centre of the cluster.

Retailers can score customers based on their individual value (by tracking their purchases), and with a Mapfusion engine now graphically express that score visually (such as the size of the dot on the map), which tells a slightly different story. Of course, this technique can be applied to any quantitative measure of customer value, such as profit (though more complex to calculate) and the net effect of individual transactions.

Retail managers now look for similar customer profiles in each cluster. By locating high ROI clusters with untapped potential, the retailers realize the full potential of catalogue mailing, and direct selling efforts online or wirelessly, within specific markets.

This information proves powerful in analyzing specific product purchasing trends. It enables retailers to identify where certain product transactions were taking place, which helps them better stock stores and target selected products to specific geographic areas.

For more information you can contact:

Mapfusion Inc.
61 Uplands Ridge SW Calgary, Alberta, T3Z 3N5 Canada
1 877 801 4441


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