Geomarketing: how can you fully exploit its potential?

14 December 2017 Updated at 23 May 2019

Geomarketing is a popular discipline among both marketers and operational managers. This is mainly due to the visual simplicity of the results (which can be represented on a map), allowing for quick decision-making. However, although geomarketing is currently regularly used in businesses, it is almost always badly or under-exploited. How can we put that right?

First of all, why is geomarketing under-exploited in this way? This is largely due to the fact that data external to the company are not integrated (or are very little integrated). This contrasts with data directly associated with the enterprise (KPIs for consumption, competitive pressures, etc.) which are fully available for the decision-making process.

However, external data require prior work to find the information that will bring a plus to the business. They also entail a simultaneous analysis of these data taken as a whole.

Geomarketing: what companies generally put in place

Geolocation data are not new to businesses and are used in a number of ways, including monitoring activity (penetration rates, turnover distribution, geographic distribution of customers), monitoring competitive pressure, or surveying the potential of geographical areas. They serve to improve knowledge of the customer base, and thus to increase commercial activity.

Some ways in which geomarketing is used by brands:

  • Optimising the installation of a brand in an area with due attention to competition and demographic data;
  • Defining catchment areas with an initial segmentation of customers by activity, in order to define the priority zones for prospecting (consumer leafletting, advertising displays, etc.),
  • Personalising the offer by geographical area (rural or urban areas, proximity of a border, etc.), so that brands can optimise their distribution channels and what they offer,
  • Measuring the impact of commercial campaigns by area and defining differentiated customer behaviours (purchase frequency, average basket, types of products consumed, cultural practices, etc.).

These are all uses that have enabled enterprises to develop their networks and to take a small step towards the ultra-personalisation of offers. However, this is just a small part of what can be achieved with geolocation.

Why is this not enough?

The main reason for this inadequacy is the arrival of open data and the provision of new sources of public information. These new information sources supplement the data that was already provided by INSEE.

They include:

  • National macro-economic data for each country (http://data.worldbank.org/). These data reveal, inter alia, the stability of a country or enable companies to understand the success or failure of commercial campaigns in terms of the economic background (for example, for an international business will have different results in different countries for the same marketing activity).
  • Micro-economic data at the national level for France (https://www.data.gouv.fr/fr/) with the availability of API data. This is a rich source of information on employment, business, housing, transport, etc.
  • Micro-economic data at the regional level and for each major city (e.g. https://data.iledefrance.fr/; https://opendata.lillemetropole.fr/) where enterprises can find data such as a list of past and future sporting events (which can be useful for sports chains ahead of an event, and so forth).

The appearance on online platforms

Online platforms such as Open Data Soft have also appeared. They provide both data and interactive maps. For example, you can find a list of cultural events in France here.

Nonetheless, the arrival of open data is still fairly recent, and some regions and cities have so far made little data available. Another difficulty lies in the choice of useful indicators from among the mass of new data. And it can take time for this choice to be established.

How can you exploit open source data in geomarketing?

The use of external data is not yet a normal part of good practice in every company. And few of those who have made the leap exploit all its potential. The external data used mainly consists of demographic information: number of residents by area (crossed-referenced with the number of customers, to obtain the penetration rate), average household income, population density, and so on.

Any number of external indicators density, average income, number of inhabitants) to be cross-referenced with each internal KPI (turnover, average basket, etc.) and map representations to be superimposed. This quickly becomes indigestible and the mapping loses all value.

Let’s take a straightforward example. Let’s say that I want to know the penetration rate per area in relation to average income per area, and whether I am in a high-density area or not:

Penetration rate

Density

Average income

Even if in our example, the grid is very fine-grained, you can see the difficulty of cross-referencing the data (even with only two external information items to cross-reference to an internal indicator). It is therefore impossible to cross-reference a dozen items of external information using this method.

So companies limit themselves to cross-referencing their KPIs with one or two external indicators, severely restricting the potential for information.

Condensing external information into a single item

Therefore, the external information must be condensed into a single output.

On possible solution for this is to:

  • select all the external indicators that provide meaningful information and which will serve to characterise the population (e.g. density, average income, proportion of young people (aged 18-25), social housing rates, with as many indicators as desired).
  • create a mixed classification with these data (k-means + ascending hierarchical classification) which will make it possible to attribute a group to each area reflecting the characteristics of the area. (An area here being a square or rectangle containing between 10 and 1 000 people).

Each area will be then be characterised by a group (e.g. young urban executives, large families on the periphery, modest incomes, etc.) Each of these groups characterising a typology of individuals (e.g. “ young urban executives” refers to young households with high education levels living in dense zones).

With this is a starting point, it becomes easy to cross-reference the KPIs already in use with this new information. Simple differentiated marketing actions can then be set up fast (quick win). It will be also be very simple to allocate every customer (those who have given their postal addresses) to an area and hence to a group.

Some companies provide customer segmentation maps, but these are relatively expensive, and not necessarily adapted to the use that is made of them. Some features are of no interest, while others may be absent.

Combining decision-making tools and publicly available data

Currently, almost all businesses obtain the postal address of their customers, and very few have failed to convert these data into coordinates. This provides a basis for geolocation data for valuable results in the short term, thanks to the introduction of new decision-making tools that will include publicly available data.

In this way every business could create its own decision-making tools incorporating the external data of their choice. However, they must not forget to adapt what they offer to this new information. This stage, which is not the simplest, is too often overlooked.

Going beyond the arrival of external data, we have recent seen the arrival of new technologies (Beacon, Wi-Fi, ultrasound). These increase the potential of geomarketing, in particular by monitoring the customer journey in stores. These new technologies are at an early stage in these business, and the of these new data remains to be developed.

Bilal Eddajibi
Bilal Eddajibi
Business & Decision

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