Problem Statement : Using Looker_Studio will aVisualize the Given :
A. Google Analytics:
Plot a chart showing the comparison of transactions, E-Commerce Conversion Rate and revenue in the month of November, 2022 with respect to the previous month i.e. October, 2022.
a. What is the highest transactions, E-Commerce Conversion Rate and revenue in the month of November, 2022 and on which dates?
b. Are there any common dates on which transactions, E-Commerce Conversion Rate and revenue were the same w.r.t. the previous month?
c. What is the average of transactions, E-Commerce Conversion Rate w.r.t. the previous month?
Plot a chart showing the comparison of transactions, E-Commerce Conversion Rate and revenue for the different channel groups.
a. Which channel group has the highest E-Commerce Conversion Rate in the month of November, 2022? b. What is the number of sessions in the month of July, 2022 from Affiliates? c. Has the revenue increased or decreased from Paid Search in the month of September 2022 w.r.t August 2022 and by how much percentage?
Plot a chart showing the revenue generation from different age groups of people.
a. From which age group is the highest revenue generated in the month of August, 2022 and on which date? b. From which age group is the lowest revenue generated in the year 2022? c. What is the percentage of revenue generated for the age group 18-24?
For different landing pages, Show the number of transactions, E-Commerce Conversion Rate and revenue with respect to the last period.
a. What is the total number of landing pages for the last quarter? b. Which landing page has the highest transactions in the year 2021?
Show the revenue generated for different cities of the USA. Also plot a chart showing the transactions and average screen time for the new users.
a. From which city, the highest revenue was generated in the first quarter of 2022? b. What is the number of new users in the last 14 days and what is the revenue generated by them?
B. Cohort Analytics:
Plot a chart showing the conversion rate per user for 30 days in any month. Plot a chart showing the grand total conversion rate for different user categories for 30 days in a month. Plot a chart showing the Bounce rates for different countries over a specific period of time. Also show different metrics such as total users, new users, conversion rate per user, time on screen, Average price, Order value etc. for different countries.
C. Funnel Analytics: Prepare a funnel overview with 5 parts - Acquisition, Activation, Retention, Revenue, Referral, Funnel Visualization. Add different charts and graphs showing the following metrics:
Acquisition:
a. The number of sessions and bounce rate for each channel. b. The number of visitors and returning visitors for each channel. c. Total users and session showing comparison with respect to the previous month.
Activation:
a. What is the Bounce rate, Pages/Session and Average Session Duration for all users? b. Number of Registrations, Engaged Users and its conversion rates.
Retention:
a. What is the number of returning users and its bounce rate? b. The number of transactions made for each session for returning users.
Revenue:
a. What is the total revenue, Total transactions, Revenue per user, E-Commerce Conversion Rate, and Product revenue per purchase for all users. b. Quantity bought for each product.
Referral:
a. What is the number of sessions and Bounce rate for the referral users? b. Number of sessions for each referral.
Funnel Visualization:
Add a Metric Funnel under Community visualisations in Data Studio showing the following processes in the funnel: Total Engaged Users - Users who completed the registrations - Users who added items to card - Users who entered Checkout - users who completed Payment.
APPROACH:
Developed a dashboard that utilized an analysis of user behavior to identify opportunities for enhancing the Google Merchandise Store website, aiming to increase sales and revenue.
More precisely to gain insights, utilized functionalities like Funnel Analytics and Cohort Analytics available in Google Analytics to gather valuable insights.
The findings derived from the analysis carried out will be applied to optimize the Google Merchandise Store website, improve the user experience, increase the conversion rates, and ultimately stimulate growth in sales and revenue.