parcelLab Promise

Self-configure, adjust, and display a delivery promise date that will help your shoppers make faster and more confident purchase decisions.

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Promise Impact

Higher Conversion

with dynamic delivery promises that increase shopper confidence and revenue

Faster Conversion

with less time spend on PDP before buying and more confident checkout experience

Accurate Predictions

provide competitive delivery promises that convert and inspire consumer trust

Promise Use Cases

Ecommerce

Increase Conversions and Revenue

This global omnichannel fashion brand wanted to increase conversion rates and checkout confidence. Previously they were using a static delivery date on a per-country basis. Lack of data prevented them from making more accurate delivery predictions, which meant they had to use a conservative date to safeguard their customer experience.

With the accuracy of the parcelLab Promise algorithm, their Order Management Expert for Ecommerce Operations was able to calibrate a more aggressive specified promise date, which has resulted in increased conversion rates and faster checkout times.

Marketing

Boost Direct Purchases and Margins

Previously, this experience-driven global consumer electronics brand was showing a static delivery date by country on their product detail pages and at checkout.

Their goal was to increase direct purchases, but delivering a positive customer experience and minimizing promise failures was paramount.

Their Digital Experience Platform Enablement Lead and Ecommerce Activation Lead leveraged the parcelLab Promise API to implement a dynamic delivery date with a customer zip code input to show a more accurate date and compete more effectively with marketplaces and retail sites.

Electronics estimated delivery date example

Ecommerce

Reduce Service Contacts and Costs

This global luxury fashion brand couldn’t previously display a delivery promise date to shoppers due to their complex fulfillment process involving over 20 stock points. It caused delivery uncertainty and frustration with customers, which drove significant support outreach.

With parcelLab Promise, their IT Order Management Applications Manager can self-configure and adjust dynamic promise dates for all orders, including complex multi-leg fulfillment scenarios, setting clear delivery expectations and reducing customer support contacts.

Estimated delivery date example for a fashion retailer

ECOMMERCE

Decrease Cart Abandonment

Previously, this omnichannel jewelry brand provided no information about delivery lead times until the checkout, where they showed a static promise date by country.

They wanted to impact conversion earlier in the buying journey to maximize traffic to their product detail pages and reduce cart abandonment, but without touching their existing checkout set-up.

With pL Promise, their Lead Developer was able to display a data-driven date range by country on their product detail pages, while keeping the existing static delivery date in their checkout. This light touch implementation was completed and delivering value in just 3-4 weeks.

Features

Low Latency API

Delivers business-critical PDP and checkout load times.

Machine Learning Algorithm

Combines parcelLab's global dataset of over a billion shipments with your own historical fulfillment data to provide a best-in-class Promise date.

Self-Serve Configuration UI

Easily configure your delivery promise with warehouse information, carrier delivery methods, additional handling or processing time, and ad hoc adjustments.

Reporting & Analytics

Gain full visibility of historical customer promise attainment, segmented by origin, carrier, and geography.

Customer Impact

parcelLab’s capability to integrate its solution into HUGO BOSS’s existing infrastructure and offer forecasted delivery dates to their global clients kicked off the long-standing partnership. HUGO BOSS initially wanted to understand where parcels were on their journey to customers. parcelLab not only delivered on that promise, but also overtime integrated personalized emails to their client base with updates on order status, delivery timing and returns.

Read the case study