• ITVI.USA
    17,113.070
    186.890
    1.1%
  • OTRI.USA
    28.200
    0.000
    0%
  • OTVI.USA
    17,079.400
    184.170
    1.1%
  • TLT.USA
    3.090
    0.190
    6.6%
  • TSTOPVRPM.ATLPHL
    2.630
    0.060
    2.3%
  • TSTOPVRPM.CHIATL
    3.080
    -0.090
    -2.8%
  • TSTOPVRPM.DALLAX
    1.180
    -0.060
    -4.8%
  • TSTOPVRPM.LAXDAL
    3.210
    -0.070
    -2.1%
  • TSTOPVRPM.PHLCHI
    1.630
    -0.090
    -5.2%
  • TSTOPVRPM.LAXSEA
    3.360
    0.070
    2.1%
  • WAIT.USA
    121.000
    1.000
    0.8%
  • ITVI.USA
    17,113.070
    186.890
    1.1%
  • OTRI.USA
    28.200
    0.000
    0%
  • OTVI.USA
    17,079.400
    184.170
    1.1%
  • TLT.USA
    3.090
    0.190
    6.6%
  • TSTOPVRPM.ATLPHL
    2.630
    0.060
    2.3%
  • TSTOPVRPM.CHIATL
    3.080
    -0.090
    -2.8%
  • TSTOPVRPM.DALLAX
    1.180
    -0.060
    -4.8%
  • TSTOPVRPM.LAXDAL
    3.210
    -0.070
    -2.1%
  • TSTOPVRPM.PHLCHI
    1.630
    -0.090
    -5.2%
  • TSTOPVRPM.LAXSEA
    3.360
    0.070
    2.1%
  • WAIT.USA
    121.000
    1.000
    0.8%
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Commentary: Data sharing offers limited customer service

Trust between contracted parties is vital

The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.

To shine light into the metaphorical black box where transporting items from point A to B takes place, reliable data is necessary to understand what is happening over the network as time passes by. As a physical activity subject to time, and taking place over a defined route, for-hire transportation services are quite measurable. When data is generated, it makes sense that it is shared among decision-makers. This includes consignors, consignees, for-carriers and other intermediaries. Of course, when it comes to assessing customer service in transportation, data is certainly necessary — but it is not sufficient. Customer relationships and the nature of the contracts that the parties enter into will make or break the perceptions of customer service.

Simply put, customer service is the process of achieving a win-win outcome between a buyer and a seller. A win-win helps build relationships beyond the merely one-off, transactional type. If successful, the buyer is receiving fulfilment while the seller is building loyalty, which may enhance predictability of customer demand and, therefore, sales revenue. Of course, sellers must take care not to overpromise the scope and quality of customer service. This helps keep relationships and expectations sustainable.

Customer service is part of perceived quality. Yet quality measures can be either objective or subjective. Fuel efficiency and carrying capacity are objective measures of service capability since they are governed by the laws of physics. The type of fuel to be used (e.g., diesel vs. electric battery) and how the capacity is to be allocated (e.g., truckload or less-than-truckload) are subjective choices. Different people may make different choices because they have different valuations of the alternatives.

All the data in the world, finely honed into actionable statistics and scenarios by machine learning techniques, mean little if the contracted parties do not trust each other and they try to get around their obligations. For-hire transportation is an intangible service as opposed to a tangible item. Consignors and for-hire carriers can, and ought to, come to an agreement as to what a win-win outcome looks like before signing the bill of lading. Part of that win-win will be defined in terms of price, meaning value for money. If the scope and quality of the customer service being sought make the freight rate appear too high for the consignor, the transaction may not take place.

With an avalanche of data being generated daily in transport markets, some companies are looking to leverage it in order to enhance their services. The founders of Uber Freight recently launched a new company, ISO. According to the company website, their services reconcile “data discrepancies between business partners in real time” and contextualize “service failures and create actionable insights.” Their single cloud-based platform is intended to give all parties broader access to what is happening in the black box. Of course, it is assumed that all parties will share their data and, equally importantly, their performance metrics. After all, apart from homogenous data, one ought to have insight into what another party thinks the data means by way of the key performance indicator it is generating. For example, when is a shipment on time? When the truck pulls into the consignee’s yard or when the truck is parked at the dock and is ready for off-loading? Without agreement, the situation is like two people looking at a Rorschach inkblot — they are looking at the same thing but seeing two different things. This is the challenge when taking digitization to the next level.

Data transparency and visibility across different silos is a good thing. But mistakes can happen in transportation. A none-too-rare example involves delivery to the wrong destination. If the for-hire carrier’s dispatching department transmitted the wrong consignee’s information to the driver, the delivery will not only be late but potentially lost or damaged if unwittingly received. The dock personnel receiving the delivery and, indeed, the driver have little incentive to wade through the bill of lading to determine if the delivery is in fact a normal one for them or something is in error. Of course, the terms of the bill of lading confer on the for-hire carrier the responsibility to make the consignor whole when the latter tries to smooth things over with his customer (i.e., the true consignee). Fixing the mistake is still customer service — but it is akin to a contractual warranty for damages and defects.

Data sharing to enhance customer service is relatively easy for freight moved under service contracts. Here there is an expectation of a stream of consignments over a period of time. Relationships tend to be stronger as well. Things do not work as smoothly in the spot market for single shipments, especially when using digital freight matching platforms (e.g., Uber Freight, uShip, etc.). Since there are multiple proprietary providers of such platforms, the data is siloed. Consignors and for-hire carriers are, therefore, incentivized to post their requests on multiple sites in order to increase the chances of a quick match (much like being on multiple online dating sites). Once a match is made on one platform, the others may still show availability which, of course, is no longer accurate. To the extent that these “off-the-market” players are chased by others, it leads to inefficiencies in route planning, capacity utilization, etc. Until these independent platforms agree to establish a clearinghouse like those operated in banking and stock markets, this problem will continue. Clearinghouses would allow all platforms to “clean house” once a match has been made at one of them. Blockchain technology may add extra layers of security to this process as well.

Data accumulation and its usage are transforming transportation markets. This is a good thing. However, there is still much to learn as the parties try to pull a win-win out of the metaphorical black box they find themselves in. Providing consistent and reliable customer service is one of the most nebulous exercises in transportation management.

Click here to see other commentaries by Darren Prokop on American Shipper and FreightWaves.

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Darren Prokop

Darren Prokop is a Professor of Logistics in the College of Business and Public Policy at the University of Alaska Anchorage. He received his Ph.D. in economics from the University of Manitoba in 1999. Prior to his academic career Darren Prokop worked in government as an economist and in the private sector in inventory planning.

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