Travelling purchase anticipation

Challenge & Selection

The main idea is to find out when to launch special offers to travellers in order to increase the assertability purchase anticipation each destination has. Therefore, my primary tool was Learning Launch, which is useful for finding out causality based on data. Once obtained, we would be able to identify where to focus according to the moment the offer must be made to optimize advertising sendings like emails or push notifications.

Application

In order to achieve this objective, I got in contact with Despegar.com, one of the most important travel agencies of Latin America, and asked them for data about purchasing anticipation of customers, as a whole.

The second step was to analyze this information, and identify the main destinations chosen by customers from Argentina, Brazil, Chile, Peru, and Colombia (each one separately), which varies between these countries.

Then, after identifying these destinations, we check out the average purchase anticipation of customers for the different main destinations, filtering by country, in order to decide how much time in advance we shall send emails or mobile notification to the customers.

Insight

This experiment helped us think about a different variation for offering travel offers to our customers. We found out that people (in average) buy plane tickets with at least 60 days in 80% of the cases, another 35% buy with at least 100 days of anticipation, and only 5% decide to buy a plane ticket with less than 30 days of anticipation. This lead us to thinking about new different approaches like origin/destination, age, nationality, etcetera.

Approach

Next time, it would be better to analyze these variables taking into account the new parameters we came into contact with mentioned above. This way, we will be able to identify and segment in a better way our offer sendings and negotiations with customers.


Travelling purchase anticipation Challenge & Selection The main idea is to find out when to launch special offers to travellers in order to increase the assertability purchase anticipation each destination has . Therefore , my primary tool was Learning Launch , which is useful for finding out causality based on data . Once obtained , we would be able to identify where to focus according to the moment the offer must be made to optimize advertising sendings like emails or push notifications . Application In order to achieve this objective , I got in contact with Despegar.com , one of the most important travel agencies of Latin America , and asked them for data about purchasing anticipation of customers , as a whole . The second step was to analyze this information , and identify the main destinations chosen by customers from Argentina , Brazil , Chile , Peru , and Colombia ( each one separately ) , which varies between these countries . Then , after identifying these destinations , we check out the average purchase anticipation of customers for the different main destinations , filtering by country , in order to decide how much time in advance we shall send emails or mobile notification to the customers . Insight This experiment helped us think about a different variation for offering travel offers to our customers . We found out that people ( in average ) buy plane tickets with at least 60 days in 80 % of the cases , another 35 % buy with at least 100 days of anticipation , and only 5 % decide to buy a plane ticket with less than 30 days of anticipation . This lead us to thinking about new different approaches like origin / destination , age , nationality , etcetera . Approach Next time , it would be better to analyze these variables taking into account the new parameters we came into contact with mentioned above . This way , we will be able to identify and segment in a better way our offer sendings and negotiations with customers .