DRIVER RETENTION ADVANCED STRATEGIES - Stay...

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DRIVER RETENTION:ADVANCED STRATEGIES

Tim Hindes – CEO & Co-FounderTimothy Judge, Ph.D. – Director of Research

Stay Metrics

Stifel Capital Markets Conference CallDecember 14, 2015

Copyright 2015 Stay Metrics, LLC.All Rights Reserved.

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Session Description• In response to high turnover

rates and projected drivershortages, carriers areincreasingly investing in driverretention strategies.

• This presentation will look atresearch being conducted andhow carriers leverage the data tobuck the industry retentiontrends.

Copyright 2015 Stay Metrics, LLC. All Rights Reserved. 2

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Topics to be Covered

1. Setting the Stage – Scope of Driver Retention Problem2. The Growing Use and Role of Driver Surveys & Analytics3. Actionable Data Leads to Operational Changes4. Advanced Retention Strategies (Including Loyalty Programs)5. Up Next: Using Predictive Modeling to Improve


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About Us

• Tim Hindes and business partner Kurt LaDow are serialentrepreneurs in the trucking industry.– Recognized need to elevate the focus on truck drivers as key to carrier success:

increase retention/decrease turnover, slow driver churn, increase safety.

• Stay Metrics, LLC founded in 2012. Based in Innovation Parkat Notre Dame in South Bend (IN).

• Tim Judge, PhD a professor at the University of Notre Damejoined the Board of Advisors; is now Director of Research.

Tim Hindes Timothy Judge


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Stay Metrics - Our Perspective

• If carriers have the right culture and implement a strategythey can beat the odds. They can have high retention/lowturnover and safer, more engaged drivers.

• The use of data analytics and action plans help managementto make changes that move the needle.

• Recognition is a key element for driver engagement.• Industry leading research; evidence-based ideas.


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Stay Metrics – Our Business• Carrier clients contract for various driver satisfaction surveys.• Driver survey data delivered to carrier:

– Includes industry comparisons and advanced analytics.• Carrier implements changes and suggested “best practices;”

and retention metrics improve.– Stay Metrics Driver Recognition Program: “Drive for Gold”

• Most clients implement as part of “best practices”• An evidence-based strategy; but not today’s focus


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• Fall 2015 American TruckingAssociations (ATA) whitepaperdetails growing shortage ofqualified truck drivers.

• Estimated at 48,000 now,possibly growing to 175,000 by2024.

1. Setting the Stage – Scope of Problem


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Carriers Face Three Related Challenges

1. Retention (Driver Turnover)- Problems & costs of turnover

2. Recruiting (to Replace)3. Recruiting (to Grow)

DriverTurnover

ReplacementRecruits

Recruitingfor Growth


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Protecting a Carrier’s Valuable Assets


EBITDADriver Count

5 Multiple

What’s the Enterprise Value (EV) of a driver?

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Carrier Responses to the Challenge

• Loyalty Program• Surveys and Data Analysis• Increase Pay• Sign-On Bonus• Driver Accommodations (routes, in-cab amenities, etc.)• Driver Retention Manager• Create a Driver-centric Culture


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Is there a common cause?

• There is only “one common known” reason supported byresearch for industry turnover:

Broken Recruiting Promise = Unmet Expectations• Our data doesn’t support the widespread beliefs:

“We are losing drivers to the trades,” or“We are losing flatbed drivers to dry vans.”

• The turnover challenge at every carrier is different.


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Carriers Continue to Look for Solutions, but…

• Lack of data about driver satisfaction and engagementmeans:– Attacking the wrong front– Spending money on increases that may not solve the problem– Losing time in the “war for drivers”


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Movement Toward Driver-Centric Culture

• It used to be the customer camefirst - some carriers are shiftingtowards a driver-first culture.

“Your employees come first.And if you treat youremployees right, guess what?Your customers come back,and that makes yourshareholders happy.

Start with employees and therest follows from that.”

Herb KellerherFounder, Southwest Airlines


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Possible Solutions: Surveys and Recognition


Source Forbes June 13, 2012

• With surveysdrivers have theability to beheard, makingthem feel a senseof love andbelonging.

• Formalrecognition with arewards platformincreases driveresteem.

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2. Use of Driver Surveys & Analytics

• Driver surveys are still a novel concept to many carriers.• Many carriers are managing with “gut” decision-making.• In our discussions, many senior managers are skeptical that

drivers will take the time to complete survey.• Our experience is that drivers want to give their opinions.


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Types of Driver Surveys

• Pre-hire Driver Match Survey• New Hire/Orientation• Orientation/Onboarding• Annual/Ongoing Attitude Survey• Exit Interview/Survey


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Stages of a Driver’s Life – Targeted Surveys

Hired Driving If SeparatedDriverEVENT

DriverPHASE

SURVEYS Orientation SurveyOnboarding Survey

Annual/OngoingAttitude Survey

Exit InterviewAfter Quit

OrientationDriver starts butat highest risk

EngagementDriver is working but need pre-emptivestrategies to prevent withdrawal

WithdrawalDrivers “quit”before they exit

OrientationOnboarding

Exit

GOALS Save the hire at period ofgreatest risk

Develop strategies to lowercarrier-wide turnover

Determine “real”Reasons for turnover

EventTIMING Annual/Ongoing


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Areas of Focus: Annual Survey

Source: Stay Metrics Sample Survey Domains.


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Driver Survey Data Analysis• Descriptive Statistics for Individual Questions• Composite Scores for Related Questions• Results by Driver Demographics

– Age, Gender, Years of Experience, Tenure with Carrier• Comparisons to Other Carriers Give Important Context

– What does an average of 3.5/5 mean?• For “recruiting promise” question a 3.5 means better than average carrier

performance.• For “great to drive for” question a 3.5 means much worse than average carrier

performance.• Comparison Data Concept is Illustrated on the Next Two Slides


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Q. Recruiting Promises Kept

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Carrier D Carrier F Carrier C Carrier B Carrier Avg Carrier E Carrier A

Experience has turned out as Recruiter explained

5 = Very satisfied4 = Satisfied3 = Neutral2 = Unsatisfied1 = Very unsatisfied


3.5 is thehighestscoreamongthissampleofcarriers.

Source:Clientreport withcomparativedata.

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Q. Carrier is Great to Drive For

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Carrier D Carrier A Carrier C Carrier Avg Carrier F Carrier E Carrier B

Carrier is a great carrier to drive for

5 = Strongly agree4 = Agree3 = Neutral2 = Disagree1 = Strongly Disagree


3.5 is abelowaveragescoresfor thisquestion


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3. Data Leads to Operational Changes

• What if a carrier did know– Why drivers stay– Why drivers leave

• Is the organization ready forchange?

• Are leaders ready for change?Can the carrier handle the truth?


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We don’t know what we don’t know

• Actionable Data• Two Case Studies

– Driver Pay– Dispatchers


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Actionable Data: Case study - Pay

• Example: Carrier Ythought they had apay issue but afterthey received ourdata they found outthat they areactually aboveaverage.

• This allows carriersto pinpoint theactual issues in theircompany.


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Actionable Data: Case study – Dispatchers



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Drivers Rating of Individual Dispatchers


5 = Very Satisfied4 = Satisfied3 = Neutral2 = Dissatisfied1 = Very Dissatisfied

Source:Sampleclient reportwithdispatchers’scores.

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4. Advanced Driver Retention Strategies

• Surveys and Analytics• Recognition• Loyalty Programs• Create a driver-centric culture• Predictive analyses and modeling


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Recognition: Basics Source Forbes June 13, 2012


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Driver Loyalty & Rewards Programs: 2015

Source: CCJ Annual Research Study.Published Sept 2015.

Driver rewards andloyalty programshave been adoptedby 45% of fleets.

Most view them aseffective:

7.25/10 self- managed,

7.8/10 for outsourced.


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Positive Impact of with Loyalty Programs• Drivers who report being more engaged with the carrier’s engagement program

are significantly less likely to leave the carrier.


Source: 12/11/2015. Analysis of StayMetrics client database of driverturnover; n= 22,531Engaged/Non-engaged driversdetermined by levels of participation inthe Stay Metrics/Carrier loyalty rewardsprogram.

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Creating a Driver-Centric Culture• Old school, blue collar operationally focused• Movement towards soft skills, people management, culture building• Challenge for some carriers – different leadership skillsets

Source: Two Stay Metrics, carriers located insame geographic region, similarcharacteristics; similar scope of operations.Actual turnover rates.


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Data Helps Pinpoint Differences – Then What

Younger Drivers are more likely toturnover (become inactive). Olderdrivers a more likely to stay.

Too simplistic to say it is a functionof the “millennial” generations; canalso be interpreted as a function oftheir age, tenure with a carrier.

At the same time, what can belearned from these differences?

Source: All drivers in the Stay Metrics database hired between January 1, 1980 and March 1,2015. n=20,383 drivers (9,835 active; 10,548 inactive), N=54 carriers.


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5. Up Next Using Predictive Modeling to Improve

• Brief explanation of predictive analysis & predictive modeling

• Specific applications to the trucking industry


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First-Level Data Analysis

33.23.43.63.8

44.24.4

D E F G H I J J ABC K

“My Pay is Fair”(1=Strongly Disagree, 5=Strongly Agree)

Copyright 2015 Stay Metrics, LLC. All Rights Reserved.

These results show usthat drivers for carrierABC are less satisfiedwith their pay comparedto other carriers

CARRIER

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Carrier Specific Recommendations

• Can a predict model offer carriers individualrecommendations for areas turnover and retention of biggestimpact?

• Main Predictive Model• Question• Index


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Second Level: Predictive Analyses• While it is worthwhile to know how satisfied one’s drivers are

compared to some benchmark, how do we know whether thatmatters?– Not all areas of dissatisfaction are equally likely to translate into turnover.– Shouldn’t we focus our efforts on changing the areas that matter?

• Developed a predictive approach where:– We first examine whether a carrier’s satisfaction is significantly higher or

lower than benchmark.– We then test whether scores on that item actually predict turnover, for

that carrier.


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Methodology• Investigated degree to which annual survey items predicted

turnover for drivers for a client carrier, which I call ABC.• While there is comparability in the predictive value of some

questions across carriers, there are also are uniquenesses.• There is also power in putting questions together

– What we call indices• So here I present results for two indices

– Predictive index optimized for ABC– All Stay Metrics clients: ABC’s predictive index applied to them


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55.56

15.79

11.76 13.33

3.57

16.5613.65

15.86 14.7217.63

0

10

20

30

40

50

60

1=Lowest 2=Low 3=Average 4=High 5=Very high

ABC All• ABC Index is a predictive index

comprised of 15 items (e.g., “Mydispatcher watches my back”) thatmaximally predict driver turnover forthem.

• Numbers in figure indicate percent ofdrivers who have left correspondingto scores on the predictive index,where 1=lowest, 2=low, 3=average,4=high, and 5=very high.

• Note: You can think of these likesatisfaction responses, where 1=verydissatisfied, 3=neutral, and 5=verysatisfied


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39.13

35.29

22.419.51

10

22.92

18.3

14.72 14.18 11.59

0

5

10

15

20

25

30

35

40

45

1=Lowest 2=Low 3=Average 4=High 5=Very high

DEF All• DEF Index is another predictive index

comprised of items that best predictturnover for them.

• Some of these items are the same asthe items ABC’s Index, but many aredifferent.

• You see that unlike the ABC Index, theDEF Index works fairly well for allcarriers, it just works better for DEF


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What Causes Your Turnover?***Shown as sample results only


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Upshot• Attitudes matter

– With ABC’s index, for the very dissatisfied, turnover is more than 15times (1,550% higher) than for the very satisfied.

• The road to reducing turnover goes through this index– True for all carriers, but especially for ABC.

• So how do I increase driver satisfaction?– We tend to under-emphasize intrinsics

• People (recruiter, dispatcher, company culture) and work.– Why do we tend to over-emphasize extrinsics?

• Salience: What comes to “top of mind” is not necessarily what drives our behavior.


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Can Predictive Modeling Help Carriers SelectDrivers?• Personality traits related to behaviors:

– Sample Personality traits: adventurous, homebody, rule-follower,self-centered, optimistic, happy, thrill-seeking, etc.

• Are there particular driver personality traits more associatedwith:– Driver Retention– Driver Safety


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Methodology• Administered surveys to driver incumbents that assessed driver

demographics, experience, work information (e.g., miles per week), andpersonality.

• Driver retention and safety criterion data collected from carriers every 90days since initial survey collection.

• Data analyzed by correlating personality variables with criterioninformation and determining the individual personality traits predictivefor turnover and safety.

• Truck driver turnover and safety rates were then computed for thebottom 10 percent (low scorers) and top 10 percent (high scorers) ofscorers on respective predictive personality traits.


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Harm-AvoidancePersons with high scores on harm-avoidance are overly cautious and avoid dangerous or high-risk situations.Low scorers lack limits, are willing to try anything once, and boldly take risks.

54%

14%

0%

10%

20%

30%

40%

50%

60%

Low Harm-Avoidance High Harm-Avoidance

Turn

over

Rat

e

Low Harm-Avoidance High Harm-Avoidance

Example: Retention and Harm Avoidance

Copyright 2015 Stay Metrics, LLC. All Rights Reserved. 45Source: Stay Metrics – University ofNotre Dame research. Judge, et al.

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AngerPersons who score high in anger feel enraged when things do not go their way. They are sensitive about being treated fairlyand feel resentful and bitter when they feel they are being cheated

75%

33%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Low Anger High Anger

Safe

Driv

er R

ate

Low Anger High Anger

Example: Safety and Anger


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Personality Profile Index (Preliminary)

NoLow Safety Score

YesHigh Safety Score

Good ProfileRight Stuff 13% 46%

Bad ProfileWrong Stuff 33% 8%

TURNOVERIs Driver Safe?

PERSONALITY FITDoes Driver Have

“Right Stuff”Based on Profile Index of 6

Personality Traits?

Index works 79% of the timebecause it correctly predictsdriver safety (Low or High) in

78% (33%+46%) of cases

Index fails 21% of the timebecause it incorrectly predictsdriver safety (Low or High) in

21% (13%+8%) of cases


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Next Steps for the Predictive Model

• Field testing the model.• Drivers are currently in orientation classes.• Follow the driver experiences over time.• Continue data analyses and refine the model.• Peer review and publication.


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This Just In: Another Use of Analytics in Trucking• Example: Literature suggests conflict/emotions distract attention and thought

processes– Application: Communication from dispatcher to driver

• Data mined from analyzing 400,000+ Qualcomm messages from dispatchers todrivers

• Used proprietary software to identify:– swear words (53 common swear words in English!), and– negative words (e.g., disgust, bad, hate, awful, stupid)

• Linked words “recording events” of unsafe driving behaviors from video captures(occurring after message)– Conducted within-person analysis, controlling for driver quality (so results are not due to

bad drivers getting more insults)


Source: Research completed with a large carrier client.University of Florida/University of Notre Dame StudyCooper, Foulk, Erez, & Judge, 2015

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Dispatcher-Drive Communication

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Swearing Negative Words

Dispatcher Message Content on Driver Unsafe Behavior

1 word above average2 words above average3 words above average

Upshot: Drivers whose dispatchers use swearwords in their message to drivers have a muchhigher number of unsafe incidents.

Upshot: Drivers whose dispatchersuse negative words in their message

to drivers have a higher numberof unsafe incidents.

Dispatcher Words UsedCopyright 2015 Stay Metrics, LLC. All Rights Reserved. 50

Source: Research completed witha large carrier client.University of Florida/Universityof Notre Dame StudyCooper, Foulk, Erez, & Judge,2015

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Conclusions• Driver Retention is Critical for Carrier Success• Conduct Driver Surveys of Various Types to Learn about Key Issues• Use the Data for Action Plans and Change Operations to Improve Driver

Engagement & Retention• Understand How to Leverage Existing Areas of Strength; Fix Problems• Implement a Formal Recognition and Loyalty Program• Shift Toward a Driver-Centric Culture• Use Predictive Modeling to Understand the Areas of Dissatisfaction

Leading to Turnover


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What Questions Do You Have?Contact us:Tim Hindes, CEO & [email protected]

Sign-Up for Newsletterwww.staymetrics.com

Follow Stay Metrics:

@staymetrics


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The Five “What’s”

Stay Metrics: An Action ModelTranslating Stay Metrics Analytics Into Higher Retention

SurveyingDriver Attitudes

Stay Metricssurveys your

drivers

UnderstandingWhat They Mean

Stay Metricsworks with you tosift through andunderstand data

GeneratingIdeas forPractice

Stay Metrics helpsyou consider best

practices

Acting on Ideas

You implement“low hanging

fruit” bestpractices

ReducingTurnover

You witness lowerturnover (which

Stay Metricsverifies)

What do they say? What do we know? What can we do? What are we doing? What’s the outcome?

The Upshot:It doesn’t do you much good to survey driver attitudes unless you’re able to put those metrics to use. In our experience,carriers are often too busy putting out fires and running the operational side of the business to effectively put the datato use (and too often consultants simply tell “war stories” that lack scientific grounding). At Stay Metrics, we use our owndata, along with your benchmark data, to help you implement turnover reduction strategies. It is a practice-orientedapproach to science, and a scientific approach to practice.


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Tim Hindes, CEO & Co-Founder

Tim Hindes has more than three decades of experience in the trucking industry, includinghaving worked as owner-operator driver and a dispatcher. His wide-ranging leadershipexperiences at several freight and logistics firms provide him with a unique perspective onthe industry, its management practices and operations and, most importantly, its drivers. Anentrepreneurial spirit led Tim to launch a brokerage company in 2008.

Most recently, Hindes and his long-term business partner founded Stay Metrics. Stay Metricsserves more than 50 carriers and 15,000+ drivers with its industry-leading loyalty driversatisfaction/engagement surveys and data analysis. Stay Metrics’ loyalty and engagementplatform has proved to have a positive impact on driver retention. Client carriers haveexperiences dramatic and sustainable reductions in driver turnover. Stay Metrics ongoingresearch is contributing the industry advances in driver safety and carrier operations.


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Timothy A. Judge, PhD – Director of Research

Dr. Judge is the Franklin D. Schurz Professor of Management and Department Chair in theMendoza College of Business at the University of Notre Dame. Judge received his Ph.D. fromthe University of Illinois at Urbana-Champaign. His research interests encompass the areas ofpersonality, leadership, moods and emotions, and career and life success. Judge haspublished 150 articles in refereed journals, including 88 articles in top-tier journals.

Led by Judge, the Stay Metrics Scientific Advisory Board includes some of the world's leadingresearchers in the areas of employee turnover, attitude assessment, recruitment andsocialization. Judge’s work, together with that of his research team and the Advisory Board,combine to ensure that Stay Metrics reflects the latest, cutting-edge research findings inthese and other areas, with an emphasis on evaluating the practical impacts of the latestresearch on the trucking industry.


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