Recommendation platforms and their role in optimization solutions for truck and rail freight movement
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Have you ever been on Facebook and noticed a segment suggesting “people you may know”? How about shopping on Amazon or eBay and seeing products like what you would typically be interested in? Ever wondered how everything seemed so personalized and catered to exactly what you were looking for? Well, the explanation is simple. Recommendation platforms and systems.
With the emergence of cloud computing through big data, recommendation platforms and systems have grown exponentially in recent years. Driven by the need of businesses to better understand customer/consumer behavior, recommendation systems collect historical data about user behavior. Using this information, these systems have the ability to determine and gain insight into factors like the types of goods and services a particular customer would be interested in, the time of day of his/her greatest activity, and other tendencies. This monumental amount of data gives businesses the ability to, as the name suggests, recommend additional and complementary goods and services to customers.
As a part of the big data revolution, companies and organizations are using information to better understand trends and patterns that contribute to the development of customized customer-focused approaches. While recommendation systems have amplified the business processes and approaches of companies, they have also provided customers with superior oversight of the goods and services available to them. This enables them to make choices that are within their range of requirements and in their best interests.
While companies in retail and media are the sectors that have dominantly utilized this technology, recommendation platforms have also been adopted in a wide array of industries, including truck and rail freight movement, to enhance the sourcing options made available to shippers.
Implementing recommendation platforms in truck and rail freight movement solutions
One significant area in which recommendation platforms are implemented is in rates management and route optimization systems. Shippers use such systems to determine the cost and most feasible way of moving a commodity, or multiple commodities from one location to another. Given the complexity of the North American, particularly US, rail network, these systems have become incredibly valuable.
Rates management and route optimization systems such as ShipX Rates give shippers complete oversight of the routes and costs associated with shipping from the point of origin to the destination. Like platforms with functionality found in Amazon and Facebook, recommendation software in these systems also depend on the vast amount of historical data collected from past transactions and activities.
Here’s how this functionality works with the Rates application:
The moment a shipper enters her/his origin and destination requirements, in order to determine the route and associated costs, the system conducts a thorough evaluation of all the historical data linked to the origin and destination. The Rates application provides the user with multiple sourcing options, with the recommended choice highlighted.
What is most significant here is that the application does not just factor in one mode of transport; rather, it consists of a rail, truck, barge, and warehouse combination. Supported by regularly updated rates, the application provides shippers with a comprehensive view of the multi-modal sourcing options available to them, effectively allowing shippers to make the most ideal decision.
With freight movement expected to increase significantly in the coming years and businesses readjusting their operations accordingly, utilizing technologies that organize and use the large pools of data available will be very pivotal. ShipX Rates, with a built-in recommendation platform that comprehensively utilizes data to determine trends and recommend options to customers, is one example that is driving the industry into the future and transforming the entire landscape.
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