For years, delivery expectations have hung on broad carrier windows: two to five days, three to seven days, etc. While these still exist, modern shopping carts and time-conscious consumers demand finer details. New tools emerge to communicate specific delivery dates and times, boosting cart conversions and, ultimately, sales.

Expectation vs. Reality

Parcel Monitor's "State of E-Commerce in the US" reports an average first-attempt delivery time of 3.6 days. However, this clashes with consumer expectations, especially Amazon Prime's two-to-three-day standard. This gap often stems from retailer fulfillment delays (sometimes up to two days) and afternoon orders missing pickup deadlines, adding another day in transit. So, while broad carrier windows are inaccurate, order placement time and retailer-to-carrier handover significantly impact delivery.

Delivery Time Influencers: Four Key Factors

·Inventory location: Closer inventories mean shorter transit times to customers.

·Retailer processing cutoffs: Order processing delays and too-early-in-the-day pickups extend the delivery window for orders that have to shift to the next day.

·Order process: Streamlined order processes minimize delays.

·Carrier historical averages and policies: Final-mile workshare products can add ambiguity and impact customer satisfaction, as well as the carrier’s historical performance in a particular shipping lane.

Static vs. Dynamic vs. AI: Choosing the Right Delivery Model

Many retailers rely on static delivery times based on carrier service guides. This method is neither dynamic nor does it take into consideration historical averages and late deliveries. Carrier APIs offer delivery estimates, but with only 70% accuracy and limited insight into the performance of final-mile workshare products. The optimal solution leverages AI technology.

AI Delivery Advantages

·Dynamic delivery dates: Increase conversion by displaying accurate dates at every website touchpoint.

·Data integration: Considers historical and current carrier data, real-time product/inventory data, and your specific delivery needs.

·Cost savings potential: AI-powered rules can help to identify times when you can downgrade service levels while still meeting delivery commitments, potentially reducing shipping costs. However, carefully monitor performance to ensure customer expectations are met.

Next Steps

·Build a business case: Analyze data from different carriers, pickup locations, and delivery ZIP Codes to identify patterns and discrepancies between promised and actual delivery times.

·Run A/B tests: Compare conversion and cart abandonment rates for various carrier services and delivery date commitments in your shopping cart.

·Investigate technology: Analyze rates and delivery tools offered by different in-cart rates and delivery technology providers to find the best fit for your needs.

Connecting the Dots

Many steps towards AI-powered delivery rates are already part of your carrier spending analysis and cost reduction efforts. Leverage existing data, conduct A/B tests, and explore advanced cart technologies. By sharing your data and implementing the right solution, you can expect a 40% increase in both sales and customer satisfaction!

Krish Iyer is VP Strategic Partnerships & Industry Relations, Auctane. He can be reached at krish.iyer@auctane.com.

This article originally appeared in the January/February, 2024 issue of PARCEL.