CASE STUDY

Context: Each plan in BrowserX comes with a number of user seats (1, 2, 5 or 10). Once a customer purchases a plan he can add users up to a maximum of the number of user seats he purchases The data section of this sheet has data about around 1200 such customers who bought plans between January and March. The description of the various fields is as follows

Customer ID: ID assigned to the customer on purchasing the plan.

Customer Email: Email ID using which customer bought the plan. Email ID can be - gmail / yahoo / company ID (all company ID represented as xyz.com).

Purchase month: Month in which plan is purchased. Only 3 months here: January, February or March.

Number of user seats bought: Customer can buy either 1, 2, 5 or 10 user seats in a plan. User seats represent maximum number of users that can be added in a plan.

Number of users added: Number of users added to the plan.

Total number of desktop sessions: This number represents the desktop sessions run by all users added in a plan. Every test on a desktop browser is counted as a desktop session.

Total number of mobile sessions: This number represents the mobile sessions run by all users added in a plan. Every test on a mobile browser is counted as a mobile session.

Customer Region: Continent to which the customer belongs.

Total number of logins: Sum of login counts by all users added in a plan.

Month churned: Month in which customer canceled his plan. None of the users added in the plan can access the product anymore.

Month repurchased: Month in which customer repurchased his cancelled plan again.

Customer churn is one of the key metrics for any SaaS company. PF data useful to analyse churn in BrowserX in the Sheet Named "Question-1 Data" and answer the questions that follow. Please read the descriptions in this sheet delieberately.

a. Based on the data above, the churn in BrowserX is 29.9%. But this is of limited use. Can you calculate churn in a way that provides better insight. What is that insight?

b. Identify parameters that may be causing churn from the above dataset and explain why.

c. What is your inference about repurchases using this dataset?

d. Provide at least 5 suggestions to reduce customer churn

e. Provide at least 5 suggestions to increase repurchase