Predicting the e-commerce churn

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Lviv Data Science Summer School

This project was completed during the Lviv Data Science Summer School 2017 (http://cs.ucu.edu.ua/en/summerschool).

Education

Predicting
The e-commerce churn
Vojtech Nedved | Frankle Mucharahy | Polina Haryacha | Oleksandr Yaroshenko | Marianna Petrova | Yarina Demkiv

Wait… Churn?!
Your churn rate is the amount of customers
or subscribers who cut ties with your service
or company during a given time period.
2

3
Data we have (or we don’t)
PURCHASE
HISTORY
CUSTOMER PRODUCTS

4
Data we have (or we don’t)
36
User database
Transactional database
Features

6
Sample of distribution of data
1%
11%
29%
14%
6%
2%
1%
1%
6%
17%
7%
3%
1%
1%
<18
18-24
25-34
35-44
45-54
55-64
65+
Users by Age Group & Gender
Female Male

27%
10%
48%
14%
26%
12%
43%
20%
19%
27%
14%39%
0
1
2
3
Money Spent
Number of customers
Number of orders
0 High spenders
1 Middle class
2 Revenue Generators
3 Expensives Addicts
Segments of our customers
7

Segments of our customers
8
0%
75%
0
100
200
300
400
0-25
25-50
50-75
75-100
100-125
125-150
150-175
175-200
200-225
225-250
250-275
275-300
300-325
325-350
350-375
375-400
400-425
425-450
450-475
475-500
500-525
525-550
550-575
575-600
600-625
Count Customers Customers Cumulative %
Defining the churn threshold for cluster XXX

9
Approach to building the model
Results

14
Mitigation of churn
People with only one transaction according to year
2010 2011 2012 2013 2014
200
400
600
Countofpeoplewithonlyonetransaction

15
Mitigation of churn
Without outliers
<18 18-24 25-34 35-44 45-54 55-64 65+
With outliers
<18 18-24 25-34 35-44 45-54 55-64 65+

16
Recommendation for future
improvement

Next steps
17
Resource
optimization
Competitor’s
influence
Know your
customer
#1
#2
#3

Vojtech Nedved | Frankle Mucharahy
Polina Haryacha | Oleksandr Yaroshenko
Marianna Petrova | Yarina Demkiv
Thank you for your attention!
CustID=1467
CustID=1468 CustID=1469
CustID=1470
CustID=1471
CustID=1472
18

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Ukrainian Catholic University Faculty of Applied Sciences Data Science Master Program January 21st Abstract. Human navigation in information spaces has increasing importance in ever-growing data sources we possess. Therefore, an efficient navigation strategy would give a huge benefit to the satisfaction of human information needs. Often, the search space can be understood as a network and navigation can be seen as a walk on this network. Previous studies have shown that despite not knowing the global network structure people tend to be efficient at finding what they need. This is usually explained by the fact that people possess some background knowledge. In this work, we explore an adapted version of the network consisting of Wikipedia pages and links between them as well as human trails on it. The goal of our research is to find a procedure to label articles that are similar to a given one. Among others, this would lay a foundation for a recommender system for Wikipedia editors, which will suggest links from the given page to the related articles. Our work is, therefore, providing a basement for enhancing the Wikipedia navigation process making it more user-friendly.

Master defence 2020 - Maksym Opirskyi -Topological Approach to Wikipedia Arti...

Lviv Data Science Summer School

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