The document discusses applying the 80/20 rule to optimize a loyalty miles program. It describes a case study of an airline using data from its 15 million customer loyalty program to forecast customer tier levels in real-time and optimize resource allocation. The airline was able to improve its linear forecasts of customer tier levels from 42% to 47% accuracy by using various machine learning techniques like generalized boosting machines and generalized linear models on clustered customer data, with a focus on the 80/20 rule to optimize non-value adding activities and resources. It concludes that opportunities exist in applying the 80/20 rule to identify and reduce waste, and that properly defining the problem question is key for success with big data projects.

1. THE 80/20 RULE IN BIGDATA
BY JOSE BERENGUERES & DMITRY EFIMOV
Smart Data Summit Dubai | 25 - 26 May 2015
How to optimise your Loyalty program
from a non-value-added-activity point of
view and other stuff
case based on: Airline new customer tier level forecasting for real-time resource allocation of a miles program http://www.journalofbigdata.com/content/1/1/3

4. BACKGROUND: ROBOTICS

5. BACKGROUND: DESIGN THINKING

6. BACKGROUND: BIOINSPIRED MANUFACTURING

10. Loyalty miles program
(15,000,000 rows)
demographics, miles, flights

11. ?

12. Linear Forecasting (42%)
80/20

13. Today ?

14. Today
?

15. Rank, Resource optimisation

16. Testing the limits: 2 weeks

17. ordered by miles
max to min.

18. HOW IT WAS DONE…
1.Time Shift CRM events to present*
2.Feature Extraction (Cluster
Dummy Method)
3.Generalized Boosting Machine
(Tree Search)
4.Generalized Linear Model
(Regression based)
5.Blending with grid search (+5%)
*the computer understand dates but it understands better them in terms of “how many days ago”

19. Conclusion
Software Lock-ins
Asking the right question is
the hard part of big data
Huge opportunities in
80/20: Waste optimisation