辅导 data编程、讲解 SQL设计编程

Background:
Nations Info Corp is an online subscription provider of real estate and credit monitoring services.
Our customers pay a monthly membership fee, after a usually $1 trial for several days. One of
the challenges of a subscription business is to forecast the subscription revenue we collect from
each customer (also known as LTV or CLV, lifetime value). On one hand, it's necessary to know
the LTV because we need that value to know if the business is profitable. For instance, if the
LTV is $100 but we are paying $120 to acquire each customer, that is clearly not a profitable
business to pursue. On the other hand, it is very difficult to know what the LTV is when you first
acquire the customer, since it may take many months for the revenue to come in, as we charge
the customer each month they remain a member subscribed to our product.
Data:
We are using a cohort approach for data analysis. For this exercise, we will define the cohort as
the group of customers that sign up in a given month (e.g. January 2020).
We collect cohort data from 2020 to 2022 for 2 different verticals of business (rto: rent-to-own
and credco: credit monitor). We wanted to use various short term metrics (i.e. how they
performed within 1 month from signup) to predict long term LTV. Data is provided in the
“historical data.csv” file. Here is the data dictionary:
Numbers #:
M0#: number of signups we get. Serve as the denominator of all following metrics.
Dollar Amounts $:
LTV 0-15: average money we collect from customers in 15 days from signup.
LTV 0-30: average money we collect from customers in 30 days from signup.
LTV 0-360: average money we collect from customers in 360 days from signup.
Percentages %:
C0%: cancels on the same day of signup.
C1%: cancels during the trial period, before the first monthly bill.
D0%: failed to pay for signup due to card declines.
D1%: succeeded to stay the trial period but failed to pay for the first monthly bill due to card
declines.
M1%: succeeded to stay the trial period and pay for the first monthly bill.
MOBILE%: signups using mobile device.
PREPAID%: signups using prepaid card.
LOGIN RATE%: login to account after initial singup
SEARCH RATE%: search for properties after initial signup (not available for credit business)
PDP VIEW RATE%: view the property details after initial signup (not available for credit
business)Task 1:
Our team is tasked with forecasting the LTV 0-360 using the given metrics and any external
data. Marketing and product teams are interested in how data analysis and predictive modeling
could help with their business decisions.
Please compile your Python / R codes and results in a .html file. Also feel free to use any
business intelligence tools to present insights.
Task 2:
We are constantly testing new features on the sites and want to assess performance of the
changes and optimize profitability of the overall business. We tested different price points of
subscription fees on “rto” business recently, where our old version (Variant A) was put head to
head in a test against the new version (Variant B). The visitor traffic is supposed to split evenly
between A and B.
Analyze the test results and present findings, giving a recommendation about what we
should do for the traffic that is being tested.
Variant A: $49 monthly subscription fee after 7 days trial.
Variant B: $39 monthly subscription fee after 7 days trial.
Data is provided in the “test result.csv” file. Here is the dictionary for additional metrics than the
historical dataset:
VISITORS#: number of unique people who visit our website, before signup.
CPA$: cost we pay to partners on each signup.
Task 3 (SQL Question):
Table1: “orders” - the information of each order being placed
Column Name Type
order_id number
order_status varchar
signup_type varchar
order_datetime timestamp
jluvr varchar
Table 2: “activities” - the users activities on our website, whether before or after the orderColumn Name Type
id number
action_type varchar
user_data varchar
created_at timestamp
jluvr varchar
Prompt:
“jluvr” is a key to define each individual user visiting our website, and can be used to link
“orders” and “activities” tables. Note jluvr is not unique in either orders or activities table (i.e.
same user can place multiple orders and can have multiple activities).
We wanted to find the last activity of users before each order being placed. The result will
show each unique order, and the matched activity if it exists (if not exists, shows null and keeps
the order info). Feel free to state proper assumptions if any are not clarified above.
Please submit the SQL codes in a plain text file / doc.
Sample Output:
order_id jluvr order_dateti
me
created_at action_type user_data
1001 abc-123-def 2023-12-01
08:00:00
2023-12-01
07:58:00
form_submit rent
1002 abc-123-def 2023-12-01
09:00:00
2023-12-01
08:02:00
form_submit own
1003 abc-123-efg 2023-12-01
10:00:00
2023-12-01
09:55:00
form_view own
1004 abc-123-fgh 2023-12-01
10:01:00
(null) (null) (null)
… … … … … …