Projected age of retirement
for current workers.

Data that is false or fabricated.

The best data scientists turn
distilled information into pure gold.

Too much churn and
companies lose the cream.

Guatemala has the largest CW
compared to population in Americas.

1 in 3
# of working Americans in
the contingent workforce.

As some jobs become out of date,
others emerge.

In a conformity string, we call attributes
that impact cost and availability of
qualified job candidates "pieces of work".

Projected growth office/clerical
staffing 2013.

Companies implementing proper
measures during offboarding.

Singapore was world's top CW
productivity market 2014.

Data Scientist: the most wanted
job by employers on LinkedIn
in 2014.

Belgium has the highest tax burden in EU.

Ratio of robots to employees in Korea,
highest level in the world.

Employers who find paying
freelancers cumbersome.

The big star in our universe is Data Centauri.

% of American workforce projected
to be freelance by 2020.

Predictive analysis is only as
insightful as the analysts.

Data should never be sugar coded.

A good strategy stretches without
changing its basic shape.

Average length of unemployment
of managerial candidates.

# of workers with tenuous
ties to employers.

% of senior HR officers identifying
talent management as top HR issue.


To find answers, we formulate questions.
Then question the questions.

< 20
% of private sector workers receiving
employer sponsored health insurance
by 2025.

CW population at average
large company.

France has the highest
tax burden in EMEA.

% of Fortune 100 who’ve
implemented a VMS.

Shortage of US managers able to
analyze big data and make decisions
based on findings.

Amount NHS spends on
temp staffing.

Independent contractors can
be reclassified by Irish courts.

Staffing Industry Analysts | CWS 3.0: February 11, 2015

By Kristen McArdle

Today benchmarking is pervasive and there are many initiatives in varying scope and scale. Few CW programs don’t benchmark performance. But it’s not as simple as comparing your program statistics to those of others. Benchmarking projects need to be targeted, rapidly executed and iterative.

Here are a few key principles to enable you identify actionable and relevant comparisons without losing focus.

Know the context of what you’re comparing. Before digging into the specific practices or metrics that you want to compare, first discuss with your benchmarking partner the general program scope, such as:

  • whether it’s internally or externally managed, 
  • the extent to which program operations are standardized across the enterprise, 
  • the size and composition of the spend (e.g., spend by skill category, location, etc.), and 
  • how long the program has been in operation. 

This will give you a better idea of what’s most directly relevant, and what might not be comparable due to significant differences in how a program operates. Here’s a very simple example to illustrate the point: It wouldn’t make sense to compare a time-to-fill metric with a highly industrial program using master suppliers at manufacturing facilities if your primary focus is on hard-to-fill IT positions using multiple suppliers to competitively bid most requisitions. This doesn’t mean you can’t find value in any of the manufacturing company’s benchmarks (they probably have IT spend too), but you’ll need to drill into the details of what you’re comparing to ensure you’re excluding data that are irrelevant and may skew the results.

Don’t just focus on your industry. Often people feel the most direct comparisons will come from other companies in their industry. This often makes sense, but limits your purview. Perhaps the most notorious example of benchmarking across industries comes from Henry Ford touring a meat processing facility, which gave him the idea of applying division of labor and assembly line techniques to his manufacturing operations. When it comes to evaluating and comparing contingent workforce programs, there are more similarities across industries than differences. Material differences are most often related to very unique skill requirements and work locations used by some industries, particularly within oil and gas and mining organizations. When in doubt, go back to step one to see if it makes sense to compare yourself to another organization.

Don’t just focus on quantitative benchmarks. The origins of widespread corporate benchmarking in the ’70s and ’80s focused exclusively on identifying specific quantitative metrics, such as inventory turns and manufacturing cycle times. Later, the term was generalized to include any direct comparison of a company’s operations, both quantitative and qualitative. Examples of qualitative benchmarks could include contingent workforce policies, change management techniques, survey methods and performance management tools.

Be prepared. In order to maximize the efficiency of the benchmarking initiative, particularly to minimize the effort for the company you’re partnering with, it’s important to have an established plan that includes a summary of the initial points of comparison so they know what information to prepare (which you’ll invariably augment as you begin the discussions and identify additional elements to include) a detailed questionnaire they can see in advance, and tools for collecting quantitative data. When talking with your benchmark partner(s), designate a note taker from your team who can focus exclusively on capturing detailed information without the need to also facilitate the discussion.

As you begin a benchmarking effort, expect the unexpected and adapt your approach accordingly. You may find you want to expand or narrow the scope of your efforts, including the number of companies you talk with and the information you compare. Ideally benchmarking should be a recurring effort using an iterative approach. Your second iteration may look very different from the first.

Remember, you don’t want your benchmarking initiative to stifle your program.

View on the Staffing Industry Analysts website