Google Merchandise Store

This exercise has as an overall scope the usage and analysis of data derived from Google Analytics, in order to gain and formulate insights related to UX/UI and improve the online store’s revenue.

Insight

Over one year period, the most popular method of accessing Google Merchandise Store, is through organic search, 48.7% of all users directly entering one of the keywords in a search engine. The following most popular methods are direct access 17.5 %, social channels 14.1 % and referral sources with 11.2%. However, over one year period, only an average of 0.50 % of the users using organic search, actually completed a purchase. The highest rate of completed purchases coming from referral sources with an average of 2.00% from this method of access. We suggest focusing the advertisement of the online store mainly on referral sources against social channels that have a low purchasing rate. In addition, we suggest strengthening the existing referral sources as also adding new sources and testing them against access/purchasing rate.

This analysis was based on comparing most commonly used channels over 2017 & 2018 and the percentage of this users that actually completed a purchase, encompassing a total of 1,428,378 users.

Process

I have decided to start by browsing through the online store, without looking at any piece of information from the data. I was initially unsure what aspects to focus on as a few came into my mind. How many customers are female and how many are males? In which part of the world the customers live? Which part of the web shop is mostly accessed by the users?

After careful navigation and understanding the overall flow of the shop, I have decided that it will be most interesting to analyse data related to accessing the online shop. From where do the customers access it? Organic search, paid ads, social channels? In this way, I have worked with the data generated in Acquisition, Overview, selecting “All goals” for Conversion and 2 different time periods: 01.01.17 to 01.01.18 and 01.01.18 to 01.01.19.

I started to look at all the methods a customer accessed the website and I have easily identified most popular. I have decided to add a filter “Made a purchase” in order to identify the rate of customers only accessing the shop against the rate of customers actually completing a purchase.

Initially, I decided to make this analysis over the period of one year, but when adding the mentioned filter, I could access data only for 3 months span. Therefore, I have started to break down the year in 3 months periods and made an average of customers completing a purchase. In this way, it was rather simple to draw on further conclusions.