Set predictive delivery promise
Set precise, machine learning-driven delivery dates and monitor fulfillment performance to reduce cart abandonment, increase conversion rates, and improve operational efficiency.
What we deliver
A global fashion brand was struggling with static, country-wide delivery estimates that reduced customer confidence, slowing checkout and reducing cart conversions. By implementing AI-powered delivery predictions, they optimized delivery promises, enabling faster purchase decisions and a measurable increase in conversion rates.
A leading consumer electronics brand needed to compete with third-party marketplaces and retail sites, while maintaining a positive brand experience. By integrating zip-code-specific delivery dates at checkout and on product pages, the retailer improved customer trust, reduced promise failures, and drove higher-margin direct sales.
A luxury fashion retailer faced high volumes of “Where is my order?” (WISMO) inquiries due to its complex fulfillment network. With AI-driven delivery promises that account for multi-node fulfillment, carrier capacity, and regional disruptions, the retailer reduced WISMO-related customer service requests and support costs, and optimized logistics efficiency.
An omnichannel jewelry brand lacked early delivery transparency, causing high cart abandonment. By displaying real-time delivery promises on product pages, they increased engagement, reduced cart abandonment, and boosted conversion rates, without requiring checkout system modifications.
AI-driven delivery predictions give shoppers the confidence to complete purchases.
Clear, accurate delivery dates and urgency cues reduce hesitation and accelerate buying decisions.
Provide competitive delivery promises that convert and inspire consumer trust with an advanced machine learning model that continuously refines delivery dates using real-time logistics data.
Predictive delivery promise
Accelerate buying decisions, increase direct purchases, and reduce cart abandonment and WISMO with intelligent real-time delivery predictions, offering full transparency and choice at every stage of the buying journey.
Deliver on your promises with AI-powered accuracy. Using real-time data from 1B+ of shipments, we predict delivery dates with precision so your customers can shop with confidence.
KEY FEATURES:
Keep customers in the loop with updates that adapt to logistics changes, regional disruptions, and carrier performance so they always know exactly when to expect their order.
KEY FEATURES:
Tailor delivery promises, add urgency with countdowns, and support any delivery method from ship-from-store to drop-shipping.
KEY FEATURES:
Keep your post-purchase experience running smoothly with real-time delivery updates, precise zip-code-based accuracy, and seamless integrations.
KEY FEATURES:
Provide your team with the flexibility to easily manage and adjust delivery promises.
KEY FEATURES:
With parcelLab’s post-purchase platform, brands can take full control of their delivery promise, order tracking, and customer communication to reduce WISMO, build trust, and create customers for life.
Over 150 reviews on G2 and recognition as a Leader in post-purchase prove we’re making a real impact. We were even named one of G2’s Top 50 Supply Chain & Logistics Software products for 2025. But don’t take our word for it – here’s what our customers say.
Consumer goods
Validated reviewer via G2
“At first, we used parcelLab to ensure we stayed connected with our customers during delivery. It’s still doing that job but the recent addition of the Estimated Delivery Date (EDD) tool has been great, enhancing our conversion rates even before a customer hits the ‘buy’ button.”
Imran S., Productive Shop
Validated reviewer via G2
“I value parcelLab for its significant impact on the post-purchase journey of our clients. It’s central to our e-commerce optimization strategy, enhancing checkouts with accurate delivery predictions and managing returns efficiently. The simple data setup and consistent reliability make it a favorite in our three-year partnership.”
Jordan U., Retail
Validated reviewer via G2
“We’ve been getting nickeled and dimed for returns with previous users and adding features like Estimated Delivery Date was like double the cost. parcelLab wraps it all in one clean interface.”
Driving trust
with AI-powered delivery transparency
Hugo Boss wanted to increase conversion rates and checkout confidence but relied on static, country-based delivery dates that lacked accuracy. To avoid disappointing customers, they used conservative estimates, which led to longer perceived delivery times and hesitancy at checkout.
By integrating parcelLab’s AI-powered delivery predictions, Hugo Boss replaced static estimates with precise, real-time delivery promises based on intuitive zip code input and machine learning-driven accuracy.
What Our Customers told Forrester
“We lacked control and visibility on this part of the customer experience, especially as we were growing, it was harder to manage the carrier communications data, so we missed traffic and opportunities. There was potential for brand damage from inconsistent messaging and tone, impacting customer satisfaction and brand reputation.”
Senior Business Analyst
Luxury fashion retailer
parcelLab’s machine learning model analyzes over a billion global shipments, historical fulfillment data, and real-time logistics updates to generate precise delivery date promises. It factors in zip codes, carrier performance, regional disruptions, and warehouse processing times to dynamically adjust estimates and provide customers with the most accurate expected delivery date at checkout and on product pages.
parcelLab’s AI-powered delivery predictions achieve 92% accuracy by continuously learning from real-time fulfillment data and past shipping outcomes. The system adapts as conditions change, monitoring carrier capacity, warehouse bottlenecks, and regional trends to ensure brands provide delivery dates that are both competitive and reliable.
Yes! Brands can configure delivery promises based on their operational strategy, selecting from conservative, balanced, or aggressive models. The system supports single-node and multi-node fulfillment, including ship-from-store, drop-shipping, and warehouse-based distribution, allowing retailers to tailor delivery dates to different shipping models and regional strategies.
Unlike traditional static delivery date tools, parcelLab offers dynamic, AI-powered predictions that continuously refine accuracy based on real-time logistics performance. With intuitive zip code input, carrier benchmarking, exception tracking, and proactive delay insights, parcelLab ensures brands provide realistic and competitive delivery predictions that improve checkout confidence and reduce cart abandonment.
An out-of-the-box API allows immediate technical product delivery. With another two weeks to train the machine learning algorithm with consumer and carrier data and configure warehouse and delivery methods, our customers can start to provide shoppers with a faster and more confident decision-making experience.
parcelLab’s dedicated and specialist implementation team of data scientists, carrier logistics experts, and project managers helps drive a smooth and efficient timeline that accommodates anticipated data requirement changes, rigorous test plans, and room for necessary user training.
