The Five Anchors of Ocean Shipping Revenue Management

In this article, Matt Jacoby, Strategy Director and Michael Bentley, Partner at Revenue Analytics explain why the five anchors of Revenue Management are essential to ocean shipping.

The ocean freight industry is at a tipping point. Consolidation and major industry pain points such as overcapacity and reactive pricing are forcing the industry to move away from legacy ways of thinking and begin forging new modes of operation that generate profitable growth.

Never before has this industry so desperately been in need of predictive analytics and data-driven insights. Some shippers are starting to grasp the potential of predictive analytics, which will help to transform their daily operational decisions into an opportunity to grow revenue and profits. Fortunately, this focus on driving profitable growth is occurring at a time when the amount of applicable data is greater than ever. From tracking containers to monitoring competitive pricing and demand, a copious amount of information is available at the click of a mouse. For an industry that has not always been data-rich, this is revolutionary.

Although revenue management is a competitive advantage at present, it will soon become a competitive imperative. And, with this wealth of data at their fingertips, ocean freight companies would be foolish not to rethink how they are using this information to better understand their customers and make better decisions, operationally and commercially. To capture growth and stay afloat, ocean freight companies must harness the power of predictive analytics and data-driven insights in each of the five anchors of ocean freight commercial decision­making: pricing, demand, capacity, network, and their customers.

Each of these anchors are connected and yet independent. If you are lacking the insights needed to maximize them all, you will fall behind. But, by eliminating the unknowns across each of these five anchors, you will gain a competitive advantage.

First, establish your pricing strategy. As capacity continues to increase and the industry continues to function as a commodity, it is increasingly necessary to implement precise and timely pricing to facilitate revenue growth. If you are not thinking about pricing daily, you are already behind. But, scalability of pricing points is not feasible without technology and analytics. With millions of data points to review, a level of technical maturity is needed to review it all or you risk hiring an army of pricing managers. As with any manual process, the lack of automation will yield suboptimal pricing results.

Accurately forecasting total potential demand, known as unconstrained demand, is also critical to an optimized network. In order to become data-driven, establishing business context is the key to forecasting demand, and automating and streamlining processes is required to calculate demand at a high frequency, accounting for all variables.

This is certainly a challenge, from a data and mathematical perspective. Computational constraints, or the rate at which these mathematical models can be calculated, need to be solved for by optimizing forecasting methodologies and defining a level of forecasting that is appropriate for the given business needs. Although this task can be complex and intensive, without an unconstrained demand forecast, it is impossible to know when and where capacity will be required to satiate network demand.

However, forecasting demand isn’t the only challenge. Measuring capacity and consumption in real-time is daunting. There are multiple systems and complex mappings used to keep track of this information, making integration difficult. However, the lack of real-time transparency limits the ability to make informed decisions at levels that truly impact the bottom line. Insights into available free-sale, committed/contracted, partners, internal allocations and empty containers are necessary to dynamically adjust decisions as network demands shift.

The ability to view expected available capacity early in the bookings process allows changes to be made to optimize yield. Understanding customer behavior, including no-shows and cancellations, allows you to calculate how much you should book above vessel capacity to improve utilization and prevent service denials.

After evaluating your approach to pricing, demand and capacity, consider how each of these levers impacts the network. You must account for available capacity, unconstrained demand and pricing to have an optimized network. But how? And, how can you route cargo across the network to maximize profitability?

There are millions, if not billions, of permutations. As computational tools are delivering data and providing recommendations, you must ensure decision-makers are taking those recommendations into consideration and adopting them, rather than allowing individual performance targets to drive their choices. Put guardrails into place (ex. only allowing a +/-10 percent difference on pricing recommendations) and monitor performance to create a cycle of continuous improvement.

Finally, don’t settle for an outdated approach to customer segmentation. Your legacy approach may be sub-optimal, leaving potential volume and profits on the table. Relying on archaic business processes and the same one-size-fits-all solution utilized by much of the industry is a huge mistake. Understanding customer demands and behaviors allows you to be prescriptive rather than reactive. Deep insights into Customer Lifetime Value contribute to informed decisions and allow you to identify which customers drive the most value for your company.

Those informed decisions for long-term contracted customers will then result in higher yield over the span of those contracts. Plus, there are minimal to no switching costs for customers. It’s logical that providing them with the products they want at current market rates will encourage loyalty and repeat business.

To find solutions that optimize all five anchors and drive organic revenue, you must rethink how each organizational function utilizes data to understand the interplay between the Five Anchors. Reconsidering this data usage will make it easier to sustain a measurable competitive advantage by combining enterprise data, predictive analytics and prescriptive Revenue Management.

But, the work doesn’t stop there. Unlock the value of enterprise and external data by identifying the factors that drive market behavior and financial performance. Once you identify those factors, you can unleash prescriptive algorithms and machine learning to optimize decisions in response to market changes and operational challenges.

To be successful in the new digital age of ocean shipping, you must use data and insight-driven solutions to optimize your decisions across every function of your business. By taking this holistic approach, you can better understand how each Anchor of the ocean freight industry is connected and how to optimize your commercial decisions leveraging data-driven insight across them all.