Products list optimization project, intro

The next series of blog posts is dedicated to the UX research project I’ve accomplished recently. The described approach could be useful for e-commerce websites and online stores, especially those with large product catalog.

Motivation and Main idea.

Products in the catalog subcategories get unequal shares of user attention. Those in the top of the list are seen by almost all users. Bottom of the list gets multiple times less attention (twice as a minimum). Most users don’t scroll all the list throughout.

But as statistics tells us, often the products from the top of the list don’t attract users’ interest. They don’t click on these products, they don’t put them to the shopping cart. At the same time, bottom list products get much more interest.

So the main idea is to analyze ratio of each product’s visibility (attention) and users interest, and put most popular items to the top of the list in order to sell more.

Summary of this series.

First, we make some tunings in our Google Analytics data collection process. We should track the page scroll to measure each product’s visibility. Also we need to gather information about clicks on the links to the product’s page and ‘Buy’ buttons for each product.

Second, we connect to the Google Analytics API from statistical environment (Rstudio in our case), retrieve necessary information, make some exploratory data analysis and get the final report. This report gives us directions as to what permutations should be done in subcategories of our product catalog.

So next time, we will start off with GA tuning.