How Shipping Data Science Improves Profits

Sunday mornings in my neighborhood are pretty quiet. The other day I walked outside and noticed something I hadn’t noticed before. A United States Postal Service (USPS) truck was making its way through the neighborhood delivering packages. Any other day of the week, this wouldn’t seem out of place, but a Federal entity is working on a Sunday?

It has been several years since Amazon established their “Negotiated Service Agreement” with the USPS for Sunday delivery. Amazon has a clear objective of doing next and same day delivery a standard across the industry – and they are marching toward that objective at a surprising pace. In fact, they currently have the ability to address greater than 25% of the US population with “Next Day” shipping service – and a good portion of that same population has “Same Day” service options available.

So in this new near “real time” delivery paradigm, what kind of consumer is Amazon creating? The answer is a very impatient and entitled consumer who expects an instant product at the end of the supply chain. Amazon’s success in meeting these elevated consumer expectations isn’t based on a “hunch” or “gut feeling.” Rather, empirical data science drives every move they make. What does that mean for the rest of us? To compete and give our customers an “Amazon-like” experience, we need to operate in the same fashion – leveraging business intelligence and data science to drive and optimize our businesses.

How to Capitalize on Shipping Data Science

Shipping data must be visualized and analyzed to uncover opportunities, introduce efficiencies, and realize cost savings without impacting your end customer. Companies that successfully adopt a data-driven shipping strategy experience an average savings of more than 8 percent, while reducing delivery times and providing a better experience for their customers.

A shipping optimization solution should have the following capabilities:

  • Dashboarding. An executive audience has a short attention span – Engaging, interactive charts, graphs, heat maps, etc. ensure that business metrics are “front & center.”
  • Analytics. Deep dive analysis and ad-hoc reporting capabilities provide your operations team with an ability to dig deeper and uncover additional trends and opportunities.
  • Availability. Shipping data science tools need to be accessible 24 hours a day to make optimal operational decisions in the way of direct marketing.
  • Automated Intelligent Decisions. Human decisions are often costly, and shipping with priority services may not be necessary to meet an SLA. Decisions on how to ship should be automated based on customer-defined business rules or geofencing.
  • Benchmarking Carriers. One carrier compared to another for individual products or locations can be cheaper. Data must be gathered about delivery times and cost, aggregated and presented in a visual manner so that metrics can be analyzed.
  • Predictive Modeling. Analysis of “What if” scenarios using shipping data science can help your organization avoid costly decisions and plan for the future.
  • Auditing & Dispute Reporting. Keep the carriers honest and recover fees for inevitable mistakes and service failures.

Adopting a strategy and solution for shipping optimization will introduce efficiencies, and drive more profit in your business. Companies that can do this successfully will be able to thrive in the “Amazon” world and consistently deliver against the expectations of the “Impatient Consumer.”

Coby Nilsson

About the Author

Paqtrack is changing the way companies analyze and optimize their shipping spend. Their platform leverages enterprise level technology, big data experience, and a scientific approach to empower our customers. Paqtrack’s cloud-based suite of analytics and optimization tools drive supply chain/logistics efficiencies and bottom line savings for customers.

Coby Nilsson is the CEO and Co-Founder of Paqtrack and can be reached at:
Website: www.paqtrack.com
Phone: (385) 419-2424
E-Mail: coby@paqtrack.com

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