Yield Management as A Process Governed by Data Mining in the auto Industry

1. Analytics, Big Data and the Cloud Edmonton, April 23, 2012 Yield Management as A Process Governed by Data Mining in the auto Industry Author: Ayman Ammoura M.Sc.

2. Introducing main concepts • Applying our science and technology to a Canadian small business • Mining on The Revenue Side - Rates • Mining on The Expense Side – Insurance • Sharing success stories Outline

3. Yield management is the process of understanding, anticipating and influencing consumer behavior in order to maximize yield or profits (Wikipedia) • Understanding  Observation and analysis • Anticipate  Forecasting • Influencing  Management actions Yield Management

4. Data Mining is a step in the knowledge discovery process. (Osmar Z.) • Data mining is a process of extracting previously unknown, valid, and actionable information from large databases then using the information to make crucial business decisions (Cabena, et al, 1998) Data Mining

5. Data repository built to facilitate OLAP (OnLine Analytic Processing) not OLTP (Transaction). • Warehouse  Multidimensional, Subject-Oriented, data model  Data Cube • To support OLAP, a data warehouse is often implemented as a hierarchical N-Dimensional data cube. Data Warehouse

6. Fact Table Time Rental Days Vehicle Class Time Dimension Table Class Location Location Each slice it an n x m 2D Table Data Cube Usually you need SIC, Source, Sold Extras .. N-Dimesions

7. Data Cube Sliced

8. There are 2 items that define the financial well being of an organization. • Revenue (our example  Rental Days) • Expense (our example  Insurance) • In our case, we need to create a data repository with Fact tables “Rental Days” and “Insured Units” Profitability

9. KDD Process: Cleans & Transform This fires @ 4:00 AM Everyday

10. Daily @ 0600 Master Control

11. Canada Winter Games Revenue: Units on Rent

12. Revenue: Fine Detail

13. How and when to adjust. • Utilization Based rate adjustment • Not Competitive • Big missed opportunities (explained next) • To answer the When question we needed to get more insight into the data • Understanding the Cycle City Sold-out Revenue: Rental Rates

14. Create a system that would issue new booking rates based on utilization. • 0%- 50% +0% • 51% - 65% + 10% • 66% - 75% + 15 % etc … • This will be transparent to the agent and has been widely used for over a decade. Revenue: Utilization based Tiers

15. Using this model, we were able to increase revenue by 30% in the first cycle (May-September) Build Availability Cube Branch Rates System Wide Every 10 Minutes Walk-in Rates Publish Intranet Rate Control Algorithm

16. During busy season, booking are received 90 days in advance • Shoulder Season  as low as 6 days average Sold Out 90 days Revenue: Guaranteed Cycles

17. Using the utilization tiered rate adjustment process alone  50% of the business can be improved by at lease 20%  Because 50% booking is required to achieve the next tier • On Average, most bookings during busy cycle were entered 3 months in advance Revenue: Busy Cycle considerations

18. Build Availability Cube Insert Cyclical Adjustments Branch Rates System Wide Every 10 Minutes Walk-in Rates Publish Intranet Known Dates Rate Control Algorithm II

19. Phase I and Phase II were constructed one cycle apart • Complete project spanned 14 months Up $2.2 Million Up $1.3 Million Utilization + Cyclical and Localized Adjustments Utilization based Tiers Revenue: Result Summary

20. So far we talked about an example of how we applied simple Data Mining tools to achieve great results on the revenue side, helping a small business. • Next we will examine how we have effectively used analytics to impact profitability by reducing a major expense. Next  Expense

21. Next to depreciation, this is usually the second biggest expense in the auto industry. • Existing Scenario is that the business had to pay the insurance premium per unit ($m) on all used units in a calendar month. • Existing solution was: Identify units that were rented (n), and pay monthly ($mxn) • How to reduce this cost? Expense: Insurance Analysis

22. Visualization of the number of active days of every insured unit for a typical month Insurance: Activity Analysis

23. Examining the number of insured units against the number of units on rent Insured Vehicles Count Rented Vehicles Insurance and Utilization

24. As there are more units in the fleet than was required, the company insured way more than was required  Information that was implicit data • Time to renegotiate the insurance model! – Preferably without sharing your results with the broker  Expense: Insurance

25. Instead of paying on all units, we negotiated a policy that allows us to pay higher prorated premiums but on a daily basis. • Without the ability to transform the data into information, this effort was “unnecessary” and probably have not happened! • Recall our definition (Data mining is a process of extracting previously unknown, valid, and actionable information) Insurance cost decreased by $120,000 per year Expense: Result Summary

26. Love to answer any questions …. Thank You 