This article originally appeared in the November/December, 2017 issue of PARCEL.

How many parcel shippers truly read the story their parcel data tells? The reason I ask: To have a discussion about parcel benchmarking against other businesses, the data — be it from invoices, transit and tracking information, your carrier agreement, or carrier rate and accessorial details — is the story.

But that’s too often forgotten or ignored because the term benchmarking has become a generic buzzword that has lost real meaning — another piece of marketing jargon used by vendors as a “me-too.”

So, let’s first separate the cliché from the reality. Real benchmarks are not derived from surveys of self-selected respondents in unrepresentative samples. Benchmarking is not a measure defined by personal anecdotes or previous experiences — no matter how recent. Benchmarks are not rough estimates. Instead, effective and meaningful benchmarking is a precise mathematical exercise driven foremost by data (lots of data).

Effective benchmarking is supported by industry expertise — useful in defining what is to be benchmarked and what important performance measures should be compared to that of peers (which don’t have to be limited to competitors, neighbors, or companies of similar size). It identifies the best types of peers and calculates benchmarks from within peer groups.

When done correctly, benchmarking is a valuable way for companies to gain perspective on the effectiveness of their own performance, to set performance expectations, and to identify and prioritize areas for improvement.

Through benchmarking, companies understand where they’re at and where they need to go, setting a foundation for strategic decision-making. But if benchmarks are derived from an inappropriate set of peers — from surveys, best guesses, or a consultant’s memory – you could miss the mark, strategically speaking.

The sports analogy

Major League Baseball, for example, has figured this out. The revolution in baseball statistics — think sabermetrics, or the central theme of the book “Moneyball” and its film adaptation — has largely been driven by the realization that broad, non-specific measures are inadequate aids to decision-making.

For instance, say you’re a baseball general manager looking to trade for a slugger. Does it make sense to look only at home run totals without considering the size (depth) of the stadiums, the elevation, or the climate in which your team plays? Of course it doesn’t. Ballparks vary substantially, just as the quality of pitchers faced can have a significant impact. Each player’s accomplishments are accumulated under unique circumstances and are affected by many variables.

Considering all factors is essential to drawing meaningful insights. MLB statisticians now use sophisticated methods to account for the conditions impacting each player’s performance to make much better player comparisons.

To benchmark performance means comparing that performance against an appropriate standard, not just the number of home runs a player hits without regard to the circumstances, or the 4.9% average rate increase (GRI) a carrier announces that combines all service types and accessorials — not the specific services and surcharges relevant to your business that make effective rate increases much higher.

It is that word — “appropriate” — that has long been a challenge. At best, defining benchmark standards has been like painting a detailed picture with a very broad brush; at worst, it’s been entirely generic — a monochrome canvas painted in the best “average” color.

The parcel application

Like a power hitter, every parcel shipper also is unique. Should a B2C shipper, subject to residential and Delivery Area Surcharges, use cost-per-package benchmarks derived from B2B shippers? Probably not. Benchmarks should be drawn from shippers with similar priorities and constraints.

Benchmarking is about finding the right standard – the best in class for the right class or peer group.

And that’s where big data and data science are redefining what benchmarking is and what it means for parcel optimization as modern computational methods become ever more powerful.

Third-party parcel intelligence providers, with legitimate and sophisticated data science operations that are more than a marketing “me-too”, can make it easier to establish who your peers are and what benchmarks matter specifically to your business.

This marriage of technology and data science creates “high-precision benchmarking” that brings together parcel data insights, machine intelligence, and subject matter expertise.

Today, with the right tools, techniques, and enough high-quality data, analytics can precisely identify the right peer group and, through analysis of this group, determine the right benchmarks.

Parcel carriers know this and invest millions of dollars into operational initiatives that rely on data collection and analysis. UPS alone reports investing $1 billion each year into solutions like its ORION (On-Road Integrated Optimization and Navigation) program. And they’re using your parcel shipping data to forecast and decide which rates to increase – and by how much. And they're using your shipping data to negotiate your contract to their benefit.

So, back to that question I posed in the first sentence: Are you reading the story your parcel data tells?

Travis Rhoades is the Director of Data Science at VeriShip, the leader in parcel intelligence. His team uses Big Data and Data Science to produce valuable, actionable intelligence that empowers companies to optimize their parcel shipping operations. He can be reached at or 913.933.3541.