Waze Churn Prediction Data Analytics.pdf

Analysis of waze based on data

1. WAZE
REPORT
AZZARYA, AMANSYAH, DZAKIAH, HAZMAN
GROUP DGROUP D

2. COMPANY BACKGROUND
DEFINE PROBLEMS
03
04
DATA UNDERSTANDING
05
DATA CLEANING
06
ANALYSIS
07
TABLE OF
CONTENTS
PAGE 02
CONCLUSION
19
RECOMMENDATION
20

3. COMPANY BACKGROUND
PAGE 03

4. DEFINE
PROBLEMS
PAGE 04
WHAT ARE THE
FACTORS THAT
AFFECT USERS
CHURNED?
WHICH USER
SEGMENTATION
ARE THE MOST
LOYAL?
WHICH USER
SEGMENTATION
ARE THE MOST
CHURNED?

5. UNDERSTANDING
DATA
CHURNED & RETAINED
ACTIVITY & DRIVING DAYS
FAVORITE DESTINATIONS
DISTANCE
DEVICE
PAGE 05
SESSIONS

6. CLEANING & PREPROCESSING
DATA
PAGE 06

7. RETAINED
CHURNED
VS.
User Churned
User Retained
PAGE 07

9. number of times Waze is opened in a month per user.
SESSIONS
PAGE 09

10. ACTIVITY DAYS
RETAINED USERS
DRIVING DAYS
PAGE 10

11. CORRELATION
PAGE 11
correlation between activity days and driving days

12. CORRELATION
BETWEEN SESSIONS
& DRIVES
PAGE 12

13. DEVICES
TOTAL USER BASED ON DEVICE
PAGE 13

14. PAGE 14
PER DEVICE
RETAINED RATES

15. TOTAL VISIT
SINCE ON BOARDING
PAGE 15

16. PAGE 16

17. BY SEGMENTATION
TOTAL USER
PAGE 17

18. PAGE 18
TOTAL USER OF WAZE
RETAINED BY
SEGMENTATION
TOTAL USER OF WAZE
CHURNED BY
SEGMENTATION

19. CONCLUSION
Factors that affects user churn:
Total Favorites Navigation
Total Visits since onboarding
Total Activity days and Driving Days
Factors that not affects:
Devices
PAGE 19
User Segmentation is divided into 4 groups:
Low
Medium
Premium
Royal

20. RECOMMENDATION
NEW FEATURES
which allows users to explore new places or
areas so that they are interested in continuing to
use the Waze application.
PAGE 20

21. THANK YOU
GROUP D