Tender Freight Indexes

The Waterfall Theory of Freight was a driving force behind the research in, and creation of, the proprietary freight indexes inside of SONAR. Users get access to the most comprehensive and largest set of tender transaction data on the planet, most of which is less than 24 hours old.

Tenders are actual load offers, which is why they are far superior to any other indicator of health, demand and capacity in the trucking and freight market. Tender indicators lead spot rate movement and by tracking them, you can forecast spot rates, capacity and demand. Economists and analysts can use tenders to monitor the state of the economy and build forecasts for economic health and activity. 

Since a tender is an offer for a load, a tender is normally sent electronically through an EDI or API from a shipper to a carrier. The carrier is not obligated to accept the offer and can reject it through another electronic message from its TMS. If a carrier accepts a load, it is obligated to perform service on the load. If a carrier rejects a load, it is refusing the offer from the shipper.

By monitoring tender activity for the entire market, SONAR users have an exclusive view of the freight market, well before everyone else. Speed is not the only thing special about the tender data – so is an exclusive view of actual loads in the market. Since tenders are based on real transactions and not load board posts or lookups, tender data is far superior to any data in the freight market today. If tenders are volatile in a market, rates are likely to be as well. 

In fact, one of the most volatile areas of trucking – reefer truckload – has significant swings in tender rejections around various produce seasons. This tends to show up in the reefer rates in the truckload market. 


When we introduced SONAR, we knew the best way to predict spot rate movement was to watch the contract trucking market and carrier activity in that market. After all, the largest carriers tend to bid on loads in formal bids given by shippers and are given first right of refusal on a set number of loads for a given lane. Shippers tend to prefer the larger enterprise carriers and will give them preference in their bids and in the routing guides.

Tender rejections are an electronic refusal of an actual load sent from the shipper to the carrier. Basically, if a carrier rejects the load from the shipper, it is showing it has other options and is not willing to accept that load. It can also mean that the carrier does not have available capacity for that specific load.

There are many reasons carriers reject loads: the carrier thinks the rate is too low;  the carrier’s capacity is allocated to a higher priority shipper; the carrier stopped servicing a given lane; the carrier doesn’t like the load characteristics (destination, origin, driver unload); the carrier has found a higher rate from a spot market load; the carrier found a higher contract rate on an ongoing commitment.

Regardless of the rejection reason, one thing is clear – the carrier is communicating that it isn’t willing to take the load offer. With information from a single shipper or carrier, we aren’t going to learn about the state of the market. It is also hard to look at a single shipper or carrier’s rejection rates and draw conclusions about the state of the trucking market or movement in trucking rates. 

But when applied over a large set of carriers and shippers, across an entire market, we can learn a great deal. At scale, the shipper- or carrier-specific anomalies become nearly irrelevant. The fragmented nature of the market is such that a single firm doesn’t have that much impact on others. A shipper that has a high rejection rate on a given lane does not indicate a capacity shortage in the market, but if this is a trend for multiple shippers, it likely means something is structurally wrong with that lane. The issues could be short-lived (i.e. a produce harvest) or long-term. If they are short-lived, rates may trend upward. If they are longer term, shippers will lock in higher contract rates on the lane. 

Things get even more interesting when we look at tender rejections at a market level. When capacity becomes scarcer from an entire market, rejections will increase. This is because in every single market, a portion of the capacity is destination-agnostic. In other words, carriers don’t have a preference of destination for their trucks and are willing to take loads to anywhere. Once this discretionary capacity leaves the market, shippers will scramble for trucks and will be willing, or forced, to pay much higher rates for trucks. 

Routing preferences in terms of first position to last are often called the routing guide waterfall. The order of carriers in routing guides  is often based on those carriers that can meet the service requirements of the load and then by price. Usually, a shipper will use service requirements as a filtering mechanism to include carriers that can perform on a lane before considering price. But once they have made the initial selection, carrier order is often dictated by the lowest-priced carrier on that lane.

Once the top carrier in the routing guide (in terms of order) rejects the load tender, the second carrier in the guide will be chosen. If that carrier also rejects it, the shipper will go to the third position and so on. If the routing guide completely breaks down (all carriers listed reject the tender) then the shipper will often go to the spot market to secure capacity. This freight will end up being offered to a select number of brokers the shipper has chosen to do business with.

This is the reason that load board-driven freight data is heavily skewed and you see extreme flips in the load-to-truck ratios that are not truly representative of market conditions. In order to truly understand market activity, you should look at actual loads that have been tendered and avoid using load board data alone.

To describe this process of how tender rejections predict rates in a market, we created something called the Freight Market Waterfall Theory. 

Freight rates are dictated by routing guide compliance. A shipper that achieves nearly 100% of routing guide compliance will continue to optimize its spending by taking advantage of lower-priced carriers. If a shipper sees compliance in its routing guide break down, it will be forced to buy capacity in the spot market, often at higher rates.  

If a market is decelerating (tender rejections are falling), spot rates are likely to fall until they hit the market floor. 

The market floor is equivalent to the collective operating cost of carriers. At this point, rates are unlikely to fall further.  

If a market is flat (tender rejections are near zero), in the short term there will be continued downward pressure on spot rates until rates hit the market floor.

If a flat market continues for more than a few months, contract rates are likely to fall towards the market floor. 

If a market is strengthening (tender rejections are increasing), there will be upward pressure on rates. There is no ceiling on rates, but if rates stay high for an extended period of time, new capacity will enter the market.

SONAR uses the Waterfall Theory to create Predictive Rates.

SONAR tracks routing guide compliance by tracking tender rejections and other data to determine if routing guides are likely to break down in the near future. This data is then compiled and compared against other data sets, including financial and operational data from hundreds of carriers, brokers and shippers. 

Using data derived from hundreds of operating and financial metrics of over two hundred200 carriers, we calculated the average operating cost of carriers across the market. We then backed this into the “base rate” across the market. The base rate is the cost of what it takes to operate a truck in the market.  

Then we built an algorithm that multiplies the base rate against the tender rejection data to get the current market condition rate, using historical market spot rates. 

The market condition rate is then trained against the HAUL index to allow for individual market conditions. If a market has a negative HAUL value, it is considered a backhaul market and rates can fall below the base rate. If a market has a positive HAUL value, it’s considered a head-haul market. 

The rate for any given lane is determined by both the origin and destination; the price of a truck has as much to do with the attractiveness of certain destination markets as it does with the availability of trucks in a given origin market.

Adjusting the base rate by conditions in those markets gives the FreightWaves data science team a scientific model of “today’s rate” by origin/destination pair.

Today’s rate is then forecasted out a year by looking at the historical rates and future direction of the market, using SONAR data from thousands of sources. The rates adjust for seasonality variations in the model and other financial and operational components. 

The model also becomes smarter over time as more data is fed into it. Significantly, our models for each market interact with each other, so a surge in volume in Atlanta, for example, will affect how we think about the availability of capacity out of Macon. 

SONAR can help you monitor the market.

With SONAR you can track volumes, tender rejections, market share, inbound to outbound load ratios, lead time, and so much more. Data has been sliced up in hundreds of additional granular details like equipment type and length of haul, allowing users to get a never-before-seen perspective of actual, near-time activity.  

With SONAR's proprietary data, users can see where the market is headed before it gets there. No more playing from behind. No more shooting from the hip. For the first time in history, you can see the impacts of actual freight movement in less than 24 hours. Plus, we have built all sorts of volatility measurement indicators and tools that look for unusual and inconsistent movement of activity in the market. 

Track the markets that need capacity and get your trucks there to capitalize on the opportunities. Recognize the supply and demand imbalances, giving you an opportunity to lower your shipping costs.

Tender Rejections

A tender rejection is a refusal by a carrier of the load offer from a shipper. Tender rejection indexes measure the percentage of tenders that were rejected. These can be broken down by region, market, equipment type requested, length of haul, mode, etc. A low rejection rate will indicate that carriers did not have better options than the loads being offered. A high rejection rate will indicate that carriers had better options than many of the loads being offered. 

The length of haul indices are broken down by the following segments: 

  • CTRI (“City”) Tender Rejection Index: CTRI: 0-99 mile length of haul
  • STRI (“Short-haul”) Tender Rejection Index: STRI 100-250 mile length of haul
  • MTRI (“Mid length”) Tender Rejection Index: MTRI 251-450 mile length of haul
  • TTRI (“Tweener”) Tender Rejection index: TTRI 451-850 mile length of haul
  • LTRI (“Long-haul”) Tender Rejection Index: LTRI: 851+ mile length of haul

SONAR also breaks out loads by what type of equipment was requested in the tender. These indices include van, reefer, flatbed and intermodal.

Weighted Tender Rejection Index

On a market level, not all rejection rates are created equally. For example: a 10% or 1,000 basis point OTRI increase in the Bismarck, North Dakota market is not as significant as a 1% or 100 basis point change in the Atlanta market. 

In this example, Atlanta represents a much larger portion of the national load volume than Bismarck, meaning fewer loads need to be rejected to create large changes in rejection rates in the smaller market. Identifying the most relevant markets by rejection rate change on a heat map or watchlist can be cumbersome because smaller markets have higher levels of volatility that exaggerate the scale. Combining volume with rejection rates into one number makes it easier to identify the most significant capacity shifts in the U.S.   

The weighted rejection index (WRI) combines market share and weekly change in outbound rejection rates. This value combines market size with rejection rate changes, which enables the user to see which markets are most relevant from a capacity change standpoint. 

The formula is Outbound Tender Market Share (OTMS) x Outbound Tender Rejection Index Weekly Change (OTRIW) = Weighted Rejection Index (WRI).

For example: Atlanta tender rejection rates moved from 5% to 6% over the last seven days while currently accounting for 4% of the U.S. outbound load volumes. The calculation would be: 4% x 1% = 4 WRI.

By contrast, the smaller Bismarck market moves from a 6% to a 16% OTRI over the course of a week and accounts for .01% of the total U.S. outbound load volume. The WRI would be: 10% x .01% = 0.1 WRI. The WRI makes it easier to see significant capacity changes on maps and watchlists while simultaneously signifying the impact of each market on national capacity over time in a chart.

Tender Volume

Outbound tender volume indices (OTVI) are built to help market participants understand how market volumes are changing over weeks, months, seasons and years. They are available in all 135 markets and track the total amount of trucking freight transactions in a given market on a daily basis. Subscribers to SONAR get the volume indices included in their standard subscription. 

The index values are set for the volume on March 1, 2018, relative to the entire U.S. market, but change daily based on the relative movements to that reference date. 

SONAR’s Tender Volume Index is based on the volume of accepted tender volumes on a given day. The data is updated daily and is organized by inbound (ITVI) and outbound (OTVI) volumes for the U.S. and regional market granularity. Existing tender volume data is being enhanced to now include the following granularities:

  • Length of haul volumes 
  • Equipment type volumes
  • Region to region volumes

Length of haul volumes will give insight into what type of volumes are moving through the U.S. and Canadian freight markets because not all loads are created equal. Longer length of haul has a greater impact on regional capacity than lower mileage loads. It also gives insight into how freight patterns are changing and allows carriers to design networks more effectively. They are divided into the following distance bands, which will be offered at a national and market level:

  • Local – 0-99 miles – COTVI
  • Short – 100-250 miles – SOTVI
  • Mid – 251-450 miles – MOTVI
  • Tweener – 451-800 miles – TOTVI 
  • Long – 801+ miles – LOTVI

Equipment type volumes for both reefer (ROTVI) and dry van (VOTVI) loads will also be offered at a national and market level granularity, better enabling SONAR users to see what type of demand is driving the fluctuations in market capacity. 

Region to region volumes will now also be offered by equipment type and length of haul organizations enabling the user to see where volumes are moving at a high level.  

What is HAUL?

HAUL measures the likelihood of being able to find a load out of a market, which makes its value obvious to any asset-based carrier. With many load planners operating on a 48-hour cycle, knowing when a market is hotter or colder on a daily basis can lead to better asset utilization. On the transactional side of the business, having an early indication of capacity changes can lead to higher margins. 

The simplest explanation of HAUL is the greater the value, the easier it will be to get a load out of the market. Conversely, the lower the value, the harder it will be to get a load out of the market.  

HAUL is calculated by subtracting the inbound tender volume index (ITVI) value from the outbound tender volume index (OTVI) value. A higher HAUL value indicates the higher likelihood of getting a load out of that market due to the fact there are more outbound loads available in the market than loads entering.

Example: OTVI.CHI is 168.45 and ITVI.CHI is 134.53. HAUL.CHI is 168.45 – 134.53 or 33.92.

The main value of HAUL is not necessarily in the absolute value, but the magnitude of the daily movement up or down. Familiarity over time will create an understanding of what it means to have a high HAUL value versus low. A market like Miami, which is considered the consummate backhaul market, with more loads entering than exiting, have a very low HAUL value. Miami had a range from -65 to -91 in August 2019.  

Whereas the general condition of the Miami market is widely known, there are times where it is easier to get loads out of the area. When HAUL increases this means there are more outbound loads available in relation to inbound, meaning the odds of finding a load out are higher. 

Haul movements can lead tender rejections as it takes time for a carrier to realize market capacity conditions. A big increase in HAUL value is indicative of anomalous volume increases that carriers struggle to fill. The converse, a large decrease, can also indicate a reduction in tender rejections is about to occur as capacity returns to the market. 

Length of Haul by Market

In addition to HAUL, FreightWaves also offers length of haul tender rejection values at a market level. Now there will be another way, in addition to trailer and lane, for users to analyze where the most opportunity or threat lies. Knowing what mileage bands have the most tender rejections out of a market will allow the user to know what areas carriers are avoiding the most.

An example of this would be if outbound tender rejections in the Los Angeles market (OTRI.LAX) increased from 15% to 18% one day. Typing in each length of haul value shows that the mid-haul tender rejections (MTRI.LAX) increased from 14% to 20%, while the other values (CTRI, STRI, TTRI, LTRI) were flat. This tells you that markets 250 to 450 miles away are where there may be load volume increases and upward rate pressure exists.

Tender Lead Time

Tender lead time (TLT) is the time in days between tender submission and requested pickup date. When this value increases or decreases, it is an indication of a change in shipper expectations and may signal anticipated capacity changes. This index has now been greatly expanded to be more in line with other tender volume indices. 

The TLT index includes both inbound (ITLT) and outbound (OTLT) organizations along with all the length of haul and equipment type granularities where volumes permit statistical relevance. SONAR users are able to see shipper behavior when submitting requests for dry van (VOTLT) and temperature-controlled (ROTLT) loads along with variations in length of haul lead time submissions. These indices are offered at the national, state and market level. 

SONAR provides tender data on the following: 

Tender rejection: A tender rejection is a refusal by the carrier of the load offer from the shipper. Tender rejection indexes measure the percent of tenders that were rejected. These can be broken down by region, market, equipment type requested, length of haul, mode, etc. A low rejection rate will indicate that carriers did not have better options than the loads being requested. A high rejection rate will indicate that carriers have better options than many of the loads being requested. 

Tender lead time: The time in days between the original load tender and the first pickup time and date. 

Tender market share: The percent that a specific market is of the entire national market, during a specific time period. 

Tender volume: The total volume of tenders in a given market. Volume can be described in an index or actual load count. 

Headhaul index: Shows the balance between outbound versus inbound loads in a market. A high positive headhaul index suggests that the market has more freight available than trucks. A low negative headhaul index suggests that the market has less freight available than trucks. 

Trucks in market (TRUK): An index that tracks how many trucks are in various markets, compared to April 1, 2018. Any growth of trucks in that specific market over April 1, 2018 will show an increase in the index above 100. If a market has fewer trucks than April 1, 2018, the index will be below 100. Any value above or below represents a percentage increase or decrease in the amount of trucks moving in a market on a given day. All the changes are made historically, meaning the methodology has been implemented over the entire data set. Note: TRUK data comes from telematics devices and ULSD consumption vs. tenders. 

Example: TRUK.GSO has a value of 117 on September 23, 2018. This means there were 17% more trucks in that market than there were on April 1, 2018.

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