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Through the Looking Glasses: A Warby Parker Funnel Analysis with SQL

Introduction:

Warby Parker has transformed the eyewear shopping experience, making it enjoyable and exciting. The process starts with a 'Frames Quiz' to identify preferences, followed by personalized frame recommendations. Users then receive selected frames for a 5-day at-home trial, and if they choose to proceed, Warby Parker fulfills their order within a week.

Data Overview:

The dataset focuses on users who completed the 'Frames Quiz,' received at-home trial glasses, and made a purchase. This dataset aims to uncover insights into the effectiveness and user satisfaction with Warby Parker's personalized eyewear selection and trial system.

Question posed by this data:

  • What percentage of users who start the 'Frames Quiz' complete it?

This query will fetch all rows and columns from the 'survey' table, providing a comprehensive view of the data captured in the survey. Each row represents a survey entry, and each column contains specific information related to the survey responses or any other relevant data stored in the 'survey' table.

Now lets find what the distinct questions were from the survey -

With these results, we can answer question one,

What percentage of users who start the 'Frames Quiz' complete it?

We can see from this result that the answer is 54% of people who start the survey complete it.

Funnel Analysis:

The Warby Parker Funnel consists of three stages: 'Take the Style Quiz,' 'Home Try-On,' and 'Purchase the Perfect Pair of Glasses.' To enhance the Home Try-On stage, an A/B Test was conducted. Users were split into two groups - Group A received 3 pairs, and Group B received 5 pairs. The analysis delves into data from 'quiz,' 'home_try_on,' and 'purchase' tables.

Here is a preview of the data tables -

Unraveling the A/B Test Impact: As we navigate through these metrics, potential correlations emerge. Questions like the influence of the number of pairs on purchase likelihood and the impact of Home Try-On engagement on conversion rates will guide our analysis.

Conclusion: By integrating these data-driven insights into our narrative, we aim to construct a compelling story that not only outlines the user journey but also uncovers the potential impact of the A/B Test on user behavior and purchase decisions.