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Digital Marketing Analytics

创建日期：2024-04-26 11:29:21

所属分类：市场营销Marketing

标签： Marketing

Digital Marketing Analytics

Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: THEend8_

Individual and Group HW 1

Digital Marketing Analytics

20% of your final grade (Peer review policy in place)
You can consult freely (internet, Help, tutorials) but all work has to be within the group. You will
have to make a lot of design decisions yourself in the analysis. There is no one right answer for
many of the questions below, but you have to write a brief explanation of why you chose what you
chose to do.
See the Instructions for many helpful tips.
INDIVIDUAL
1. (10 points) Create a database in Postgresql and load the data files given to your database---
this may be more troublesome than it sounds because of the .csv format, and dates, so start
early. If you are having trouble loading with SQL, try the pandas routine, but do use SQL for
the queries.
Submission (in a pdf or a text file):
x a query (query only) to identify the top 5 customer locations by average spend.
x If you are using pandas the Python source code you used
x any auxiliary queries/code that you did to load data, clean up data
(read the description to find the field where this information lies; check the format or put it in
the right format; decide what to do with missing data; join tables appropriately to extract)
GROUP
(SRLQWV) AnalǇse ƚhe daƚa seƚ ƚo gain ͞insighƚs͟ inƚo ƚhe effecƚiǀeness of ƚhe ǀarioƵs direcƚ-
marketing channels---specifically catalog mailing vs. email. This is an open-ended HW so no ǭǭrighƚ͟
answer, but you are expected to do substantial work. The grade depends on both effort and scope
of your work as well as depth.
" (5 points) Raise two interesting data-analytical questions you can ask on the data, and study
and analyse the data to give answers to your questions. Sample question: which channel
has better response rates, catalog mailing or email?
# (10 points) Segment using the RFM dimensions (5 quantiles for each dimension). Estimate
response rates for each RFM cell. Make a decision on how many to mail based on an
estimate of ROI of the campaign (say it costs a normalized $1 per mailing and an average
profit of $30 per purchase).
(10 points)
B
Calculate the Average CLV of a customer. (Note that this is an e-commerce setting).
C
Management believes different types of customers have different CLVs. Come up with a
plan (1 page max) on how you would do such a project so the results are useful for the
business. (There is no need to implement it, but write a plan on how you would go about
doing it, based on the data you have in this case.)
I could have given you cleaned up data---but instead am giving you raw data plus instructions, so you
get practice in data wrangling and also the SQL skills you learned
Notes and instructions:
1. Please go through the data description for interpreting the data. It is all the information I
have also. For instance Location information is in SCF_Code, which gives the first three digits
of the zip code.
2.
.csv format loading into database tables is finicky and you may have trouble even loading the file into
a database. Try
(a) saving them as tab-delimited files (native format for postgresql) and then
(b) loading it after first creating tables (use the CREATE scripts available in pgAdmin after right-
clicking on the table name). Read Postgresql Help on loading
(c) Check you are loading dates properly
Options: COPY is fast and simple with only a few parameters, but is very finicky; another option
is pandas read_csv (say with sqlalchemy). The latter has as many options as a Mercedes luxury
car, so you will spend equal time with either one.
Other ways, such as reading line by line, or with R could turn out to be way too slow.
You can take either strategy (for the loading part), but do the queries using SQL as, if you take
some care in indexing and writing the queries, significantly faster than all other methods.
If you are using the COPY command, you will face the problem of creating the table. What I do
is take the header, copy into Excel and transpose it and then format it into a CREATE command
with the fields to create the table. Then use COPY.
If there are 100+ fields as in the Summary table (you only really need the Orders table for RFM,
bXW in caVe \oX haYe Wo«). One option is to create the table using pandas and import only a
line, and then use the COPY command if the data is too big and pandas turns out to be slow.
Now, once you have the table and columns set up, as I mentioned, csv files might be
troublesome to load. Convert to tab-delimited first to make your life easier.
Here is a sample loading script
Eg query to load in SQL (the file cleaned2.txt in the query is in tab-delimited format)
COPY
dataset9_cusorders(custid,ordernum,orderdate,linedollars,gift,recipnum)
FROM µF:\\classes\\data sets\\Data set 9\\DMEFcustomer orders
cOeaQed2.W[W¶ NULL µ µ
in pandas
from sqlalchemy import create_engine
import pandas as pd
fLOe=¶F:\\classes\\data sets\\marketing edge datasets\\Data set
9\\DMEFE[WUacWSXPPaU\V01.CSV¶
engine =
cUeaWe_eQJLQe(µSRVWJUeVTO://M\DBSeUYeU:PaVVZRUdTRM\DB@ORcaOKRVW/dLJLWaO
cOaVV¶)
df = pd.read_csv(file,nrows-=2 )
df.WR_VTO(µdaWaVeW9_VXPPaU\¶, eQJLQe)

YoX ma\ VWill haYe Wo ZoUk Wo geW iW going«

Further Notes and instructions:
1. Please try to use SQL on Postgresql at least for storage and to do joins or filters across the
four tables. Some parts you can probably do easier with pandas (say loading data), but I it
would be good to do some basic SQL for the main queries for practice.
Try to use the SQL aggregate and window functions rather than pandas for summarizing
dataʹ they are more low-level but more compact. Hint: Look into ntile.
On the other hand, if you are more comfortable with pandas, you are free to use pandas
instead.
2. There are a lot of files and info you don͛t need. Eg. Various fields DMEF Demo codes
reference. Please ignore them
3. Index and link your tables for faster queries and analysis.
4. Missing data is a problem---you have to deal with it
5. The question of Average Spend is subtle---Average over what, and what time-window?
Which location? Resolve these to the best of your judgement.

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