How to measure traveler perceptions and expectations
Remember our report from Monday this week showing the correlation between temperatures in Palma de Mallorca and the behavior of travelers in social media? Well, we have some more insights on this today and its even more exciting.
We observed the exact opposite phenomenon happening in a very different destination: the city of London. Basically, what we did was, study the correlation between London’s temperature records for the month of May 2016 and the number of times that the word “Sun” or “Sunny” is being mentioned each day by travelers.
The result? By using Pearson coefficient, we found a 0.61 correlation which is a medium-high correlation.
Why is this important? Because we are finding exact opposite patterns for Palma and London and this is telling us something about traveler expectations and how to measure them using social data.
Basically, we found a correlation for the city of Palma de Mallorca when there was bad weather (our PCindex was decreasing along with temperatures because travelers were complaining about the weather). In London, there is no correlation between temperatures and weather complaints but there is a medium-high correlation between temperature records and the number of mentions of the word “sun” or “sunny”. As the above chart shows, when there is a spike in temperatures, there is also a spike in the number of times that these words are used.
The most plausible explanation for this is how different traveler’s expectations are for Palma de Mallorca and London when it comes to the weather: When people visit Palma in June, they are expecting good weather and warm temperatures, this is why they are more prone to complain when temperatures plummet or there is a rainy day. On the contrary, London’s visitors are neither expecting a sunny weather nor warm temperatures; this is why when they find unexpected warm and sunny days they tend to share it through their personal networks.
This is especially exciting for us, because it means that we are able to measure traveler perceptions and expectations real-time. It also gets us closer to understand traveler behavior and anticipate future pattern.
We hope that you enjoyed reading this report as much as we enjoy finding this pattern!
As always, feel free to get in touch with us if you want more information 🙂