How machine learning is driving e-commerce

The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.

The retail landscape is rapidly changing due to the consumer’s desire for speed and instant gratification. More specifically, last-mile delivery particularly has evolved due to the offline paradigm shift brought on by food delivery that accentuated people’s perception of delivery. For example, a person can get their meal ordered and delivered to them within 45 minutes. Customers, not surprisingly so, responded very well to this level of instant gratification and want it applied to all sectors of retail delivery, not just the food industry. This food delivery model has transcended into every touchpoint for other non-food retailers.

To cater to this demand while continuing to face the challenges of the ongoing supply chain crisis and pandemic-related product production delays, experts predict that retailers will invest in and adopt inventory optimization platforms and last-mile orchestration software that leverages machine learning technology in order to adapt to these developments.

Machine learning refers to when a software or a machine evolves from past data input without being explicitly programmed or changed by the user. Common examples of this self-evolving technology in everyday life include Amazon’s Alexa and the iPhone. Both machines have a learning chip that scans behavior, identifies patterns, and builds upon those patterns to present relevant outcomes.

When applied to the retail space, this powerful technology specifically pivots based on buyer analytics and behaviors, tailoring each retail experience to the buyer and makes behind-the-scenes operations more seamless for retailers and customers.

These tools will provide retailers with intel on every aspect of their business such as inventory, last-mile delivery, post-purchase experience, etc., while still providing exceptional customer service. Below are five ways machine learning is revolutionizing the world of e-commerce. 

Personalized purchase experience

The moment a customer makes a purchase with a retailer, machine learning software begins to create a retail profile for that person. This profile includes recommendations based on the user’s previous purchases, user product reviews when they last purchased items, etc., and will evolve with every purchase the customer makes. This element of machine learning technology creates a truly personalized purchase experience.  

Similarly, machine learning improves group identification and user preferences on online retail platforms that host more than one buyer per account. Amazon is a prime example of this orchestration. Machine learning software categorizes and suggests retail purchases based on which buyer is purchasing what and with what credit card, rather than making broad suggestions based on all purchases delivered to a specific address. This allows for hyper-specific product recommendations based on buyer profiles, rather than relying on one algorithm targeting the collective household’s purchases.       

Unification of software

Machine learning allows orchestration software and a retailer’s internal software to communicate and evolve based on a user’s multiple preferences, taking it many steps further than simply only suggesting products based on previous purchase history. For example, if a buyer often shops for a certain product, consistently selects curbside pickup, and pays with the same credit card, the software will evolve to suggest products they often shop for that are also available for curbside pickup, while also remembering the card for future purchases. This creates a hybrid purchase experience incorporating all retail preferences making it easier for them to shop.

Payment gateways

When a customer makes a payment, either in person or online, various electronic approvals are needed before that payment is processed. Machine learning software streamlines all these approvals, synchronizing the efforts of banks, credit card companies, etc. so that the transaction happens seamlessly and almost instantaneously behind the scenes without user intervention needed. It also anticipates problems and alerts the retailer and buyer should there be an issue with payment.  

Holistic approach to last-mile delivery

Retailers are turning to companies who can provide a robust single API and dashboard for last-mile delivery orchestration that includes  “self-healing” capabilities that enables last-mile performance across a SaaS platform.

The comprehensive strategy to orchestrate delivery is based on rate shopping among numerous delivery providers to get the best price and fastest delivery option for each transaction. For example, once a retail partner selects the criteria and services that best fit their business on the dashboard, the rate shopping and delivery coordination takes place behind the scenes with no impact or delay for the front-end customer.

Self-healing, a crucial machine learning component of a SaaS platform, eliminates the need for merchants to manually cancel, reconstruct, and notify the delivery provider of modifications or fulfillment issues. The platform can provide the ideal end result for both the provider and the consumer in order to fulfill the promise of same-day delivery while predicting any difficulties and pivoting to avoid delays. Using this omnichannel strategy is especially critical for preventing losses and unforeseen risks to distribution and supply chain channels, and it allows the business and its customers to focus on providing outstanding service.

Post-purchase customer communication

In addition to the recommendations provided during the shopping experience, machine learning also allows retailers to monitor the post-purchase experience as well. This technology manages general email distribution, notifying customers of the latest and greatest from brands as well as promotions that they may be interested in, while also taking a more personalized approach through text messaging and post purchase surveys. Retailers, in turn, can use this data to continue excellent customer service and evolve their retail experience based on consumer feedback.

Overall, machine learning has met the critical demand for an enhanced shopping experience caused by consumers’ desire for rapid satisfaction, the supply chain crisis, and the ongoing pandemic. This technology enables retailers to keep an eye on every part of their business while yet providing an above-board, personalized consumer experience.

About the author

Nourhan Beyrouti is the senior director of corporate marketing and brand at Delivery Solutions, a provider of last-mile delivery and fulfillment software. With over 18 years of experience, Beyrouti has worked and lived in 15 countries around the world, giving him a global perspective on branding, corporate innovation, and real estate development.

Beyrouti began his career as a brand manager with SABIC in Riyadh in 2007 before moving on to become the head of corporate communications at OCTAL Petrochemicals in Oman. He was then invited to join Nawras (now Ooredoo), a mobile telecom operator, as head of branding and innovation. He relocated to Saudi Arabia to work as the head of brand experience for Mobily, the region’s largest mobile operator. He also aided the Dubai government in the creation of the “Dubai Plan 2021.”

Following that, Beyrouti worked as the marketing operations and creative services lead for Majid Al Futtaim Holding, where he oversaw more than 25 mega shopping malls, 700 cinemas, 800+ hypermarket retail stores, 21 hotels, and three indoor ski resorts. Prior to joining Delivery Solutions, he served as the marketing director for TMG Northwest, the Pacific Northwest’s fastest growing property management and HOA management firm.

Beyrouti has an MBA from Lebanese American University.

Belanja dan Ulasan Produk