1. Get in touch. Call us at 1.866.963.6941 or write us at info@canworksmart.com.
Hiring & Predictive
Analytics:
Selecting the right
candidates to increase
driver retention.

2. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 2
Hiring is a tireless search for
the best candidates. It requires
finding candidates with the values,
education, and work experience
managers think will lead to
success within their company.
But, predicting success requires experience.
And unfortunately that experience can be
unique to each manager­. Some of the reasons
to hire someone can be verbalized, but most
are hard to verbalize — they use intuition.
Each manager has a hiring “formula”. Their hiring formula goes
beyond tools provided by human resources, because managers
ultimately decide who works for them. However, intuition makes
hiring formulas hard to share and combine.
If hiring formulas can be defined, the hiring process can be improved.
Defining hiring formulas allows managers to combine them, test
them, and track their results over time. Companies have struggled to
do this because they can’t capture the subtle (and less–subtle) reasons
for hiring or not hiring a candidate. Thankfully, predictive analytics
makes this possible.
Predictive analytics can create a hiring formula using data from
every applicant and employee — and their job performance.
Predictive analytics uses data to capture the patterns in your
company’s collective experience, across employees and time.
Don’t worry, you likely have the necessary data. All companies,
except the newest, have the data necessary for predictive analytics.
We work in the digital age. Companies collect data on everything.
How would your
interviews change if
you could focus on only
qualified candidates?

3. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 3
Every event and action is translated into data. The key is knowing
how to find the data and unlock the right patterns. The value of data
is in the patterns.
The patterns in your data allow you to predict the future and describe
the past. In this case study we explain how we used the patterns in a
trucking company’s data to tell them who to hire and why. CAN used
predictive analytics to reduce driver turnover 10% and save $1.7M
per year.
The problem
CAN was approached by a trucking company that hires 5,000
drivers a year. They had an annual driver turnover of nearly 100%.
Unfortunately, high driver turnover is common in the trucking
industry.
High turnover wasn’t the problem: the problem was drivers were
leaving too early: before the company could earn a profit on hiring
and training each driver— an investment of $3,500 per driver or
$17.5M in total per year. The following chart shows the distribution
of the total number of weeks every driver in the study was employed.
Number of weeks it takes
each driver to break-even

4. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 4
The company broke-even on most drivers between the 10th and
30th week of employment, depending on the number of miles they
drove. However, this was also when the probability of drivers leaving
increased most dramatically—as illustrated above.
CAN was asked to identify ways that the company could reduce
the probability of drivers leaving between the 10th and 30th week.
The result was an increased return on the investment in hiring and
training drivers.
The solution
This problem was different than CAN’s other employee retention
projects in retail, insurance and banking. Typically we identify
employees that are most likely to leave, allowing managers to give “at
risk” employees more attention, new assignments, encourage them,
or make them feel special. During the discovery process for this
project we decided that this approach was unlikely succeed in trucking.
Instead of trying to improve employee retention post-hire, we
decided it would be more effective to hire drivers that had higher
probabilities of staying. We decided to improve the hiring process,
instead of the management process.
Over 3 years
$35,639,000 was
invested to train
~10,354 drivers who
quit within one month
of completing training.
How can this slope
be flattened?
Probability of being employed
by week

5. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 5
CAN started with 3 years of data, and after cleaning the data, had
reliable data on 12,819 drivers. We divided the drivers with 9,701 in
the study group and 3,118 in the control group. Our analysis looked
for patterns in the following variables:
The results
CAN’s analysis found 6 variables to be statistically significant
predictors of driver retention—3 are controllable. We made
recommendations based on what the company could control
and presented decision makers with estimated impacts to driver
retention and savings. The following is a discussion of our findings
for all 6 variables, including our recommendations.
1. Hire type
This variable is uncontrollable. The company hired several types of
drivers, however the majority all fell into one hire type. They have
little control over this, even if other hire types have higher retention.
2. State
This variable is uncontrollable. State is most likely an indicator of
local competition from other trucking companies and competing
industries such as construction. While our client can avoid hiring
some drivers in highly competitive areas; in the long-run they can’t
avoid hiring drivers where they are needed.
3. Training school
This variable is controllable. The company hires drivers from several
thousand training schools—5 training schools have higher than
average driver turnover. Our recommendation is to avoid hiring
from these 5 training school: saving the company $86k per year. The
following graph shows the probability of driver retention by week
before and after the 5 training schools were removed.
1. Gender
2. Training school
3. Hire type
4. Number of unloads
5. Family type
6. State
7. Age at hire
8. Average net pay per mile
9. Eligible for rehire or not
10. Average net pay per week
11. Days tractor broke–down
12. Home time requests rejected

6. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 6
4. Age at hire
This variable is controllable. Age is a proxy for experience, CAN
recommends focusing on experience when possible instead of age.
However, age at hire is a statistical significant predictor of turnover.
The following graph shows the inverse relationship between average
turnover and age at hire.
Average Turnover by Age at Hire
Weeks Employed
%Retention
60%
40%
20%
80%
100%
0 5 10 15 20 25 30 35 40
Before
After
Removing the bottom 5
training schools
Impact of $86k per year

7. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 7
We wanted to understand why age at hire had an inverse relationship
with average turnover. We interviewed several drivers and managers.
We discovered that many people looking for work are told that they
should go into trucking and apply without understanding the job.
We also discovered that more experienced workers with higher
retention were more likely to come from jobs such as farming that
required long hours of driving. While many less experienced workers
with lower retention came from construction, manufacturing and
the military. The theory and data supported each other — the key to
good data driven research.
The company needed to continue to hire qualified drivers of all
ages. CAN did not recommend the company only hiring drivers of
a certain age, but that they change the distribution of drivers. The
following graph shows the distribution of drivers by age at hire that
CAN recommended.
Current and Recommended
Workforce by Experience
Before
After
36-40 41-45 46-50 51-55 56+
Age at Hire
31-3526-3020-25
10%
5%
0%
15%
20%
25%
%ofDriverPopulation

8. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 8
Shifting their workforce to favor experience helped them save $358k
per year.
Weeks Employed
20 25 30 35 40151050
Before
After20%
40%
60%
80%
100%
%Retention
Adjusting Workforce for
Experience
Impact of $358k per year
5. Average net pay per week
This variables is controllable. Drivers are compensated by how many miles
they drive. We found that if the company could provide drivers with an
additional $70 worth of work a week they could increase retention for a
savings of $1.3M per year.
Weeks Employed
20 25 30 35 40151050
%Retention
60%
80%
100%
40%
20%
0%
Before
After
Increasing Work and Pay
$70 per week
Impact of $1.3M per year

9. Hiring & Predictive Analytics: Reducing driver turnover 10%.
Contemporary Analysis Page 9
6. Number of days tractor is broke-down
This variables is uncontrollable — beyond regular maintenance and
safe driving. However, breakdowns means drivers can’t earn a living.
Drivers are assigned a tractor and are paid by the mile. If they don’t
drive, they don’t get paid. It was not surprising to find breakdowns as
a significant driver of driver retention.
Conclusion
In the end CAN helped the company reduce driver turnover 10% and
save $1.7M per year.
Weeks Employed
20 25 30 35 40151050
Before
After20%
40%
60%
80%
100%
%Retention
All 3 Recommendations
Impact of $1.7M per year
This case study show that predictive analytics excels at helping
companies understand and manage the complexity of hiring
employees — from defining selection criteria to filtering applicants.
Predictive analytics allows companies to capture the experience
and intuition of your company and managers to demystify human
resources and create a shareable hiring formula. We look forward to
helping you increase your employee engagement and reduce turnover
by hiring the right employees.

10. Get to know us. Learn more about CAN.
Contemporary Analysis Page 10
Our solutions are used by fast-growing technology companies,
Fortune 500s, as well as small- and medium-sized organizations.
Our clients are in a variety of industries including construction,
insurance, education, healthcare, government, not-for-profit,
software and engineering.
Our vision is to make predictive analytics simple and affordable
because all companies, not just the largest, should be able to
benefit from predictive analytics and data science.
Our principles:
1. We care about business.
Each business deserves a custom solution. Problems are our passion.
2. We solve core business problems.
We make a big impact quickly. Value is our focus.
3. We don’t have all the answers.
We help our clients make better decisions. Less wrong is the goal.
4. We are technology agnostic.
We focus on the solution. Technology is just a tool.
5. Our job is to solve problems, not introduce complexity.
Our solutions are simple because our clients are busy.
Since 2008, Contemporary
Analysis has used predictive
analytics and data science to help
companies of all sizes work smart.
Our five products use data to help our clients
improve their sales, marketing, customer
service, management, and strategic plans.