What is the Cost Change metric?

The cost change metric is designed to quantify how your rates have evolved over time in comparison to the market at large, which includes all contributors. This metric is visually represented in the Benchmark Analytics dashboard, where you can find two primary colors in the legend:

  • Purple: Signifies the customer's or your loads.
  • Gray: Represents all market contributors.

This differentiation allows for a clear understanding of how individual rates fluctuate over time in relation to the market's overall performance.

Understanding the Dashboard

The dashboard provides a dual view:

  1. Bar Chart: This component answers whether your rates are higher or lower compared to others by aggregating benchmark data. It does so by comparing your load on a specific date against the market at that same moment. However, it doesn't capture market trends. 
  2. Trend Analysis: Unlike the bar chart, the trend analysis vividly illustrates the market's boom and bust cycles, reflecting the tightening and loosening of capacity over time. It uses the 2016 index as a baseline for measuring change.

The 2016 Index

The index is based on a full-year model from 2016, with the following characteristics:

  • All Contributors (Gray): Ideally, these rates hover around the 0% line, indicating a balance where inputs equal outputs.
  • Your Rates (Purple): Shows how your rates have changed over time in comparison.

Practical application: The Pandemic date range

A compelling use case of this analysis is examining rate changes during the pandemic. For instance, in May 2020, the rates were 5.7% higher than the 2016 baseline. By May 2021, this had jumped to 32%, highlighting a significant inflation rate within a year.

Why use the Cost Change metric?

This metric is preferred for its ability to control variables that other metrics can't, such as distance and directional mix. This is particularly useful at more granular levels, such as lane or carrier analysis, where year-over-year comparisons might be skewed by changes in average distance covered or load type.

Example: carrier analysis

When analyzing carrier performance, drastic year-over-year changes in the average distance covered can heavily influence rate per mile or cost per load, rendering those metrics less reliable for measuring true cost inflation. The cost change metric, by contrast, offers a more accurate comparison by normalizing these variables.