For an upcoming football campaign FanDuel are going to send
an email to existing FanDuel users letting them know headline tournaments and
new site features for the season. In addition there will be targeted offers to
certain players based on their transactional history.
As an analyst your job is to help define the segmentation
and then use the SQL database to generate lists of users. The data tables you
have at your disposal are below:
USER TABLE
– This is where we store information about the user’s account
FIELD |
DESCRIPTION |
user_id |
Unique identifier for users |
username |
Username of user on site |
email |
Email address of user |
deposit_count |
The total number of deposits the
player has made |
account_suspended |
A binary variable to show if the
account has been suspended |
registration_date |
Shows the date on which the user
initially registered on the site |
ENTRY TABLE
– This is where all the information about every submission (entry) into games
is stored
FIELD |
DESCRIPTION |
entry_id |
Unique identifier for each entry |
game_id |
Identifier for the game the entry
is valid for |
user_id |
Identifier for user who made the
entry |
entry_date |
The datetime value of when the
entry was made |
entry_fee |
The value of the entry fee into
the game (can be 0, 5, 10, 25, 50 or 100) |
winnings |
The value of the winnings from
the entry by finishing in a paying final position |
mobile_entry |
A binary variable to show whether
the entry was made on a mobile device |
GAME TABLE
– This is where all the specifics about games are stored
FIELD |
DESCRIPTION |
game_id |
Unique identifier for each game |
sport |
Indicator of which sport the game
is for (MLB, NBA, NFL, NHL) |
size |
The total size of the game from 2
players up to 1,000 players |
PAYMENTS TABLE
– This is where we store information about and deposits and withdrawals
FIELD |
DESCRIPTION |
payment_id |
Unique identifier for each
payment |
user_id |
Unique identifier for users |
payment_type |
Identifier for deposits and
withdrawals (values either ‘D’ or ‘W’) |
payment_date |
Shows the date when the payment
was processed |
amount |
The value of the transaction |
1)
Based upon the information you have in the
database what variables would you consider most when designing a segmented
offer for users in the new football season and why do you think that they are
important? What sort of groups would you like to create and how might you vary
the incentives/offer to each of those groups?
2)
The CRM Manager has an idea for a test, but
wants to know if there are enough users available to make the results
significant. There are a couple of filters to apply before he gets in to
further detail so asks you to work out the count of users who have made at
least one deposit and registered in the previous year (2013). How would you
do this with SQL code? Are there any other constraints you might consider
adding to give a more accurate estimate?
3)
The CRM Manager is happy with the base user size
so fills you in on the additional restrictions. He now wants to see how many
users have made at least one deposit, registered in 2013 and played NFL
in 2013. What would be the SQL code needed to answer this?
4)
Now we have the list size finalized it’s time to
generate the list of users to be sent the email. With the same constraints as
above you need to generate a list with the following fields, so that the CRM
Manager can start to build his custom segments:
i.
user_id
ii.
email
iii.
total entry fees for NFL in 2013
iv.
total entry fees for the other sports combined
in 2013
v.
the number of winning entries the user has ever
had
vi.
the percentage of entries on mobile across ALL
sports in 2013
vii.
the last paid
entry date of the user
viii.
the total net deposit value, i.e. deposits -
withdrawals
How would you create this list in SQL?
5)
The Marketing VP is concerned that there has
been a movement away from head-to-head (2 player) games over the last 3 years
for football. He believes it may be a reason for declining tenure so has
requested that you investigate. Specifically he wants to know in each year
between 2011 and 2013 what percentage of entries and entry fees were
head-to-head games in each user’s first 30 days after registration. Provide the
SQL code required to answer his questions.
6)
The marketing VP is pleased with what you pulled
from Question 4! They want to repeat what you’ve done but this time across
different sports and different years (not just NFL and 2013). They send you a
file called sport.csv with the below output:
sport |
year |
NFL |
2019 |
NHL |
2012 |
NBA |
2010 |
MLB |
2015 |
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