Rate shopping is one of the core processes of outbound fulfillment. However, traditional methods for rating leave both cost savings and performance improvement opportunities on the table.
In this blog, we’ll detail the most common mistakes that shippers make when rate shopping, and how to correct them.
One of the most significant errors a shipper can make is comparing one carrier's base rate to another's total cost. It's the classic apples-to-oranges problem.
When a carrier API returns a rate, it often only provides the core shipping charge, when the full picture of surcharges, accessorial fees, unique negotiated modifiers all factor into cost comparison. These costs, which include everything from fuel surcharges to address correction fees, can inflate execution costs and negatively impact rate shopping efficacy.
Shippers might select a carrier based on a low initial rate, only to find that the final invoice, packed with fees, was more expensive than a competitor's. These small discrepancies add up fast. In a study of companies with a mix of carriers who don’t return fully loaded rates, we've found that shippers overpay by 6% on average. For an enterprise spending $10 million on parcel shipping annually, that's $600,000 lost.
The Solution: You need a rate shopping engine that can accurately calculate the "fully loaded" cost of every shipment, in real-time, by using your specific contract data (unique rates, discounts etc.) and accounting for surcharges and accessorials charged by each carrier.
Rate shopping using carrier APIs has another key flaw — latency.
Throughput has a huge impact on enterprise shipping operations. Your shipping technology plays an important role here. A platform that relies on making multiple external API calls will only ever be as fast as the slowest carrier response.
Here's a typical scenario:
Your entire rate shopping process is now held up by Carrier C, which can bottleneck your entire operation, slowing down label generation and increasing the time it takes for a package to move through the warehouse. This can subsequently lead to missed delivery cutoffs and missed dates.
The Solution: The most efficient method for multicarrier rate shopping is to virtualize your carrier contracts. Instead of making an API call for every rate request, a robust shipping platform can host all of your carrier contracts and rules in-memory. This enables the system to calculate complete rates with sub-second response times.
Relying on static transit tables is also a common practice. While useful for general planning, these tables do not account for the dynamic, real-world variables that impact a shipment's actual time-in-transit.
For example, a two-day transit time from a static table might work for most of the year, but what happens during a blizzard, a holiday rush, or an unexpected carrier outage? That two-day promise can quickly become three or four days, leading to WISMO inquiries, a negative customer experience, and a lower likelihood of repeat business. Conversely, when you make a promise you can keep, then keep it, the shipping experience contributes positively to the overall shopping experience. Beyond the improved performance outcomes on the operations side, this leads to more satisfied customers and differentiation from competitors.
The Solution: Your shipping technology should use both differentiated data and historical performance to model more accurate transit times. Instead of relying on a carrier SLAs, the right platform can calculate a more realistic delivery date, often using machine learning, for every shipment. This means taking into account factors like current network volume, weather patterns, and carrier service bulletins. Having a more accurate promise dates means that you incorporate the right date constraint when rate shopping. This ensures you're not just picking a carrier based on price, but also on their actual ability to meet a specific delivery date.
This brings us to the next common mistake.
It's easy to get caught up in finding the lowest number, but a truly effective rate shopping strategy considers more than just cost. We think about this as three-stage decision-making process: Gate Shopping, Date Shopping, and Rate Shopping.
By moving beyond a simple price comparison and embracing a more layered approach to rate shopping, you can avoid missing dates — or selecting a more expensive service than you need to hit them.
The goal of rate shopping isn't just to find the cheapest carrier; it's to find the right carrier. The mistakes we’ve outlined above can lead to unnecessary costs, missed delivery promises, and frustrated customers.
Modern shipping technology can help you address common rate shopping challenges. By virtualizing carrier contracts, using complete costs, and leveraging ML modeling, you can enable a truly intelligent rate shopping process.
If improving rate shopping efficacy is a priority, our CEO and ex-Amazon technology executive Jason Murray is hosting a LinkedIn Live discussion on August 21st that will dive deeper into this topic — register here if interested!