Low-cost airlines are here to break with the traditional schemes of commercial aviation and disrupt the market. Paying only for what you use, offering ultra-low-fares starting at 1 dollar, one-way trips, and, in the case of JetSMART, developing a system of interregional routes are just some examples of the game changer innovations we introduced when we started in 2017 in Chile, then in Argentina and this year in Peru.

Being a low-cost airline means delivering a high-quality service at affordable prices for people who had never been able to get on a plane before because of high fares. To deliver a flexible and high-standard service, it is essential to have the necessary technology.

Read More

In this post, I would like to invite you to continue our intuitive exploration of A/B testing, as seen in the previous post.

Resuming what we saw, we were able to prove through simulations and intuition that there was a relationship between Website Version and Signup since we were able to elaborate a test with a Statistical Power of 79% that allowed us to reject the hypothesis that states otherwise with 95% confidence. In other words, we proved that behavior as bias as ours was found randomly, only 1.6% of the time.

Even though we were satisfied with the results, we still need to prove with a defined statistical confidence level that there was a higher-performing version. In practice, we need to prove our hypothesis that, on average, we should expect version F would win over any other version.

Read More

Many of us have heard, read, or even performed an A/B Test before, which means we have conducted a statistical test at some point. Most of the time, we have worked with data from first or third-party sources and performed these tests with ease by either using tools ranging from Excel to Statistical Software and even more automated solutions such as Google Optimize.

If you are like me, you might be curious about how these types of tests work and how concepts such as Type I and Type II Error, Confidence Intervals, Effect Magnitude, Statistical Power, and others interact with each other.

In this post, I would like to invite you to take a different approach for one specific type of A/B test, which makes use of a particular statistic called Chi-Squared. In particular, I will try to explore and walk through this type of test by taking the great but long road of simulations, avoiding libraries and tables, hopefully managing to explore and build some of the intuition behind it.

Read More

Google’s Lift Measurement is a tool which offers a convenient, easy to set-up and almost real-time approach to measure the effect that our campaigns have on some of the most strategic marketing metrics, specifically, ones related to brand management.

Having said this, there are some critical missing pieces of information that we, as marketers, would typically want to have, as provided in most studies.

Read More

Case Study Description: To predict the trip duration of a New York taxi cab ride, we can build different types of features and evaluate them. We will start by describing what is a feature in this context; then we will develop some elementary features and add features using the software package featuretools. We will assess how these features perform in predicting trip duration.

Read More