Customer Acquisition

Just like every other business, airlines are always looking for new customers. With the travel market becoming more and more competitive and saturated by the day, there is always a constant need to attract new customers. Customer segmentation models can be used to build predictive models that identify key characteristics of attractive customers.

Obviously, an airline will have no internal data available on customers they don’t already have on their books, so the analysis becomes a data mining exercise using publicly available input variables. Alines can then target these customers with a view to attracting those who have the traits that they see in their already valuable customers. The best external data to use would be population census data, linked to the internal customers by a location identifier (such as postcode or mesh block). It is acknowledged that in some jurisdictions robust and accurate census data may not be available so the model would be relying on whatever information the airline records on its customers from a demographic and lifestyle point of view.  

This approach becomes a classical data-mining problem, where a pool of independent variables are tested for the strength of association with the response variable. Once the relevant predictors are identified and the characteristics and traits are defined, marketing and acquisition campaigns can be targeted at the population towards these kinds of people. This would be something that looks to predict a metric derived from current/past customers. Such a metric could come from a segmentation model that identified the high value customers that are most attractive to the gaming company.

There are several approaches that can be used and once the target has been defined, this allows for a parametric equation to be derived. This equation attempts to predict the characteristics that distinguish the desirable customers from the rest. This model can only use publicly available information (although other airline information might be acceptable) as that is how a potential customer would be identified. Current information that the company would have on hand would be age, nationality, gender, and address. Where available, third party data should be looked at to further enhance the findings. This could be census data that gives an indication of further customer demographics and this enhances the ability to hone in on customer sweet spots. Data from our data broker partners can also be tapped.