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

» 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 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.

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

» 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”

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» 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.

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

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

» 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

Build Availability Cube Every 10 Minutes Branch Rates Publish Intranet Walk-in Rates System Wide Insert Cyclical Adjustments Known Dates

Up $1.3 Million Utilization based Tiers Up $2.2 Million Utilization + Cyclical and Localized Adjustments » Phase I and Phase II were constructed one cycle apart » Complete project spanned 14 months

» 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 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

» Examining the number of insured units against the number of units on rent Insured Vehicles Rented Vehicles Count

» 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 ….