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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. Introducing main concepts Applying our science and technology to a Canadian small business Mining on The Revenue Side - Rates

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analytics big data and the cloud edmonton april 23 2012
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.

o utline

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
yield management

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
data mining

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
data warehouse

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
data cube

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

profitability

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
kdd process cleans transform
KDD Process: Cleans & Transform

This fires @ 4:00 AM Everyday

revenue rental rates

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
revenue utilization based tiers

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
rate control algorithm

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
revenue guaranteed cycles

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
revenue busy cycle considerations

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
rate control algorithm ii

Build Availability Cube

Insert Cyclical Adjustments

Branch Rates

System

Wide

Every 10 Minutes

Walk-in Rates

Publish

Intranet

Known Dates

Rate Control Algorithm II
revenue result summary

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
next expense

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
expense insurance analysis

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
expense insurance

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
expense result summary

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