Recruiting Outside Your Network: Compounding Margin Erosion in a Challenging Market

Attempting to maintain acceptable margins for a truckload carrier in a normal freight environment is a challenge. Doing so in this current market may seem impossible for most. Building on prior discussions of the importance of freight network discipline, the next step is to focus on the financial implications of recruiting drivers outside a core, profitable network. Unfortunately, it’s common for communication gaps to develop between operations and recruiting/retention teams. This can lead to a mismatch of geographic operating footprints and the home domiciles of drivers.

To begin, let’s review some assumptions, facts, and myths.

Assumptions

Within KSMTA’s client base, the average driver generates approximately $194K in operating revenue (linehaul + fuel surcharge). This is based on 50 weeks of productivity at 2,050 miles and 9.5% deadhead. The current operating ratio for the last six months of all reporting carriers is approximately 98.1%. We believe that the representative peer group (our clients) are predisposed to higher performance and profitability than the average carrier in the industry. So, we believe the implied operating ratio for the rest of the industry is much higher (worse). Based on the statistics above, the average driver in this current market is generating an annual operating profit of $3,686. To reinforce this point, the average driver is generating less than $4K of annual operating profit per year. This is a stark reminder of the razor thin margins and the current state of the industry.

Facts

With most carriers, the geographic footprint of the business becomes the driver recruiting footprint of the business. In this current market environment, these footprints are expanding in order to keep drivers moving. What the recruiting team likely doesn’t know is that expansion of the network is directly correlated to margin erosion. In other words, the practice of recruiting within the geographic network footprint without any analysis of density, profitability, or strategy will actually have a compounding margin erosion effect, essentially becoming an anchor for the business.

Myths

We routinely overlay the network footprint of our carrier clients, with the home domicile zip codes for each of their active drivers. As a follow-up to that exercise, we challenge each client to analyze the financial performance of each of those drivers. This is a relatively easy exercise of calculating the revenue, time, and standard costs related to each loaded and empty trip in a specified period of time. With few exceptions, this exercise reinforces the reality that drivers living outside the carrier’s geographic footprint are less profitable than the average and, due to prolonged spot market depression, any chance of profitability evaporates with each trip back home.

Prior to reviewing the data, the common rationale is that the driver is on the road five or six weeks at a time, so this exercise is not useful. This doesn’t mean that there are not exceptions to this rule, however, they are few and far between. The action item(s) from this exercise are three-fold:

1. Narrow the geographic footprint for recruiting in ONLY those areas of high density and average or above profitability. Using the below example to provide guidance in this area, recruiting activity would be limited to the green or yellow areas.
2. Re-connect initial expectations to reality. If the driver committed to five to six weeks on the road (which is exceptionally difficult to maintain over the long term) and the reality is different, a conversation should be had immediately. If the driver cannot commit to consistently being away from home for a period that will bring them back to average profitability, parting ways with the driver should be considered.
3. Eliminate communication gaps between operations, recruiting, and finance. Every department in a trucking organization is crucial to profitability and viability. Maintaining regular check-ins and analytics on network density and profitability will reduce the disconnects that cause these types of compound market erosions.

Below is a real-world example with a client that further supports our hypothesis: out-of-network drivers, on average, erode potential margins compared to their in-network colleagues.

Case Study

We isolated a group of tractors that generated low or negative profitability over the course of 12 weeks. This exercise allowed us to put a spotlight on specific trips (both loaded and empty) and calculate the resulting margin. In this example, the driver requested time off and was assigned a broker load from eastern Washington to Missouri involving 124 miles of deadhead (DH) miles prior to pickup and 1,908 loaded miles. This initial load resulted in a total margin of $210 and an estimated net profit of -$550. The driver then had a 50-mile DH to return home. The driver’s next trip after time off at home was a 315-mile DH to Kansas to pick up another load destined for Amarillo, resulting in an additional $332 margin and a net profit of -$100.

Over a 14-day period, the total revenue amounted to $4,723 but the total margin was only $542, significantly lower than the client’s average margin of $1,227 for the same time period. Based on this client’s operating ratio for the last six months, the average driver would have generated $390. However, this driver generated approximately -$650 in the same time interval (14 days). Given the potential annual profit for an average driver, this pattern of behavior only needs to occur a few more times to completely eliminate any chance of profitability for that driver.

Optimizing Driver Recruitment

Recruiting truck drivers from outside a company’s geographic footprint often leads to significant margin erosion due to increased operational costs and complexities. Companies must carefully evaluate these trade-offs and consider strategies such as enhancing recruitment efforts within their profitable and dense network. They should also focus on driver retention to maintain profitability and operational efficiency.

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