How AI-Driven Experiences are Driving Profitability in e-Commerce

Amazon is secure in its position as one of the biggest companies in the world, dominating both e-Commerce and major developments in retail and tech.

e-Commerce is becoming an increasingly common purchasing option for US consumers, leaving retailers the task of bridging the gap between retail and technology. Stefany Zaroban at DigitalCommerce360 reports that while e-Commerce represented only 13 percent of total retail sales in 2017, it accounted for nearly 50 percent of the growth.

At the same time, the growing share of e-Commerce transactions do not have to be dominated by the giants. Ingrid Lunden at TechCrunch reports that Amazon’s slice of the e-Commerce pie stood at 49 percent in mid-2018; in contrast, the tech giant’s share of all retail spend stood at 5 percent. We share these stats for two reasons:

1. The Amazon share of digital retail is profoundly disrupting. For digitally native and brick-and-mortar retailers alike, “How do we compete with Amazon?” is a looming, critical question.
2. Though e-Commerce continues to grow as a share of all retail revenue, most retail purchases are still made in physical locations. Digital retailers are still learning how to make online shopping as engaging as in-store shopping can be.

Retailers have an opportunity to increase their share of the pie by focusing on experience-driven e-commerce for their customers, powered by the tight integration between content management (CMS) and commerce systems. More specifically, AI-enhanced e-Commerce marketing can make for better personalization and engagement across all channels.

In this post, we dive into how AI fits into building personalized and profitable digital experiences, and what that means for e-Commerce brands. With next-generation AI at the center, we answer three major questions:

1. Why is personalization important for profitable e-Commerce interactions?
2. How does personalization fit in with creating digital experiences for e-Commerce customers?
3. How is artificial intelligence driving personalized retail experiences?

Personal is Profitable

Personalization within e-Commerce is not exactly a new idea or endeavor. Retailers have been A/B testing and crunching user data since the 1990s to serve shoppers the products retailers think they’re looking for.

The last several years, however, have seen a dramatic shift in the capabilities of personalization thanks to an increase in available data — and sophisticated tools for analyzing that data. Vance Reavie, CEO and Founder at Junction AI, writes at Forbes how data-informed AI improves personalization (and profitability) in three ways: It analyzes customer-specific variables, it assesses much more data than individual marketers could, and (as a result) it creates unique customer profiles with personalized experiences.

Reavie also reports that nearly three-quarters of customers prefer shopping with brands that take their personal information into account. Eighty-six percent of customers say it factors into their purchasing decisions.

With these numbers, it’s becoming more clear that investing in personalization will make digital retail more profitable. Shep Hyken says it drives profitability in two ways:

• Increased revenue. Four out of ten consumers say they have bought something more expensive than originally planned because of personalized service.
• Increased loyalty. Forty-four percent of consumers say they will most likely buy again after a personalized shopping experience.

Backing these numbers up, Mark Floisand at MarTechAdvisor reports that retailers see sales rise up to 10 percent after implementing personalization efforts.

AI Personalization is at the Heart of Digital Experiences

Personalization is all about creating a better shopping experience for your customers. Andy Tow, managing editor at the Retail Marketing Group, writes that operating in a consumer-driven economy puts more pressure on retailers to personalize and to create these better experiences.

In fact, Tow says digital experiences will soon emerge as the key differentiator for brands. In just a few years, Tow predicts, customers will be scrutinizing brands based on the experiences they create more so than price or product.

Marketing consultant Chris H. Peterson says this is the lesson retailers like Sears and Toys "R" Us failed to learn. According to Peterson, these businesses failed because they focused on products, not customers. “In order to thrive, today’s retailers must evolve from a product-centric past to a future focused on increasing relevance by providing greater value add to customers,” he writes.

“[Customers] are demanding more value in ways that are relevant to them.” In other words, personalization is now an integral aspect of digital experiences — and something customers have now come to expect.

AI’s Role in Creating Personalized Experiences

This is where artificial intelligence steps in.

The newest generation of AI tools can crunch customer data, understand people’s individual tastes and help tailor shopping experiences accordingly. Rob Garf, VP of industry strategy and insights for Salesforce Commerce Cloud, puts it best: “

“AI is key to linking product, customer, and transactional data in meaningful ways.”

AI brings a new level of accuracy and automation to personalized digital experiences, in more ways than one.

“AI models can more accurately determine a consumer’s current journey and anticipate the next step in that journey,” Patrick Nguyen writes at CMSWire. “This comprehensive data set includes interaction events from web visits, phone calls, mobile app sessions and in-store visits, as well as transactional events from sales, billing and other enterprise systems.”

Bringing artificial intelligence into the mix means being able to automatically assess different data layers and address them across different channels. Attempting this manually would be virtually impossible, since there are hundreds of data points at play. AI overcomes that problem, as Graham Cooke points out at VentureBeat.

Content Forms the Building Blocks of These Experiences

As the team at BitRewards points out, AI this sophisticated can anticipate the behaviors of shoppers. This marks a major paradigm shift in the way brands interact with consumers.

When AI can mine a company’s data to reveal how its consumers’ tastes have evolved over time, that creates a roadmap for how the company can speak to those customers during future buying journeys.

A maternity clothing company, for example, could send nurturing emails to new parents that sync with their baby’s own growth. The emails at three months and 12 months would speak to very different needs, for example. The shop’s home page could respond dynamically, as well. When the data shows a shopper’s baby is 24 months old, the featured products would be for toddlers. As the BitRewards team points out, Amazon has seen a 29-percent increase in sales by targeting product recommendations this way.

AI-informed experiences are not just about more targeted messaging or making personalization efforts more efficient. They also help build relationships with customers. Dr. Anil Kaul, founder of Absolutdata, points out that virtual personal assistants (like Alexa), chatbots and voice-activated apps all change buyer-seller dynamics by acting as the intermediary in purchase decisions. This is yet another content channel for digital retailers to take advantage of.

By allowing AI to inform what companies know about their customer, those companies get a better idea of what their customers want and thus are able to create more profitable experiences on a shopper-by-shopper basis.

So, what can these experiences look like with AI at the helm?

Examples of Next-Gen AI Driving Digital Retail Experiences

The two real-world examples below paint an enticing picture for retailers. These well-known brands demonstrate precisely how to use AI to drive profitability in a couple of ways:

• By connecting shoppers with smart personal shopping assistants.
• By personalizing shopping experiences to boost conversions.

Case Study No. 1: ASOS

ASOS is a UK-based retailer that has grown substantially in the past 18 months. The company’s expected revenue for 2018 is more than $3 billion, up from $2.5 billion in 2017.

The rapid growth comes as ASOS doubles down on its investment in tech. Earlier this year, Samantha McDonald at Footwear News reported that the retailer had invested more than $100 million on tech, a large portion of which went to an experiment with AI. The company tested a virtual assistant that was designed to offer customers tailored suggestions based on sizes, preferences and other data.

The company statement is unequivocal in the reasoning behind this move: “We have been driving growth and profitability by optimizing customer experience through AI over a number of years now, and we are aiming to establish AI development capability in every area of the business, from customer experience, customer care, supply chain and retail in pursuit of being a truly data-driven organization.”

This investment almost immediately translated into growth. “Improved customer experience, at home and abroad, from delivery to mobile, was a focus for ASOS in its latest half-year,” Chloe Rigby at Internet Retailing reported. After those six months, Rigby points out that ASOS unveiled dramatically rising sales and profits. And it didn’t stop there.

Based on the initial experiments and investments, ASOS has now launched a voice app and virtual assistant named Enki. Nikki Gilliland at Econsultancy writes that the chatbot sets the bar for other retailers in the digital sphere.

The bot “first appeared as an ASOS chatbot service on Facebook Messenger in the UK earlier this year,” writes Essential Retail Editor Caroline Baldwin. “Shoppers have been using the AI-driven solution to search the e-Commerce site using images from the web or taken on their smartphones, while Enki also recommends brands and provides style matches.”

In another article, Baldwin points to the impressive growth of ASOS as proof of the success of this technological investment. Profits were up by 29 percent in October, with total orders up by 27 percent. There may be multiple factors at play in driving up this revenue, but this use of next-generation AI is certainly one of them.

Case Study 2: The North Face

In a partnership several years in the making, outdoor sportswear company The North Face has teamed up with IBM Watson to create a better, more personalized experience for its customers.

The result is a Q&A-style chatbot called Expert Personal Shopper, or XPS.

The tool walks customers through a series of questions and offers personalized product recommendations based on their responses and preferences, Paula Levy at Apparel News reports. The questions take on where, when and how customers may use a particular piece of clothing from The North Face — a jacket, for instance. Based on inputs that include customer responses as well as external data such as weather forecasts, the tools narrows down a customer’s product search to just six options, arranged in order of best match.

“This will save users time from scrolling through hundreds of jacket options, many of which would not even match their functional needs,” writes Mariah Parker at BuZZrobot. While most AI-enabled tools aim to increase conversion rates, this approach for The North Face also helps remove potential friction points for site visitors — e.g. facing an overwhelming array of choices.

The AI tool for The North Face is still in its testing stage, but early results suggest revenue growth on the horizon. According to Daniel Faggella at Tech Emergence, the pilot program resulted in a 60 percent click-through rate and 75 percent total sales conversions.

The Bottom Line: Use These Tools to Boost Engagement and Conversions

AI is making personalization smarter and more effective than ever. This drives both engagement and conversion rates for retailers, and makes at least one thing clear: personalization at this level should become the standard for retailers looking to bridge the gap between the physical and the digital.

This is crucial for retailers to understand, especially as the landscape only becomes more competitive, and customers begin to place more weight on shopping experience over price.

Note, however, that these aren’t necessarily capabilities you must develop in-house. Sandy Shen and Mike Lowndes at Gartner recommend leveraging commercial AI solutions to get the ball rolling in a shorter time frame.

The case studies outlined above highlight some of the ways this can take shape. With the right software, you can roll these efforts out in months instead of years. There’s still time to get ahead of this AI wave, but the time to act is now.