Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
QlikView: Advanced Data Visualization

You're reading from   QlikView: Advanced Data Visualization Discover deeper insights with Qlikview by building your own rich analytical applications from scratch

Arrow left icon
Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789955996
Length 786 pages
Edition 1st Edition
Arrow right icon
Authors (4):
Arrow left icon
Barry Harmsen Barry Harmsen
Author Profile Icon Barry Harmsen
Barry Harmsen
Miguel  Angel Garcia Miguel Angel Garcia
Author Profile Icon Miguel Angel Garcia
Miguel Angel Garcia
Stephen Redmond Stephen Redmond
Author Profile Icon Stephen Redmond
Stephen Redmond
Karl Pover Karl Pover
Author Profile Icon Karl Pover
Karl Pover
Arrow right icon
View More author details
Toc

Table of Contents (25) Chapters Close

QlikView: Advanced Data Visualization
Contributors
Preface
1. Performance Tuning and Scalability FREE CHAPTER 2. QlikView Data Modeling 3. Best Practices for Loading Data 4. Advanced Expressions 5. Advanced Scripting 6. What's New in QlikView 12? 7. Styling Up 8. Building Dashboards 9. Advanced Data Transformation 10. Security 11. Data Visualization Strategy 12. Sales Perspective 13. Financial Perspective 14. Marketing Perspective 15. Working Capital Perspective 16. Operations Perspective 17. Human Resources 18. Fact Sheets 19. Balanced Scorecard 20. Troubleshooting Analysis 21. Mastering Qlik Sense Data Visualization Index

Customer churn


Customer churn is a measure of the company's tendency to lose customers. Our user story speaks of the need to detect at-risk customers and prevent them from becoming a lost customer.

Surely, there are many variables that we may use to predict customer churn. In this case we expect customers to consistently make a purchase every so many days, so we will use a variable called customer purchase frequency to detect those that we are at risk of losing.

We could calculate the average number of days between purchases and warn sales representatives when the number of days since a customer's last purchase exceeds that average.

However, a simple average may not always be an accurate measure of a customer's true purchasing behavior. If we assume that their purchase frequency is normally distributed then we use the t-test to determine within what range the average is likely to fall. Moreover, we prefer the t-test because it can be used for customers that have made less than thirty or so...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime