Project presentation about CLTV Prediction that we learned within the scope of Data Scientist Training Program organized by Data Science School

1. CUSTOMER LIFETIME VALUE
PREDICTION & SEGMENTATION

2. INTRODUCTION
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3. FIRST OF ALL, WHAT IS CUSTOMER LIFETIME VALUE?
It is the monetary value that a customer will bring
to the company during its relationship with a
company.
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4. THEN, WHAT IS CUSTOMER LIFETIME VALUE PREDICTION?
It is used for time-projected and probabilistic
customer lifetime value estimation.
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5. 01
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UNDERSTAND THE WORK
Sharing of the problem
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UNDERSTAND THE DATA
Basic information about
the dataset
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CLTV PREDICTION
Making predictions and
interpreting the results
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6. UNDERSTAND THE WORK
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7. PROBLEM
An e-commerce
company wants to set a
roadmap for its sales
and marketing activities.
In order for the company
to make a medium-long-
term plan, it is necessary
to estimate the potential
value that existing
customers will provide
to the company in the
future.
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8. PROBLEM
With this potential value, the company will decide what type
of campaign to which customer segment and how they will
act in the field within a certain period of time.
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9. PROBLEM
In this study, using the 2010-2011 sales data, it is expected
that the customers' return to the brand in a certain time,
segmentation according to this return and action decisions
are taken according to the segments with the CLTV Prediction
method.
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10. UNDERSTAND THE DATA
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11. THE STORY OF DATASET
In this section,
the story of the
dataset is
discussed.
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12. THE STORY OF DATASET
The dataset includes the sales of a UK-based online store
between 01/12/2009 - 09/12/2011.
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13. THE STORY OF DATASET
The product catalog of this company includes souvenirs. It
can also be considered as promotional products. There is also
information that most of its customers are wholesalers.
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14. RECOGNIZING VARIABLES
In this section,
basic
information
about the
variables is
explained.
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15. RECOGNIZING VARIABLES
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VARIABLE NAME DESCRIPTION
Invoice Invoice Number
StockCode Product Code
Description Product Name
Quantity Number of Product
InvoiceDate Billing Date
Price Price of the product in pounds sterling
Customer ID Number of Customer
Country Country name of the customer

16. RECOGNIZING VARIABLES
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VARIABLE NAME VARIABLE TYPE
Invoice Categoric
StockCode Categoric
Description Categoric
Quantity Numeric
InvoiceDate Datetime
Price Numeric
Customer ID Categoric
Country Categoric

17. RECOGNIZING VARIABLES
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VARIABLE NAME MISSING NUMBER OF
OBSERVATIONS
Invoice 0
StockCode 0
Description 1454
Quantity 0
InvoiceDate 0
Price 0
Customer ID 135080
Country 0

18. DISCOVERY DATA ANALYSIS
In this section,
the data set is
examined in
more detail.
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19. FIRST LOOK AT THE DATA
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20. 3.783.295
Total number of products sold for UK.
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21. 6.609.119£
That’s a lot of money
3921 users
And a lot of users!
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22. PREPARING THE DATASET FOR ESTIMATION
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23. COLUMNS
◉ RECENCY:
TIME BETWEEN
CUSTOMER’S
FIRST PURCHASE
AND LAST
PURCHASE
◉ T:
TIME FROM
CUSTOMER’S FIRST
PURCHASE TO DATE
(CUSTOMER'S AGE)
◉ FREQUENCY:
CUSTOMER'S TOTAL
PURCHASES
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◉ MONETARY:
TOTAL MONEY
EARNED BY THE
BRAND FROM THE
CUSTOMER

24. PROCESSES APPLIED TO THE DATASET TO FIND EXPECTED
PURCHASE AND EXPECTED AVERAGE VALUE
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We have calculated the
average earnings per
purchase.
We have expressed the
recency value on a
weekly basis.
We have filtered the Frequency
value for customers who have
made a purchase at least twice.
We have deleted the
observations with a
Monetary value of 0
from the data set.
We have expressed the
T value on a weekly
basis.

25. NOW, WE ARE HERE!
BG/NBD MODEL
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26. BG/NBD MODEL
After training the BG/NBD model
with Frequency, Recency and
Tenure metrics, 6-month expected
purchases were calculated.
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27. NOW, WE ARE HERE!
BG/NBD MODEL GAMMA GAMMA MODEL
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28. GAMMA GAMMA MODEL
After training the Gamma Gamma
model with Frequency, Monetary
metrics, 6-month expected average
profit were calculated.
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29. AND, WE ARE IN THE FINAL
PART!
BG/NBD MODEL GAMMA GAMMA MODEL CLTV PREDICTION
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30. CLTV PREDICTION
We estimated the 6-month CLV value based on the Gamma Gamma and BG/NBD models.
Here are the 10 customers with the highest CLV:
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31. SEGMENTATION
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32. CLV VALUES WITH SEGMENTS
Customers are
segmented into A, B, C
and D segments based
on their CLV values.
Action decisions can be
taken according to these
segments.
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33. THANKS!
Any questions?
You can find me at
baharzerenturk@gmail.com
linkedin.com/in/baharzerenturk1
BAHAR ZERENTÜRK
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