FenixCommerce Builds a Multicarrier Shipping Solution using Predictive AI on AWS

Today’s retailers have multiple options for multicarrier shipping software, which is used for doing carrier-rate shopping (CRS). But although this software can automate some parts of the rate-shopping process, every retailer must ask whether it’s getting the best shipping decisions from their CRS solution—and whether reaching those decisions can be done better.

If cart abandonment and high shipping costs are hurting your ecommerce business, continue reading. After a recent chat with Jon Nordeen, Former CIO, Kohls/Hudson Bay that you can watch in this video, on how CRS can impact an ecommerce business, we set out to help retailers find industry-leading off-the-shelf AWS Partner solutions for keeping your ecommerce profits high, shipping costs low, and your customers happy.

In this blog, we explain the nuances of CRS and how the solution from FenixCommerce (Fenix) helps retailers worldwide to improve delivery accuracy and checkout conversion. Using artificial intelligence (AI) delivery models powered by Amazon Web Services (AWS), this AWS Retail Competency Partner crunches data for over 3 million shipments monthly.

Fenix’s CRS solution optimizes carrier choices and displays them as shipping options at checkout, creating a low-friction experience for shoppers and higher profits for businesses.

Figure 1 – Fenix CRS solution – Shipping options at checkout

If your parcel management software doesn’t display optimized shipping costs or accurate delivery estimates, then you’re only doing partial rate shopping. This strategy could be hurting your business in ways that you might not realize.

Flaws in Today’s Partial Multicarrier Rate Shopping

Most CRS solutions today work in one of two ways: rate shopping at label printing and partial rate shopping at checkout.

Rate shopping at the time of label printing

Most retailers automatically rate shop at the point of printing a label by simply choosing the cheapest carrier service available. Although this strategy helps reduce shipping costs, retailers often end up shipping with a carrier that doesn’t meet shoppers’ expectations, leading to late deliveries. By sacrificing the delivery experience for cost savings, retailers risk losing shoppers over time. BusinessWire reports that 39 percent of customers won’t buy from a retailer again after a late delivery.

There is also manual work involved with rate shopping. Traditional multicarrier management solutions are not intelligent enough to integrate upstream data such as inventory levels or fulfillment locations. Retailers might need to adjust for an excessive number of orders that haven’t gone through processing as planned—a scenario in which the very purpose of automation has been defeated.

Doing partial rate shopping at checkout

Some CRS solutions show delivery time lines and shipping costs at checkout based on pre-determined or static carrier selections. However, the technology employed in these cases often extends page load times, which also impacts the customer experience.

First, the software must be integrated with different carrier APIs to retrieve current delivery times and rates. As shoppers reach the checkout page, the software needs to keep calling those APIs in real time to request shipping services and rates. Only then can it choose the most efficient delivery options and display them to the shopper.

Calling APIs across different carriers and service levels for every order can cause load times of 2 seconds or more. Imagine waiting for a checkout page to load for 2 seconds. Sounds funny, we know, but try it sometime—especially on your phone.

Even if a shopper does wait for the page to load, the CRS might not provide an actual delivery date. This uncertainty can lead to frustration and potentially deter customers from completing purchases.

The New, True Multicarrier Shipping Solution

Fenix’s CRS solution goes beyond checking APIs at checkout. It harnesses current and historical enterprise data, including third-party and carrier data, to create custom shipping options for brands and shoppers before purchase. You can even display estimated delivery dates on product detail pages, using actual inventory locations and the shopper’s geolocated IP address to determine the optimal carrier service in near real time.

At checkout, the CRS reconfirms the optimal carrier selection based on actual cart value, the customer’s address, packaging, and other relevant parameters. This reconfirmation, performed in near real time within Fenix’s predictive AI–based delivery models, identifies the best-fit carrier service. This strategy provides a much faster and more accurate expected delivery date experience.

During Black Friday and Cyber Monday 2023, Fenix clients experienced a surge of up to 8 times in website traffic and order volume on average. At the same time, page load times from Fenix averaged 250 milliseconds over the entire 5-day period—much faster than the 2-second load times with carrier API–based methods.

In the course of fulfilling an order, conditions may have changed between checkout and label printing. With Fenix’s CRS, retailers can opt to go with a less-expensive carrier that is predicted also to arrive the same day or earlier. This helps retailers keep their promised service-level agreements, saving additional costs while meeting shoppers’ expectations.

But how can you determine the ROI of such an innovative solution? Try before you buy.

In 2024, Fenix launched its Multi-Carrier Parcel Rate Simulator, which uses proprietary algorithms running on AWS to analyze historical shipment data and compare rates across carriers. The simulator’s engine assesses past carrier performance and accounts for factors such as size, destination, and shipping speed.

The engine will select the lowest-cost carrier service that meets or beats the expected delivery date that was promised to the customer at checkout. The carrier selection is truly optimized for cost, the retailer’s standards, and the strongest possible customer experience.

The simulator then compares its selection for that shipment against the retailer’s existing CRS selection to assess which option is better. Using Amazon QuickSight, which provides business intelligence at hyperscale, Fenix built a proprietary reporting platform that unifies all of this data. Retailers can view and understand the insights behind the results on a simple-to-use dashboard.

What will Fenix’s CRS Simulator show you?

Below is a snapshot of the CRS dashboard for a major apparel brand, using just one month’s shipment data. The Multi-Carrier Parcel Rate Simulator shows the number of shipments for which Fenix would have selected a different service and how much retailers would save using its recommendations (circled in red below).

Figure 2 – Fenix Multi-Carrier Parcel Rate Simulator Dashboard

The Multi-Carrier Parcel Rate Simulator was run on more than 1 million shipments over 90 days. Several retailers reported that they saved 10–25 percent on their carrier spend, with an average of 16 percent.

Maximize Profits with Smart CRS Solutions

More retailers are adopting modern, AI-based delivery software for true CRS, before purchase and at fulfillment. They’re not only gaining valuable insights—they’re also boosting long-term profits.

Fenix provides a multicarrier management platform that saves time and money for retailers and shoppers—giving you a material advantage. You can learn more about Fenix through the AWS Partner Network (APN) and by visiting Fenix’s listing on AWS Marketplace.

Contact an AWS representative to discover how we can help accelerate your business.

Further Reading

• FenixCommerce improves profitability using AI
• AWS last mile solution for faster delivery, lower costs
• Build conversational experiences for retail order management
• Building a Serverless Event-Driven Retail Order Management System

AWS Partner Spotlight

FenixCommerce uses inventory data, carriers’ historical transit times, retailers’ operational performance history, and proprietary AI algorithms to predict accurate delivery dates and personalized shipping costs. It works on branded, direct-to-consumer ecommerce sites during the pre-purchase stage on product, cart, and checkout pages.