From carrier SLA to customer reality: how machine learning improves delivery date accuracy

The gap between what carriers promise and what actually happens at your customer’s doorstep is where conversions are lost.

Most OMS platforms rely on conservative dates from carrier SLAs (Service Level Agreements). These dates look safe on paper but slow down checkout. In reality, Last-mile deliveries in dense ZIP codes often arrive a day earlier sometimes even more.

If you promise too cautiously, shoppers abandon their carts. If you promise too aggressively, you risk missed deadlines. The solution is to stop relying on carrier paperwork and start relying on proof from real delivery performance.

That proof is already in your business. Infios FAP (Freight Audit & Payment) shows the actual delivery times They are the ground truth of when packages really arrived.  

Infios Machine Learning (ML) trains on this data, along with carrier performance and zip-to-zip patterns, to learn your true transit times by lane and by season. 

When Infios OM (Order Management) applies those predictions at checkout and in-flight, you shift from sandbagged delivery estimates to promises you can hit (Requires nightly FAP ingestion, lane-level features like origin/destination ZIP, service level, seasonality, and a weekly model refresh.)

The problem with conservative delivery promises  

Traditional order management systems rely heavily on carrier-provided transit times that reflect worst-case scenarios rather than typical performance. These SLA-based promises create several challenges:

1. Conversion erosion at checkout: When customers see a delivery date that's 2-3 days later than what competitors promise, they abandon their carts. Research shows that delivery speed is a primary factor in purchase decisions, with faster delivery options increasing conversion rates by up to 25%. 
2. Missed revenue opportunities: Conservative promises prevent you from capitalizing on your fulfillment network's actual performance. If your last-mile carriers consistently deliver ground shipments in one day for certain zip codes, but you're promising three days, you're leaving money on the table.
3. Customer trust issues: When packages consistently arrive earlier than promised, customers begin to question the accuracy of your delivery estimates. While early delivery might seem positive, it undermines confidence in your logistics capabilities and promise-keeping.  

Building ML models with real delivery data

The key to accurate EDD (Estimated Delivery Date) prediction lies in combining multiple data sources that traditional OMS systems don't typically leverage together:

Essential Data Inputs

1. Carrier performance feeds provide real-time service commitments and network capacity, but they only tell part of the story. These feeds capture promised transit times without accounting for actual performance variations by lane, season, or package characteristics.
2. FAP delivery actuals serve as the ground truth for your ML model. These records capture when packages reached customers' doorsteps, providing the historical performance data needed to identify patterns that carrier promises miss.  
3. FAP data reveals the reality behind the SLA. It shows which lanes consistently outperform expectations and which underdeliver.
4. Zip-to-zip transit patterns uncover geographic performance trends that aggregate carrier data obscures.  
5. A ground service might promise 3-day delivery nationwide, but your FAP data might reveal that specific origin-destination pairs consistently deliver in 24 hours due to regional carrier networks and last-mile density.  

How the model computes EDD (processing, lead, transit)  

The model calculates EDD by breaking delivery into three components:

1. Processing time accounts for pick, pack and carrier hand-off activities within your fulfillment centers. This component varies by location, order complexity and operational capacity.
2. Lead time covers internal handoffs and any intermediate SLA requirements between order placement and carrier pickup. 
3. Transit time represents the ML-predicted journey from pickup to final delivery, based on historical actuals rather than conservative carrier promises.

For accurate transit prediction specifically, the model analyzes patterns in your FAP data to identify when real deliveries consistently outperform or underperform carrier commitments. It considers factors like origin zip code, destination zip code, carrier service level and seasonal variations to generate predictions that align with actual performance.  

What the data reveals: Last-Mile, zip-to-zip and seasonality

Analysis of Freight Audit & Payment (FAP) actuals compared to carrier promises reveals significant opportunities for EDD optimization:

• Last-mile delivery frequently exceeds expectations. Ground services that promise 2–3-day delivery often complete last-mile segments in 24 hours for high-density zip codes. This is particularly true for packages entering metro areas where carrier networks are optimized for rapid final delivery.
• Zip-to-zip patterns show consistent over-performance. Certain origin-destination pairs routinely deliver 1-2 days faster than promised, especially for routes between major distribution hubs and population centers. Your ML model can identify these high-performance lanes and adjust EDDs accordingly. 
• Seasonal variations impact accuracy. Carrier promises remain static throughout the year, but actual performance fluctuates based on weather, peak seasons and network capacity. ML models can factor these variations into EDD predictions, maintaining accuracy during challenging periods.  

Business impact of ML-Driven EDD accuracy

Organizations implementing ML-driven EDD systems report measurable improvements across multiple metrics:  

• Higher checkout conversion rates: When customers see trustworthy delivery dates that reflect actual performance rather than conservative estimates, they complete purchases at higher rates. Some retailers report conversion improvements of 15-20% for expedited shipping options (Source: Infios Analytics) 
• Reduced customer service volume: Accurate delivery promises to reduce “Where Is My Order” (WISMO) inquiries by 30-40%. When customers receive realistic delivery dates and packages arrive as expected, they don't need to contact support for delivery updates.  
• Improved customer satisfaction scores: Net Promoter Score (NPS) and Customer Satisfaction (CSAT) ratings increase when delivery promises align with reality. Customers appreciate predictable, accurate delivery communications over pleasant surprises that come with uncertainty.  
• Optimized carrier mix by lane: Understanding actual performance by route enables better carrier selection and service level decisions. You can confidently use faster, more expensive services when ML models indicate standard services will meet customer expectations.  

Implementation strategy for ML-Driven EDD

Rolling out ML-powered delivery date prediction requires a systematic approach that minimizes risk while maximizing learning:

• Start with high-volume lanes: Focus initial implementation on your top 20% of shipping lanes by volume. These routes provide sufficient data for model training and deliver the largest impact on overall customer experience. 
• A/B test EDD display at checkout. Run controlled experiments comparing ML-predicted delivery dates against traditional carrier-promised dates. Monitor conversion rates, customer satisfaction and actual delivery performance to validate model accuracy.
• Measure prediction accuracy continuously. Track the percentage of deliveries that arrive on or before your predicted date. Establish acceptable miss rates (typically 5-10%) and adjust models when performance falls outside these thresholds.
• Segment by channel and marketplace. Different sales channels may require different promise strategies. Direct-to-consumer orders might tolerate more aggressive promises than marketplace fulfillment, where late delivery penalties are severe.  

Transforming Delivery Promises with Infios FAP + Order Management (OM)

The gap between carrier promises and delivery reality represents both a challenge and an opportunity.  

By leveraging machine learning to analyze Freight Audit and Payment (FAP) actuals and zip-to-zip patterns, you can predict delivery dates that reflect actual performance rather than conservative SLA commitments.

This data-driven approach enables you to promise confidently without sandbagging, improving conversion rates while maintaining customer trust.  

The key is starting with solid data foundations, implementing systematic testing protocols and continuously refining predictions based on actual delivery performance.