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Survival analysis for conversion rates

Description

PyData Amsterdam 2017

What percentage of your users will spend? Typically, analysts use the conversion rate to assess how successful a website is at converting trial users into paying ones. But is this calculation giving us results that are lower than reality? With a talk rich in examples, Tristan will show how Shopify reframes the traditional conversion questions in survival analysis terms.

Abstract What percentage of your users will spend? Typically, analysts use the conversion rate to assess how successful a website is at converting trial users into paying ones. But is this calculation giving us results that are lower than reality? With a talk rich in examples, Tristan will show how Shopify reframes the traditional conversion questions in survival analysis terms.

  • To get started we will see examples of how skewed conversion rates can be when analyzing conversions that are delayed.
  • Then, we will cover the basics of survival analysis (survival function, hazard function) and the kind of data it requires.
  • Using the lifelines python library, we will apply survival analysis to answer the limitations of conversion rates and gain insight about new merchants on Shopify.
  • In a second case study, we will code to extend our toolbox outside of lifelines with the analysis of multiple outcomes (competing risks).

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