Instruction:
1 This exam consists of a Case Study, Data Set 1, Data Set 2, Data Set 3, Question 1, Question 2, and Question 3
2 Read the Case Study carefully, you may ask the lecturer to explain the case
3 Learn the Data Sets that will help you to investigate the problem in the Case Study
4 Answer the 3 questions in English. You may use Graphics, Charts, or any other statistical tools
5 The duration of the mid exam is 90 minutes
6 Submit the Excel file that contains your answers using the submission tools provided by the lecturer
Grading:
100-85 Deliver a complete and comprehensive analysis of the data set using descriptive statistics both numerics and graph
Provide reasonable root causes including the detail process how to find the cause. Formulate correct hypothesis.
Provide the predictive model with comprehensive argument to choose the model. Provide a detail steps by steps to
84-70 Deliver a complete but not comprehensive analysis of the data set using descriptive statistics both numerics and gra
Provide reasonable root causes without the detail process how to find the cause. Hypothesis is not well formulated
Provide the predictive model with comprehensive argument to choose the model. Steps by steps to implement the
69-55 Descriptive statistics is not complete and comprehendive. Incorrect usage of graphical statistics.
Root causes have no enough supporint arguments. Hypothesis is not well formulated.
Provide the predictive model but with no comprehensive argument to choose the model. Steps by steps to implem
54-40 Descriptive statistics is not complete and comprehendive. No graphical statistics.
Root causes have no enough supporint arguments. No hypothesis or hypothesis is not relevant with the causes.
No predictive model or wrong model. Steps by steps to implement the model is not detail. No additional data
estion 2, and Question 3
by the lecturer
stics both numerics and graphical.
rmulate correct hypothesis.
ovide a detail steps by steps to implement the model. Provide clear example of additional data with tools how to get the data
tatistics both numerics and graphical.
othesis is not well formulated.
eps by steps to implement the model is not detail. Provide clear example of additional data with tools how to get the data
odel. Steps by steps to implement the model is not detail. Provide clear example of additional data but no tools example on how to get the
t relevant with the causes.
etail. No additional data
w to get the data
o get the data
ols example on how to get the data
A retail company released an online service a year ago, in 2023. They plan to use the online service
use this online shoping service.
This simple online shop has several features, such as:
1. Profile is a feature where the user can edit their profile such as upload photo, change some pers
2. Product is where the user can see the list of the product, search, and filter the list to suit user pre
3. Product detail is the next page where the user see the detail of the product after they select a ce
4. Payment is the next feature when the user decides to buy the product
5. Help Center is one of the prime features in this service. The company needs to reduce the risk of
so that user may have clear guidance on how to use the application
Problem:
At the beginning, there were 10 customers registered or subscribed to this online service. However
subscription while otheres already canceled the subscription.
The company conduct investigation about this problem. They have the log data of the customer on
strategy how to fix this problem.
Customer
Customer ID DOB Gender Location Start Date End Date Status
C001 10/10/1990 male Surabaya 8/10/2023 12/15/2023 canceled
C002 11/10/1990 male Bandung 8/20/2023 10/27/2023 canceled
C003 12/10/2000 female Jakarta 9/5/2023 2/1/2024 canceled
C004 12/20/2002 male Jakarta 2/3/2024 3/13/2024 canceled
C005 5/30/2004 female Jakarta 9/11/2023 1/17/2024 canceled
C006 6/10/1998 male Bandung 12/25/2023 4/5/2024 canceled
C007 5/20/1994 male Jakarta 10/8/2023 11/11/2023 canceled
C008 5/13/2006 male Bekasi 12/14/2023 - active
C009 9/23/2001 female Surabaya 3/15/2024 4/24/2024 canceled
C010 4/20/2000 female Jakarta 12/10/2023 3/18/2024 canceled
Customer Log Data
Log ID Customer ID Date Login Logout
Log001 C001 8/10/2023 13:00 15:30
Log002 C001 8/10/2023 16:45 18:17
Log003 C001 8/14/2023 20:00 20:50
Log004 C001 11/10/2023 7:00 7:15
Log005 C002 8/20/2023 10:20 10:55
Log006 C002 9/15/2023 20:00 21:12
Log007 C003 9/10/2023 10:00 13:30
Log008 C003 1/29/2024 11:30 11:40
Log009 C004 2/3/2024 12:50 13:40
Log010 C005 9/11/2023 14:05 14:35
Log011 C005 12/19/2023 19:30 20:15
Log012 C005 1/16/2024 21:00 22:00
Log013 C006 12/25/2023 8:10 9:40
Log014 C006 1/12/2024 18:45 18:55
Log015 C006 3/20/2024 17:15 18:35
Log016 C007 10/8/2023 15:50 16:25
Log017 C007 11/11/2023 17:00 17:05
Log018 C008 12/14/2023 16:00 16:50
Log019 C008 1/14/2024 16:05 16:50
Log020 C008 2/13/2024 16:05 17:05
Log021 C008 4/10/2024 18:10 18:25
Log022 C009 3/15/2024 19:20 22:00
Log023 C009 3/25/2024 19:15 20:00
Log024 C010 12/11/2023 9:10 9:30
Log025 C010 2/18/2024 12:05 12:45
Customer Log Detail
Detail ID Log ID Feature ID
Det001 Log001 1
Det002 Log001 2
Det003 Log001 3
Det004 Log001 2
Det005 Log001 4
Det006 Log002 2
Det007 Log002 3
Det008 Log003 2
Det009 Log003 3
Det010 Log004 1
Det011 Log005 1
Det012 Log005 5
Det013 Log006 2
Det014 Log006 3
Det015 Log006 4
Det016 Log006 1
Det017 Log007 1
Det018 Log007 2
Det019 Log007 1
Det020 Log007 2
Det021 Log007 3
Det022 Log007 5
Det023 Log007 1
Det024 Log008 2
Det025 Log008 1
Det026 Log009 2
Det027 Log009 5
Det028 Log009 2
Det029 Log009 3
Det030 Log010 2
Det031 Log010 3
Det032 Log010 4
Det033 Log010 2
Det034 Log011 2
Det035 Log011 3
Det036 Log011 2
Det037 Log011 3
Det038 Log012 1
Det039 Log012 2
Det040 Log012 3
Det041 Log013 1
Det042 Log013 2
Det043 Log013 3
Det044 Log013 2
Det045 Log013 3
Det046 Log013 5
Det047 Log014 2
Det048 Log014 5
Det049 Log015 2
Det050 Log015 3
Det051 Log015 1
Det052 Log016 1
Det053 Log016 2
Det054 Log016 3
Det055 Log016 4
Det056 Log017 5
Det057 Log017 2
Det058 Log017 5
Det059 Log018 2
Det060 Log018 3
Det061 Log018 4
Det062 Log019 2
Det063 Log019 3
Det064 Log019 4
Det065 Log020 2
Det066 Log020 3
Det067 Log021 2
Det068 Log021 3
Det069 Log022 2
Det070 Log022 3
Det071 Log022 2
Det072 Log022 3
Det073 Log022 1
Det074 Log022 5
Det075 Log023 1
Det076 Log023 2
Det077 Log023 3
Det078 Log023 4
Det079 Log024 1
Det080 Log025 2
Det081 Log025 3
Descriptive Analytics
- Based on the given dataset, please use Descriptive Analytic to summarize the data
Answer:
Root Cause Analysis
- Use RCA tools to find the causes of the problem. Give arguments upon the selected causes.
- Create hypothesis based on the causes
Answer:
Predictive Analytics
- Suggest what is the best predictive model to find the best strategy to solve the problem
- Explain the steps with examples on how to implement the predictive model
- Suggest additional data that may be necessary to support the decission
- Create example of tools to get the additional data. If it is a questionaire, then what are the questions, and who is the sample
Answer:
estions, and who is the sample
