3 Ways Data Can Be Better Utilized to Predict the Market Rebound

The commercial investment in data and data science is skyrocketing, and rightly so. Uncertainty will always be a given, especially in a cyclical industry driven by the whims of consumer spending, geopolitics, and weather. The hope is that data will minimize risk and increase confidence in operational planning and decision-making.

For shippers pursuing the typical annual bid cycles to set transportation budgets, gathering freight rate predictions based on macroeconomic data aggregates is becoming incredibly common. But this type of top-down data approach isn’t producing the accurate results that transportation leaders need to maintain predictable rates, because the financial institutions are using data that focuses on big-picture aggregates, rather than real-time transactions.

Here are three ways to move your data strategy from top-down to bottom-up, from near-time to real-time, and gain an edge when building your transportation budget. 

1. Detect market trends early with a granular view of loads and capacity

Having a direct and real-time view of load and capacity granularity allows for the detection and tracking of smaller trends and patterns before they become visible to the outer world. This perspective is crucial for observing and acting on the momentum of market shifts that may not be immediately apparent at a macroeconomic level. By analyzing millions of data points daily, patterns and trends can be observed before they become visible to the broader market. This early detection enables businesses to anticipate changes in carrier performance and market dynamics. For example, by analyzing implied margins at lane level and tracking cost trends and average carrier bids, data scientists can detect early trends that determine future profitability. 

2. Adapt models according to market environments

Data alone will not suffice, and neither will set-it-and-forget-it models. Because market environments change and seasonality impacts freight rates and lanes, data scientists should remain agile when building forecasting models. To forecast individual U.S. lanes and provide a detailed and accurate prediction framework, you must consider not only model performance, but model suitability, as different models perform better in various market environments. Make sure you’re using the right machine learning “lens” for each market configuration. Remember: data isn’t a magic eight ball or a perfect prediction of the future, but can provide a trust-worthy framework for risk and opportunity.

3. Map out high-risk areas and actionable opportunities

Granular-level data analysis can help shippers identify which lanes are at the highest risk of price and capacity slippage, since capacity isn’t going to leave the market uniformly. This information is crucial for shippers to mitigate potential risks and capitalize on emerging opportunities. Understanding where trouble may arise as seasonality kicks in and rates spike allows for proactive measures to be taken to protect against market volatility.

For many shippers, access to data equals the opportunity to control the uncontrollable. But not all data is real-time, and not all data scientists are skilled at building responsive models. By leveraging data in these three ways, businesses can make informed decisions and navigate the complexities of the market rebound with confidence.

About the author:

Jonathan Salama is CEO and co-founder of Transfix, which combines enterprise-grade, machine learning technology with software with supply chain experts to offer long-term strategy and capacity planning for shippers.

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