Michael N. Ferrara, Jr.Michael N. Ferrara, Jr.Sr. VP Field OperationsSr. VP Field OperationsFebruary 17, 2009February 17, 2009

Predictive Analytics for Fleet Safety

Over fifty years of combined experience in the transportation industry.

Industry leader in predictive modeling for transportation safety.

Very broad client base.

Intelligent Intervention Systems.

Minimize Effort, Maximize Results

FleetRisk Advisors

A leveled approach

Calibration, Planning and Customizing are the keysCalibration, Planning and Customizing are the keys

What is it…

Innovative and sophisticated use of statistical and quantitative data to build predictive models that drive human decisions, and fully automated decisions, to predict future events and dramatically improve performance.

Predictive Analytics

Some perspective…..

In business, as in baseball, the question In business, as in baseball, the question isn’t whether or not you’ll jump into isn’t whether or not you’ll jump into analytics; the question is when! Do you analytics; the question is when! Do you want to ride the analytics horse to want to ride the analytics horse to success…..or follow it with a shovel?success…..or follow it with a shovel?

Rob Neyer, ESPNRob Neyer, ESPN

Analytics will be the gold rush of the next Analytics will be the gold rush of the next several years, and many companies will be several years, and many companies will be assiduously panning their data for assiduously panning their data for treasure.treasure.

Michael Treacy, cofounder, GEN3 PartnersMichael Treacy, cofounder, GEN3 Partners

Every 30-90 days you knew which drivers were most likely to have an event, or type of event, with a 65-85% accuracy rate before they happen?

What if I told you that the driver does not have to go online, fill out any forms, take any tests or submit any personal data that would violate any company policy or privacy regulations?

What if you knew whether it was a personal, professional or skill issue that was impacting the driver.

What if there was an intelligent intervention system that could effectively manage the issue?

What if……

Modeling PlatformModeling Platform

Analytics Engine

Utilize your data for a 2nd generation ROI!

HR

OBC

MVRTelematics

Maintenance

Claims Data

Vehicle Type

Traffic

PopulationOther

Weather

Fatigue

© 2008 QUALCOMM Incorporated. External presentation to (audience), prepared by Qualcomm’s (presenters name) – Month Day, 2008.

8

7 8 9 10 4 5 6 1 2 3

Parking & BackingModel 3: Parking/Backing/Collision

Incidents per Year Assigned

-

0.02

0.04

0.06

0.08

0.10

0.12

Nu

mb

er

of

Inc

ide

nts

1 2 3 4 5 6 7 8 9 10

Over The Road CarrierTrip Distance variability

Net Payroll amount

Age of Vehicles

% of training done on time

Gasoline HaulerFatigue Score

Hard Brakes

Idle Time

Monthly average speed

Commercial FleetYears Driving Experience

Vehicle age

Sales performance

Zip code

Predictive Data at Work

RetentionService Failures

Monthly payroll amount

Take home percentage of pay

Total miles prior 6 months

Recruiting Avg. number prior employers

Longest prior tenure

Count of W/C claims

Real ResultsReal Results

We love gain share opportunities!

A Predictive Model’s Capabilities

Accidents Severity Turnover Productivity

23%

81%

49% 12

%

The Target -

Likelihood of drivers to have a worker comp claim in January Based on Decembers data.

The Result –

3 out of the 8 worker comp claims were made by drivers identified in the worst 1%.

A model built on 2 years of historical data:

Workers Comp Summary

Change from:

What happened?How many and where?What do we think the problem is?What do you think we should do about it?What’s the worst that can happen?

Change to:

Why is this happening?What does our data really say?What will happen next?How do we stop it from happening?What’s the best that can happen?

Change Management

Intelligent Risk Management… Intelligent Risk Management… That Drives Your Company ForwardThat Drives Your Company Forward

Thank You!Thank You!

FleetRisk AdvisorsFleetRisk Advisors