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Analytics, Big Data and the Cloud Edmonton, April 23, 2012

» 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

» 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

Âť Data Mining is a step in the knowledge discovery process. (Osmar Z.) Âť 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 repository built to facilitate OLAP (OnLine Analytic Processing) not OLTP (Transaction). » Warehouse  Multidimensional, SubjectOriented, data model  Data Cube » To support OLAP, a data warehouse is often implemented as a hierarchical N-Dimensional data cube.

Fact Table Time

Vehicle Class

Rental Days


Dimension Table Class Location


Each slice it an n x m 2D Table

Usually you need SIC, Source, Sold Extras .. N-Dimesions

» 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”

This fires @ 4:00 AM Everyday

Daily @ 0600

Canada Winter Games

» 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

» 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.

Every 10 Minutes

Build Availability Cube

System Wide

Publish Intranet

Branch Rates

Walk-in Rates

Âť Using this model, we were able to increase revenue by 30% in the first cycle (MaySeptember)

Sold Out

90 days

» During busy season, booking are received 90 days in advance » Shoulder Season  as low as 6 days average

» 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

Every 10 Minutes

Build Availability Cube

Insert Cyclical Adjustments

System Wide

Publish Intranet Known Dates

Branch Rates

Walk-in Rates

Up $2.2 Million Up $1.3 Million

Utilization based Tiers

Utilization + Cyclical and Localized Adjustments

Âť Phase I and Phase II were constructed one cycle apart Âť Complete project spanned 14 months

Âť 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. Âť Next we will examine how we have effectively used analytics to impact profitability by reducing a major expense.

» 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?

Visualization of the number of active days of every insured unit for a typical month


Insured Vehicles

Rented Vehicles

Âť Examining the number of insured units against the number of units on rent

» 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 

Insurance cost decreased by $120,000 per year

» 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)

» Love to answer any questions ….

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

by Ayman Ammoura

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