Conversion Funnel Analysis

I had an opportunity to work on some data to study the efficacy of a conversion funnel. It was a two-step ETL process beginning with a CSV file. I imported the file into Pandas, modified the data and exported it into a SQL database for visualization. I did not reduce the dimension too much at this stage, because I did not want to lose anything that might be important later.

Total Funnel

The first step after processing the data is to look at how users are progressing through the funnel.  In this case, we are looking at overall funnel performance as well as deciphering how search plays a role in the conversion process.

The Funnel process is as follows:

  1. Home Page
  2. Store Ordering Page
  3. Search
  4. Checkout Page
  5. Order Success

Funnel by Platform

After looking at the big picture, I started to drill down along key dimensions starting with the platforms used by the clients.

The Android users exhibit a distinct behavior pattern while iOS and Web users are more similar. The behavior differences could arise from the user themselves, or from platform constraints such as app design, or unique platform features.

Funnel Retention

The visualization is nice, but sometimes the actual numbers can be useful to understanding what is happening.

Web based users are more likely to use the search function and are also the most likely to convert to a customer. iOS users are least likely to convert and also have the largest fall off rate from the home page. The search function may be most accessible/usable from via the web platform, although it is not clear yet if the Android user is affected by platform features or if the user is distinct. 

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