Directions: Please review and answer the following questions, which have been assigned to you in the CompanyOne case. You will need to capture screenshots to complete these questions; if necessary, review these instructions on capturing screenshots.
Find the number of active users (1-day, 7-day, 14-day, and 28-day) for December 2018. Calculate the daily average number of active users for each of the four time periods. For example, the daily average for the 28-day period will be the number of active users reported by Google Analytics divided by 28. Based on your calculations, what conclusions can you derive? (Note: Active users refers to the number of users who visited the CompanyOne website within the last 1, 7, 14 or 28 days looking back from the last day of the period, which is December 31, 2018.)
The metrics in the report are relative to the last day in the date range you are using for the report. Thus, your date range is December 1 to December 31:
- 1-day active users—the number of unique users who initiated sessions on your site or app on December 31 (the last day of your date range).
- 7-day active users—the number of unique users who initiated sessions on your site or app from December 25 through December 31 (the last 7 days of your date range).
- 14-day active users—the number of unique users who initiated sessions on your site or app from December 18 through December 31 (the last 14 days of your date range).
- 28-day active users—the number of unique users who initiated sessions on your site or app from December 4 through December 31 (the entire 28 days of your date range).
Plot graphs of 1-day active users for the 31 days in December 2017 and in December 2018. Compare the number of active users for both periods from the two plots. What do you conclude about the change in marketing effectiveness, if any, from December 2017 to December 2018? Please provide a screenshot to support your analysis.
Plot a graph of the bounce rate for the 31 days in December 2018 and compare it with the same time period in December 2017. What do you conclude? Please provide screenshots to support your analysis..
If you focus on the demographics of younger users (18-24 and 25-34), what do you observe? Has the number of younger users as a proportion of total users changed from 2017 to 2018? (Note: January 1 and December 31 are the start and end dates for a whole year comparison.) How about changes in the proportions of older users during the same time period? Please provide
screenshots to support your answer.
CompanyOne’s objective was to attract a larger proportion of female visitors (displayed as a percentage in the pie chart) to the online store in 2018 as compared to 2017. Was that objective met? Please provide a screenshot to support your answer.
CompanyOne needs to analyze gender differences in more depth, especially in the new user segment. Did CompanyOne attract more or fewer new users in 2018 as compared with 2017, irrespective of gender? What about new male users? What about new female users? Please provide screenshots to support your answer.
As you have confirmed, CompanyOne was not successful in attracting a larger number of new female visitors to its website in 2018 as compared with the previous year. When parsing the category of new female users, by age group, was CompanyOne more successful in certain age group(s) of female users in 2018 as compared with 2017? Please provide a screenshot to support
CompanyOne wishes to target high-value users in future marketing campaigns. These are user groups with the highest e-commerce conversion rate or average order value. Which age group generated the highest revenue for CompanyOne in 2018 in dollars? How much was the revenue from this age group? Which age group generated the least revenue? Which age group had the highest average order value? Which age group had the highest e-commerce conversion rate? Based on these observations, which age group or groups should be the focus of CompanyOne’s marketing efforts during 2019? In other words, which age group is likely to provide the most bang for the buck? Provide a screenshot to support your answer.
CompanyOne wishes to understand its site visitors better in order to fine-tune its future marketing efforts. Understanding audience composition in terms of gender, age, and interests will allow CompanyOne to develop the right creative content and decide the media buys to make.
Google Analytics has the following ten default affinity categories:
- shoppers / value shoppers
- lifestyles and hobbies / business professionals
- sports and fitness / health and fitness buffs
- technology / technophiles
- banking and finance / avid investors
- travel / travel buffs
- travel / business travelers
- media and entertainment / movie lovers
- lifestyles and hobbies / art and theater aficionados
- media and entertainment / music lovers
Identify the top three affinity categories for CompanyOne among males and among females for 2018 in terms of the revenue from each affinity category. Please provide a screenshot to support your answer.
The two groups every online business like CompanyOne cares about are users who convert (purchase a product) and users who don’t. Understanding the users who convert (converters) will help CompanyOne refine the successful aspects of their marketing and show them where they can improve their efforts to
reach users who demonstrate untapped potential (non-converters). Developing insights into why certain users aren’t converting lets them address the weak spots in how they approach these users.
For the purpose of this analysis, CompanyOne wishes to focus on the back-to-school shopping season (July 15, 2018 to September 15, 2018). CompanyOne wishes to obtain statistics of users, sessions, page views, pages per session, average session duration, and bounce rate for these two segments (converters and non-converters). Provide screenshots to support your analysis.