Note: Images from the original text are omitted in this text version.

Executive Summary

Company profits are driven, directly or indirectly, by the performance of
every employee. Performance data for specific positions, carefully selected from
available metrics, can be used to improve each employee. Productive employees
will in turn increase the output of a position as a whole, which will lead to
increased company profits. But job effectiveness can only be maximized through
the use of proper performance metrics that accurately define success in a
specific position at the individual level.

This white paper provides specific steps to help you identify your strongest
employee performance data, then transform that data into a repeatable process
that will increase position productivity to its fullest potential through
hiring, training, and employee development. Before you know it, your workforce
becomes the engine that drives profits to new levels.

Converting Performance Data to Profit Dollars

Can I share a deep, dark secret? I am terrible when it comes to color
coordination. You would not believe the number of times I am told, “That outfit
doesn’t match.” Every time I hear criticism, I find myself thinking, “What are
they talking about? It looks great to me!” On the bright side, someone very
smart invented the color wheel for people like me. The beauty of the color wheel
lies in its simplicity. This well-designed model not only represents the primary
colors, but it also illustrates how they are interrelated and which colors
complement one another.

In contrast to the color wheel, many times in business we overcomplicate our
workforce models by using crazy strategies, dotted-line structures, complicated
competencies, or other popular attempts to improve productivity in the
workplace. Sometimes complicated solutions are the best answer. In contrast to
those complicated models, the Productivity Cycle (shown at left) provides
specific steps to help you catalog employee performance data, then transform
that information into a system that increases position productivity and drives
profits for the organization.

You will find that the Productivity Cycle provides a simple visual
representation of the steps needed to align people and profit. Like the color
wheel, the center of the cycle contains the primary stages: Catalog, Transform,
and Systemize. Each stage is supported by secondary actions that guide the user
around the wheel. These steps are represented by different shades of color
within each primary stage. You progress clockwise around the Productivity Cycle
as you move your workforce into a profit center.


By cataloging your available performance metrics, you embark on the path of
maximizing the performance of your people. But you must know where you are
before you can determine where you need to go. This principle applies to your
performance data. The first stage, the green area in the model, is designed to
help identify and understand performance data as it relates to an individual in
a specific position.

Learning Objectives
• Learn to classify the different types of metrics that are important to
employee performance.
• Learn how to collect the right performance data in the proper manner to
increase the accuracy of your findings.
• Learn to formulate the tough questions that help you choose which data best
promotes profitability through people.


The easiest way to understand performance data is to view it on a continuum.

Soft Metric: What Is It?

Soft metrics, on the left end of the continuum, describe any evaluation method
that relies heavily on a person’s judgment. Soft metrics can take many forms,
one of the most basic being when a supervisor ranks employees from the “best
performer” to the “worst performer” based on the supervisor’s opinion. Another
example may take the form of a subjective label. This scenario would entail a
subjective ranking of each employee (Good, Better, Best, or A, B, C, etc.).
Typically, there is not much science wrapped around this process. Practically, a
supervisor would sit down, think back to their perception of individual
performance, and apply a subjective label based on opinion and very little, if
any, objective criteria. When I see this evaluation method, I like to call it
the “I know my people” approach.

To align your employees with profitability, you should only use soft metrics as
a short-term solution and a first step toward more accurate performance
measures. Soft metrics can be used in placed of real data in situations where
there is no data available, but in the long-term you should be moving to systems
or programs that replace subjectivity with objective performance evaluation.
Soft metrics should not be used in place of performance data that is tied
directly to actual performance on the job. I have observed many corporate
executives who felt that they had a very tight grasp (without actual data) on
who their best and worst performers were. Each and every time we compared the
executive perception against actual performance, there was a sizable disconnect
between perception and reality based on the data. The point is to move your
organization away from taking the “I know my people” approach as quickly as you

Performance Appraisal: What Is It?

In the middle of the continuum, we find one of the most popular forms of
evaluation: the performance appraisal. This shift away from pure soft metrics
represents a reliance on subjective opinions, but those opinions are documented
using a standardized evaluation. Let me explain further. This method of
evaluation involves a person who possesses firsthand knowledge of each
employee’s daily performance. However, the performance appraisal differentiates
people through the use of standardized formats that capture performance

For example, a supervisor is supplied with a form that captures job components
or critical aspects of the position that have been studied and proven vital to
success in the role. These job components may include items such as Work Ethic
(reliable attendance, diligence in follow-up activities, positive attitude),
Communication Skills (conveys ideas clearly, resolves conflict), or Project
Management (meets deadlines, organized). The supervisor will actually rate
employees one at a time on each critical aspect of the job. Sample performance
ratings might be “Ineffective” to “Highly Effective,” or use a numeric scale of
1 to 5, or cover a range from “Does not meet expectations” to “Exceeds
expectations,” or thousands of other variations. This approach documents the
areas where employees are doing well, as well as where they may need
improvement, through a standardized system that translates general perception
into specific ratings regarding actual aspects of the job.

A performance appraisal tool can be an effective way to capture the opinions of
management in relation to employee performance. Appraisals are a popular form of
performance evaluation because in many positions it is difficult to quantify
performance at the individual level. In fact, we studied a sample of 37,055
people in 487 various positions in different companies and found that 69% of
these positions relied on performance evaluation tools as their primary form of
measurement. In addition, performance appraisal tools provide a flexible method
of quantifying performance based on the opinions of those who observe the
employees at work—primarily their managers.

Be aware of potential sticky issues associated with performance appraisals.
Obviously, one such issue is the subjective nature of the evaluation. This
emphasis on opinion often introduces inconsistencies across different
organizational groupings, such as geographies, departments, and locations. For
example, a manager in one area of the country may tend to rate incumbents much
lower than managers in other areas. This may make evaluating employee
performance across different groups difficult. A similar problem may be found
when the performance appraisal contradicts other performance metrics. This lack
of alignment often points to inconsistencies between managerial opinion and
numerical performance. There may be a number of reasons for the lack of
alignment, but there is always a high potential for inconsistency when human
opinion is at the center of the appraisal process.

Even though a performance evaluation is a popular tool, many companies are led
astray by the simplicity and ease of deployment throughout the company. If you
are truly pursuing an alignment of your employees to profit, you should do
everything in your power to go straight to the source—the numbers. Many
companies do a very good job of creating performance appraisal systems. The data
collected from these systems are high quality and as sound as can be. But when
the performance appraisal results for individual employees are compared to the
actual output numbers (in cases where the ratings are not based on the numbers),
there may be no relationship, and often presents a negative relationship. Be
sure that you do not rely solely on the ratings. Challenge yourself to find ways
to evaluate jobs with actual data.

Hard Metric: What Is It?

The right end of the continuum represents hard metrics. A hard metric is best
described as objective data that directly represents quantifiable information.
These types of metrics are typically linked directly to an organization’s bottom
line. Some examples of these metrics include throughput numbers, calls answered,
percentage of quota, quality scores, number of units sold, total sales, average
handle time, or any measure directly related to job performance. Hard metrics
provide valuable insights into the numerical productivity of a person in
virtually any position. From a company’s perspective, the appeal of hard metrics
stems from the objectivity of the data. Hard metrics are not adjusted or
influenced by human opinion. As long as the role stays the same and the data is
collected in the same way, hard metrics are a dependable measure of performance.

You will come across some jobs that do not appear to possess clear, hard
metrics. In this situation I would encourage you to remember the phrase “work =
output.” What we get paid for is called work because there is an expected
output. It is simply a matter of collecting information surrounding the skills,
abilities, responsibilities, tasks, and expectations of the job. Then use that
information to create ways to quantify the output of the position and
systematically collect performance data. With a little time, effort, and
creativity you will find that nearly any position can be numerically classified
in terms of hard metrics.


Now that you know how to classify performance data, the first step is to collect
the data. Later we will be able to evaluate its usefulness. Have you ever heard
the saying, “The devil is in the details”? Likewise, your ability to transform
your workforce from an expense to a profit center can be derailed quickly during
the action step of data collection. Prior to collecting the data, you will need
a few safeguards to ensure the consistency, accuracy, and accessibility of the
data collection process will not affect the interpretability of the metric.

Consistency of the data collection process is very important. Everyone involved
in data collection should understand and adhere to the specifics of the data
collection process. Inconsistent data collection methods will lead to inaccurate
comparisons among individual performers. Pay special attention to location or
regional differences. Inaccurate evaluations of performance will contaminate any
future findings and reduce the effectiveness of your future adjustments. Think
of consistency in terms of a simple illustration. If I ask all my district
managers to give me their turnover numbers, I may receive percentages from each
district manager but the numbers may mean many different things. Some may have
given me annual turnover, some turnover for a single month, and others may have
given me involuntary turnover only. The point is to be careful and ensure your
data collection processes drive consistency.

Accuracy of the performance data being collected is also an important phase of
the collection process. Accuracy must be a priority when interpreting individual
performance. Later in this process, inaccurate data will lead to false
conclusions and bad decisions when evaluating and developing your employees.
Think of inaccurate data as the enemy of transforming your workforce. After you
have collected your data, use these “red flags” to alert you to potential
inaccuracies in the data:

• Incomplete data or cases where it is commonplace to find no information.
• The use of “0.” Is that “0” representing actual performance or a blank entry?
• Data presented in a number of different formats – for example, half of the
data is presented in percentages and half as round numbers.
• Odd outliers – for example, most of the cases in a data set contain
single-digit performance measures, but some cases show triple digit measures.
• Labels do not match the data – for example, “Dollars Sold” is the label, but
the data is presented in percentages.
• Conflicts in columns – for example, an employee with a September hire date has
performance data recorded from March of the same year.

Another factor to consider is the accessibility of the performance data.
Sophisticated human resource information systems (HRIS), payroll systems, and
performance management systems are helpful tools as long as you have easy access
to the data. Avoid situations where the data is difficult to collect and study.
All too often companies focus on collecting performance data at the aggregate
level and neglect to collect and study it at the individual level. Whether the
data is performance ratings, quality scores, or sales figures, make sure your
data collection systems are tied to individual performance.

Another valuable tip to consider when collecting a performance metric is the
number of data points, or employee observations, represented in the data set.
Whenever possible, it is beneficial to have access to multiple observations of
the performance data. For example, monthly observations would be richer than a
simple yearly total or average for the year. Anytime the data is aggregated,
there is a chance that you will lose some valuable information that may be
helpful in understanding performance trends related to the position. When
collecting your data, always focus on your objective, which is to obtain the
best data that will lead to the richest amount of information.

Now it is time to collect data. Apply the principles you have learned about
performance data to collect the cleanest data set that you can. It is a good
practice to initially overshoot the amount of data you would reasonably expect
to use. Collect many types of metrics and forms of performance data for each
position. This practice gives you multiple measures of performance, but more
importantly, it helps you choose the best combination of performance indicators
by providing options (different performance data) as we will discuss later.


After the performance data has been collected, there are several choices you
need to make to help identify the best metric(s) to focus on. In order to make
the best choices, there are a few things to consider. Specifically, does the
data you captured reflect variability, job-relatedness, and a relationship to
your business objectives (keep reading for an explanation of these terms)?
Throughout this process, it is important to understand that as soon as your
performance metric is specified, it will begin to shape and guide the direction
of your workforce. All future performance, evaluation, and developmental
activities in that position will be directly influenced by the metric.
Therefore, choosing the right metrics to follow is an important consideration to
drive the future of your business.

Variability is ensuring that the data metric represents all performance levels.
Ask yourself this question: Does the metric differentiate between individuals’
performance levels? Oftentimes, performance metrics are consistently collected
and accurate, but they lack variability in performance scores. I once worked
with a company that insisted a particular quality rating was its main indicator
of performance for its call center representatives. Upon further review of the
data, we found that the average score was nearly 100%, with only a handful of
incumbents receiving a lower score of 98-99%. This data offers no useful
measurement because it implies that each employee is performing at the same high
level, with no variances to highlight specific performance concerns. Any
business leader would have a hard time choosing a metric with no variability;
therefore, this type of data offers little, if any, real value.

Job relatedness is another important issue to consider when choosing the best
data on which to shape your future workforce. Determine how much influence an
individual has on the performance metric. In all cases, direct influence is
best. The less influence incumbents have on the metric, the less descriptive it
is of their actual performance. In an ideal situation, you will have great
confidence that your performance data is related to the job and that each
incumbent is able to affect that metric directly.

For example, a car dealership can track the number of cars that are sold by its
salespersons, as well as how many of those sold cars are returned to the
dealership’s service department for repairs. When looking for job-related sales
metrics, the former measure is good, but the latter is unrelated to sales. Does
a salesperson have an influence on the mechanical soundness of the car he sells?
No, he only has control of the selling process. Relying on a metric with little
relation to actual job activities will lead to inaccurate conclusions.
Additionally, your mindset should be in pursuit of truth as it relates to real
performance on a daily basis. This truth can only be found if the data is
related to performance in the position.

Business drivers—it is time to think strategically! Think in terms of the
direction that you want to take your business, and then the position-specific
metrics will move each position in that direction. Alignment can be found by
working backwards. Ask yourself how each position fits into your business
strategy or contributes to the financial performance. Then determine the
individual performance metrics that best align to the position and allow you to
track your progress toward achieving your business goals. Referring again to our
car salesperson example, a strong business driver might be “number of cars
sold.” If it does not drive bottom-line profit, it should not be a cornerstone
of your performance data.

Evaluating your individual performance data in terms of variability, being
job-related, and being a business driver is a major strategic step in the
process of transforming your workforce from an expense to a profit center,
thereby directly improving the productivity of your people in driving your

Summary: Finding Ideal Performance Data for a Position

Now that we have explored the Catalog stage, you have learned how to:
• Classify performance data according to what is available, useful, and
• Collect the data from individual performers in a specific position.
• Choose the performance data that reflects variability, job-relatedness, and a
relationship to your business objectives.

At this point, you should have performance data selected and collected for each
targeted position so that you can turn that knowledge into the building blocks
for a position-specific template.


As previously stated, the goal of this white paper is to help you identify your
strongest employee performance data, then transform that data into a repeatable
process that will maximize productivity. In the last section we classified,
collected, and selected the strongest measures of employee performance. Now we
examine the Transform phase of the process in which your performance data is
matched to the actual job behaviors strongly related to success in the position.
By determining which traits are most important to good performance, we can then
build a position template that organizes those traits, and then translate that
position template into desired behaviors specific to the job. The Transform
phase brings you closer to the ideal workforce that drives profitability for the

Learning Objectives

• Learn to recognize key traits that tell you how a person is successful in a
• Learn tips on how to create a job level position template that targets the
traits necessary for success.
• Learn to translate the traits within a position template into job-related
behaviors that reflects those who are producing more, and contrast their
behaviors with less productive individuals.


At this point in the Productivity Cycle, we have focused on the critical aspect
of cataloging performance data. Although the performance data indicates the
result of each person’s efforts, it does not tell you how they achieved their
results, nor will it tell you how internal or external candidates for the
position will perform on the job. Therefore, we need to spend time discussing
the first component of the Transform phase—identification of traits.

Behaviors, or traits, that drive performance are best determined by “letting the
data speak” as opposed to making “educated guesses.” A time-tested method of
identifying traits, skills, and other relevant pieces of job-related information
comes from the use of a job analysis. A job analysis collects clues as to what
is needed to properly execute a job.

There are many methods to analyze a job. One common method is to send out a job
questionnaire to experts in the role, asking them to document their opinion on
the important tasks or traits needed to be successful. Another method is to
manually observe and document the traits needed for success. However you package
it, the basic idea is to manually study and document aspects of the job. A job
analysis provides solid information about the minimum qualifications and skills
necessary for a role. But a typical job analysis will fall short when you want
to gather a deeper insight into the actual performers in a position. Said
another way, a job analysis will not provide you a vehicle to “get in the heads”
of those who are successful and compare them to those who are not successful in
a role.

It is important not to confuse minimum qualifications with actual predictive
performance. Many people make the mistake of assuming that meeting the minimum
qualifications is the finish line. For example, a job analysis may indicate that
it is necessary for incumbents to operate a particular phone system. After the
second day of training, everyone understands the phone system and can
effectively operate it. Even though using the phone is essential for daily
performance, it exhibits no relationship with real success on the job. Being
able to perform a job and being successful at it are two very different
concepts. Your goal for each position in your company should be strong
performance, not simply getting by.

To achieve the goal of strong performance, you must dig deeper into the actual
traits and behaviors that drive success. Behavioral assessments are a very
effective way to collect data on the traits of individuals from all performance
levels in the position. A behavioral assessment is a tool or, as I call it, a
vehicle for data collection that extracts information from individuals related
to their behavioral preferences. These traits, in addition to performance data,
will provide the data needed to help identify how employees have success or
failure in a position. Specifically, behavioral assessments will provide you
with insight into individuals’ preferences related to how they approach
problems, process information, interact with others, and respond to various work
situations. Typically, this information is collected through a series of
questions presented to the individual using a questionnaire. The answers are
turned into conclusions that represent specific preferences or behavioral traits
that provide clues into how and why people do what they do when working.


Be careful—it is not all about the performance data in the job analysis or the
results of the behavioral assessment. It is about how you use the two together
to transform performance data into a template of targeted behavioral traits. To
fully capture the traits most conducive to success in a position, you need to
let your business drivers (performance data) dictate the importance and amount
of each trait. The assumption that more of each trait is best will lead you down
the wrong path. Consider a trait such as “independence” in an individual
contributor role. A successful person in this role is measured in terms of
throughput. This position requires an employee to sit at a desk and complete
repetitive tasks in accordance with specific instructions from a manager. Think
about it—would someone who is extremely independent-minded be successful in this
role? In this case, it is safe to assume that an individual’s desire for
independence would actually inhibit their performance.

Using Technology to Measure Traits

When developing a position template (Performance Data + Traits), you should
begin by identifying the traits of successful people that differentiate them
from their less successful co-workers. Technology is often used to simplify this
process. Most behavioral assessment tools generate numerical representations of
an individual’s behavioral traits. These numerical representations are often
called dimension scores, characteristic scores, factor scores, or many other
assessment-specific names. The basic idea is to provide you with information
that plots a person’s trait on a scale where you can better understand how that
person compares to others for each characteristic. Most behavioral assessment
tools offer many traits used to describe the individual. Either way, technology
will enable you to quickly and accurately collect trait information.
Additionally, utilizing assessment technology will streamline your ability to
make statistical comparisons between individual performers. The final objective
is to use performance data to discover the traits that are most predictive of
success in the position.

The specific steps listed below will help you create a position template with
the use of technology.

• Statistically search for the relationships between traits and performance
• Within a position, split your employees into groups based on their performance
• Calculate trait score descriptive statistics (average, median, standard
deviation, etc.) for each performance group.
• Compare performance groups by descriptive statistics.
• Search for any hidden patterns of traits among performance groups.

If Technology is Not an Option

If assessment technology is not available in your situation, let me suggest a
few pointers that may guide you in your efforts to creating a position template.
First, ask your subject matter experts if they have any theories as to which
traits enable individuals to be successful in the role. Then, compare the traits
based on the experts’ theories to the performance data you have collected. The
goal is to determine if the theories are supported or contradicted by the data.
Think of this as a process of taking something from theory to reality. The key
is not to take the experts at their word, but to apply the theory against actual
performance data and attempt to confirm or deny the theory. A good illustration
of this concept comes from the retail sector. A certain group of executives
theorized that successful store managers were very ambitious. However, as we
collected information at the individual level, we found that successful managers
had been in their role for many years and were very comfortable with their
contribution to the company. There was no desire to move up or out, so the
assumption of “high ambition” did not prove to be accurate.

Here are a few specific steps that may help you in creating a position template
without the use of technology.

• Use the performance data to create subgroups based on performance level.
• Count the percentage of people in each subgroup who possess unique traits.
• Document the commonalities among the performance groups.
• Compare and contrast the characteristics across performance groups.
• Find those characteristics that stand out and differentiate performance
• Use your findings to create new areas to study—keep digging.

Group Traits

It is important to remember that, at this point in the cycle, you are looking
for group traits, not the traits of one individual performer. Focusing on only
one individual as the ideal employee for a position will eventually lead you to
inaccurate conclusions. This is true because some trait studies contain
anomalies, such as successful individuals whose approach to work is unique when
compared to the other successful people. Understanding the “group” concept will
help you ensure that your position template is based on traits that can be
replicated by others. The template, once created for each position, becomes a
powerful tool that can be used to directly align individuals with real
performance objectives.


You should have now identified the traits required in the position (both key
behaviors and optimum targets based on performance data), and built a position
template of those traits. The next step is to make the position template useful
in practice. It is important to note that this step is not changing the
template, but simply understanding what the position template means for the

So right now you may be thinking, “This is a great exercise, but how can a
position template impact daily performance?” This is the exciting part! Because
your template is based on desired performance (performance data), it represents
the individual traits that have exhibited relationships to individuals
performing in a desirable manner. However, we want to make sure the position
template can be used on a daily basis. By translating the traits of the template
into job-related behaviors, you will better understand those who are producing
more, and contrast their behaviors with less productive individuals. This
enables you to apply the information in a way that drives your workforce toward
actual productivity results while aligning closely to your business drivers.

For example, imagine that you are analyzing the cashier position in a grocery
store. While collecting job traits for your position template, you discover that
cashiers need some level of sociable behavior to perform successfully.
“Sociability” becomes a part of the position template that you are building.
Your observations indicate that the best performers seem to be moderately social
while lower-performing cashiers tend to be extremely social. These findings may
contrast with logic (the more friendly the cashier, the better), but the
performance data supports the moderately sociable trait.

You can now translate that trait into actual practice. According to your earlier
job analysis, it is the cashier’s job to be friendly while maintaining a focus
on productivity. Overly social cashiers attempt to have deep and meaningful
conversations with every shopper, causing long lines and dissatisfied customers,
while the social moderates can engage in small talk with customers while keeping
their lines moving. By translating the sociability trait, we establish the link
between the trait and performance on the job of those who are producing more.

Summary: Transforming performance data into traits that drive work-related

By following the transformation process from trait collection, to position
template creation, through translation of the position template into
work-related behaviors, your position template becomes a practical tool to
fine-tune your workforce. The position template gives you a scientific method to
analyze the traits that differentiate performance for any position.
Additionally, the template gives you the direct link needed to move your
workforce from an expense to a profit center.

After all of the hard work put into creating your position template, you want to
be sure that it is fully utilized. Be sure to spend time developing a strategy
designed to leverage the position template throughout each employee’s life
cycle. A couple of key areas where this information can make a direct impact are
selection and succession planning. Also, make a point to study your progress
(after a sufficient period of time has elapsed) and make adjustments based on
your study findings.

Learning Objectives

• Learn to select employees from the candidate pool who best represent the
collection of ideal behaviors important to success in the position…and make the
best selection on a consistent basis.
• Learn to develop individual succession planning strategies for each position
based on the position templates that you create.
• Learn to study the performance of employees hired and developed using a
position template.


Selecting the right people for the right positions is always a great place to
use your position template created using successful traits. Employee selection
will always have a large and immediate impact on your financial bottom line. Any
sports coach will tell you great players make great coaches, just as any manager
will tell you great employees make great managers. Leveraging this information
in your selection process will improve the odds of finding the best of the best
for your position.

Here are a few areas where you can incorporate your position template quickly
and efficiently in the selection process:

• Pre-screens—This is a very practical way to focus on those candidates who meet
the minimum qualifications for the position. Because you now have deeper insight
into success in the position, you can screen out those who do not have the
“right stuff.”
• Phone screens—Here is another way to help your recruiting staff be more
efficient and performance-minded. Start by sharing your findings. Then provide a
series of phone screen questions, based on the desired traits, to help
recruiters identify those who have the best chance to succeed in the role.
• Behavioral assessments—synchronize the position template with your assessment
tool to identify “ideal” candidates as part of the application process.
• Face-to-face interviews—Creating a consistent interview process is a tough
task. Hiring managers often have different opinions as to what contributes to
success. Now you have an ace in the hole. You know the important traits
(recorded in the position template) that drive high performance. A great way to
incorporate your position template in the selection process is to create a set
of trait-based interview questions. Be sure to train your hiring managers on the
data-driven source of these questions and how they are linked to success in the


Succession planning is a long-term journey for leveraging your position
templates. Most succession planning programs are designed to develop the bench
strength at the management level. Any succession planning program requires a
target or, in this case, a template to teach, train, and evaluate potential
future performers based on the traits needed to be successful in the role.

Once you have successfully created a position template, you have a map for
success that can guide your internal promotion and development programs. From a
long-term perspective, think of the possibilities. You have valuable information
to shape programs at the position level, based on traits linked to performance
data, which directly reflects your business drivers. If leveraged properly, you
will be able to use this information to identify gaps in your training as well
as create content for individual training plans tailored to each employee and
their current and future roles. Do not forget about your ability to coach and
develop your workforce more effectively by communicating clear and specific
expectations for performance.


A study should occur after an adequate length of time has passed since the
rollout and implementation of your position template. Since it occurs after the
rollout, it is generally referred to as a post-deployment study. You should
schedule time in the future to measure your workforce improvement as it relates
to the deployment of your position template. It is also sensible to initiate a
recalibration of your findings at some point in time based on the findings of
your post-deployment study. In other words, your business may change and new
products, expanded markets, and reorganizations all contribute to changes in the
traits or position template of a job. Always keep in mind that anytime you
change the way you measure job performance, you increase the likelihood of
changing the traits and the position template.

The goal of a post-deployment study is to ensure that the position template
continues to represent the desired traits most conducive to success in the
position. Prior to conducting a post-deployment study, there are a few tips to

• Be patient and give your process time to “bake.” Many companies make the
mistake of attempting to study progress without considering the time needed for
full implementation or new hire ramp-up. It typically takes 12 to 24 months to
see the full effect of the changes you have made in the workforce. I was
involved in one situation where a business executive wanted to know why his
company’s turnover rate had not decreased during the first few weeks of a new
hiring strategy deployment. We kindly explained to him that he needed to hire
people based on the newly defined behavioral traits for a period of
time—allowing them to gain some experience on the job—before there would be
enough data to evaluate performance and tenure.
• Set realistic expectations—You will have the ability to examine every position
and extract key traits, but not all positions will qualify for a follow-up
study. In some positions, there may not be enough employees or turnover to
effectively evaluate the direct impact at an aggregate level.
• Clean the data—When following up, be sure that you focus your efforts on
relevant samples of people. Do not mix different positions in the same study
group. Narrow the sample to employees that have been on the job long enough to
possess an adequate work record, and remove any data that will contaminate the
sample and produce inaccurate conclusions.

One Company’s Results after Following the Productivity Cycle

HSBC, one of the largest financial organizations in the world, used the
principles described in the Productivity Cycle to increase sales generated by
employees in the position of Account Executive. HSBC Account Executives initiate
loan sales and provide customer service, two activities that directly influence
company profits. After methodically cataloging, transforming, and systemizing
the behaviors of those in the position, HSBC selected new employees that sold
21% more loans than coworkers who were hired outside of the Productivity Cycle
process. This figure was based on the post-deployment study of (n = 2,040)
employees in the role. (To review the full case study, visit


HSBC is just one example of a large company that saw an opportunity to
transform an important sales position into a stronger profit center. The 9-step
Productivity Cycle succeeded in raising the bar for average sales by 21%. Could
your organization make room for a 21% sales increase? How about 30% more calls
handled by telephone representatives? A 40% reduction in annual turnover? The
sky is the limit once you have worked your way through the Productivity Cycle
for a specific position. The results may warrant applying the process to all of
your job positions over time.

Author's Bio: 

Jason Taylor uses science and technology to design tools for the selection and talent management field. Annually, the tools under Taylor's direction match several million employees to employers. Taylor often speaks on talent management and selection technology at conferences across many industries including HR, retail, hotel, restaurant, real estate, and industrial-organizational psychology. Member: APA and SIOP.