The only thing predictable with the freight market today seems to be its unpredictability. Before COVID-19, there was some ability to foresee highs and lows in freight volume based on seasonality and historical trends, adjust available capacity, and anticipate rates to be paid. That’s no longer the case. Seemingly overnight, volumes go from all-time highs to record lows and rate structures respond accordingly. Yesterday’s readily available carrying capacity quickly becomes a lack of empty trailers today.
Several factors complicate the pricing scenario for carriers. There’s been the need to operate more maintenance-intensive older equipment while new truck and trailer production slowly returns to normal, the ongoing driver shortage, and still historically high prices for fuel. Like never before, analyzing freight trends and predicting rates needs to be proactive.
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“The most effective rate benchmarks are rooted in transaction data, actual prices paid to the carrier and not bids or indexes,” Croke continued. “Additionally, granularity is important, so look at the depth of the dataset. Is it overly weighted by any one equipment category, region, or type of freight? Is there a history of rates spanning a number of freight and economic cycles? Do you have the flexibility to produce benchmark rates averaged over different periods of time?”
Rates averaged over the short term can indicate which direction prices are moving so you can negotiate with brokers more effectively, Croke added. “Long-term benchmarks can help you forecast rates and give shippers a well-reasoned, indexed, competitive price,” he said.
Carriers 'need to know their numbers'
Carriers need to know their costs or “they need to know their numbers” to see if the freight they are accepting is profitable, explained Brent Hutto, chief relationship officer at Truckstop. At the same time, data analysis can be highly beneficial when you leverage information to your advantage. For example, when you know how many available loads and trucks are in a lane, you know your negotiation strength against the rate to offer and accept on the load.
“Technology can provide broad market-rich data for freight that includes market rates,” Hutto continued. “Combining that with rate data from additional partners produces a predicted rate for that day/time within the lane and by the type of freight. It also provides a high and low range of rates that have been accepted and a confidence index based on how many rates have been analyzed.”
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Chris Oliver, chief marketing officer at Trucker Path, summed it up this way: “Carriers want to fish where the fish are biting.” Using freight volume and rate data, Oliver said, can help identify geographies that have attractive primary and backhaul rates to maximize earnings. Rates are a direct reflection of volume based on the number of available trucks in each market, he said.
“If carriers understand that dynamic, they can use it to their advantage to identify markets that have more outbound volume than inbound volume to give them better negotiating power,” Oliver added. “Typically, you will see a head haul paired with a backhaul and average the two rates. Then you can compare that to your costs to analyze profitability.”
Successful data classification will allow machine-learning models to better understand rate differentials, noted Criss Wilson, a data scientist at McLeod Software. That categorization will feed algorithms for better estimating what a rate should be today, next week, next month, and next quarter, Wilson said.
“That type of rate forecasting is an example of predictive analytics, but rate analytics can be difficult to interpret,” Wilson explained. “Often, the best way to use the analytics is in a graphic that speaks to the user on an intuitive level and when the analytic is injected by a system into a point of decision in the workflow for quoting a rate. That way, it’s most easily consumed if the math is already done, and users don’t have to whip out a calculator or start plugging numbers into spreadsheets.”
Wilson also pointed out that rate data can be used to compare shipment pricing in specific markets. When carriers understand where the market sweet spot is, they have a better chance of offering rates that shippers will accept, enabling growth, he noted. They also can see where their profit margins over or underperform a market pair average. And knowing the number of trucks in a market compared to the number of tenders allows machine-learning models to improve rate predictions by understanding market dynamics day by day.
“That is an example of diagnostic analytics,” Wilson added. “Having rate analytics at the point of decision for the people who are making load and order decisions all day makes the ROI for an investment in analytics immediate and substantial.”
This is the first of two parts on how shipper and carrier transparency can help both provide better service and how being "truck friendly" can give shippers an advantage in tough freight markets. Read part two.
