Company XYZ is a worldwide e-commerce site with localized versions of the site.
A data scientist at XYZ noticed that Spain-based users have a much higher conversion rate than any other Spanish-speaking country. She therefore went and talked to the international team in charge of Spain And LatAm to see if they had any ideas about why that was happening.
Spain and LatAm country manager suggested that one reason could be translation. All Spanish- speaking countries had the same translation of the site which was written by a Spaniard. They agreed to try a test where each country would have its one translation written by a local. That is, Argentinian users would see a translation written by an Argentinian, Mexican users by a Mexican and so on. Obviously, nothing would change for users from Spain.
After they run the test however, they are really surprised cause the test is negative. I.e., it appears that the non-localized translation was doing better!
You are asked to:
- Confirm that the test is actually negative. That is, it appears that the old version of the site with just one translation across Spain and LatAm performs better
- Explain why that might be happening. Are the localized translations really worse?
- If you identified what was wrong, design an algorithm that would return FALSE if the same problem is happening in the future and TRUE if everything is good and the results can be trusted.
Please see the Jupyter Notebook on my repository or
