Exploitation of Data for Marketing Purposes
The proliferation of data on the Internet encourages companies to exploit this gold mine to increase their market share and increase the volume of their sales.
Geomarketing, a technique that has the wind in its sails
Recent data mining techniques for marketing include geolocation. Thanks to the latest technologies, especially those used by smartphone applications, companies can send targeted advertising. Regulations may consider that companies have the right to know their customers better in order to correctly define commercial actions. But it would be wrong to believe that all companies have the right to use the personal data of consumers as and when they wish.
The use of geographic segmentation must not place the consumer in a position of automatic exclusion from a service or contract, nor deprive the customer of a right enjoyed by all members of a segment. In other words, geolocation and the type of profile in which a customer has been classified should not be used as a pretext to oppose a refusal of sale to a consumer for example.
In addition, the use of a geolocation process requires the adoption of specific precautions: it is particularly recommended to allow consumers the opportunity to accept or refuse the location via their terminal. In addition, companies have an obligation to clearly inform customers that their device could be located.
The segmentation
Another method used: segmentation. It allows to group consumers, not by geographical area, but by type of profiles. For obvious reasons, some data are excluded from the outset. It is not allowed to collect or exploit them. These are: racial origins, union membership, political opinions, and state of health.
The limits of data mining in marketing
Exploiting the data for marketing purposes is useful. But you have to be aware of its limits.
First, the choice of data processing methods has a strong influence on the results obtained: the analytical methods are implemented with some subjectivity on the part of the analysts. This bias is also present in the algorithms, because whoever chooses the algorithms and the parameters can influence (voluntarily or not) the results. This bias can occur even when the company uses an outside collaborator, a data mining expert.
Then, formatting and visualization done on the database are very important effect: in order to have convincing results, it is essential to discard superfluous data to keep only the relevant ones. Lastly, the quality of the analysis should not be neglected: only extensive data processing combined with real reflection makes it possible to use the information collected in an intelligent manner to determine ambitious, coherent and achievable objectives. In short, the data does not mean anything in itself. The action of the human who contributes to their exploitation is decisive.
