Fashion e-commerce online catwalk: 3 faux pas not to repeat
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The Jetsons suggested a life of automation and ease that we haven’t quite mastered yet, but the use of artificial intelligence and machine learning has the potential to get the future of online shopping – especially retail – closer to the ideal.
Before jumping into the future of online shopping and how retailers are strutting the digital catwalk with AI and ML, let’s distinguish the two.
AI (Artificial Intelligence) is a completely automated, intelligent system that can help shoppers find exactly what they need.
ML (Machine Learning) is most often discussed in retail, as it takes in countless lines of historical data and tries to find patterns and trends, as well as make accurate predictions.
The pandemic illuminated the need for both technologies, proving that both have staying power.
The future of online shopping: Virtual try-ons, fewer returns
Here’s how AI and ML are revolutionizing the future of online shopping:
- Helping shoppers find the right size and product to reduce e-commerce returns
- Strengthening retail supply chains
- Boosting personalization for brand differentiation
COVID restrictions quickly shut down stores across the globe in early 2020 and retailers quickly had to figure out a new way to help their customers make informed decisions. With few in-store experiences, customers were left to guess if products on their screens were ones that they would enjoy. Buying two sizes of the same shirt might be easy for an unsure customer, but it wreaks havoc on retail inventory.
Jason Goldberg, Chief Commerce Strategy Officer at Publicis Group, explains that the move to virtual try-ons helps reduce returns and boosts sustainability.
Eight percent of in-store purchases get returned while “in e-commerce, 20 to 30% gets returned. So that’s an astronomically expensive and ecologically disastrous outcome,” he says.
As various retail segments continue their meteoric growth online, this mismatch must be addressed to avoid massive hits to profit and revenue.
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Fashion is a $2.5 trillion industry, producing 10% of global carbon emissions, 20% of global wastewater, and vast biodiversity loss. Consumers are demanding change, forcing sustainability in fashion as a requirement, not a trend.
Strike a pose: How machine learning and artificial intelligence are powering CX and loyalty
Training machine learning models to help customers order the perfect size and type of product makes sure that they are happy the first time. Virtual try-ons proved very useful during the pandemic when fitting rooms were closed. Their high level of effectiveness proves they’ll stick around post-pandemic.
This is especially true in categories like cosmetics. Trying on a tester that several others had used was never a very hygienic practice and COVID may have ended germ-ridden experiences like that for good. Augmented reality enables customers to try on several cosmetic products without having to wipe off the previous color or even leave their homes.
Similarly, AI and ML have started to help consumers make more confident decisions, which helps retailers maintain stock levels and ease the stress on their supply chains overall.
Retail supply chains get smarter for better online shopping
The pandemic exposed just how important supply chain is to retail. With all the toilet paper hoarding, many shoppers encountered a completely empty shelf for the first time.
Consumers don’t often think about where and how to get essential products until they can’t have them suddenly.This is where Goldberg sees a perfect application for machine learning. “We can use machine learning to look at all that historical behavior, predict our supply chain, better predict how efficient our factories will be at making the [products], and match supply to demand better in the store,” he says. “The customer doesn’t have to do anything different; they never know or care that machine learning made that store better, they just know that Walmart had exactly what they wanted.”
This seamlessness is the real end goal: getting the customer what they want and need in a timely fashion.
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AI in the future of online shopping: Striking a balance
COVID accelerated consumer acceptance of new ways to shop. This is just the start of using AI and ML in retail. As consumers start to use and enjoy the features already on the market, they will start to expect these features to work together.
For example, a home renovator might want to change the color of their walls and carpeting. Being able to visualize the change in a fully augmented reality view helps them make better decisions based how the products do or don’t complement one another. Switching over to apparel, a retailer might want customers to virtually try on an entire outfit to better cross sell and reduce returns.
With so much customer data collected, retailers should rush to create personalized experiences. At the same time, retailers must strike a balance with AI; it shouldn’t be used for processes that already work seamlessly. No one needs technology for the sake of technology. Instead, AI and ML should be leveraged to materially enhance the customer experience.
AI and AR: Powering the future of fashion e-commerce
Fashion e-commerce will continue to grow beyond the pandemic, driven by AI and AR technologies that help shoppers find the perfect fit online.
ML fuels personalization, differentiation
Machine learning can also serve as a differentiator for retailers in highly competitive categories. For instance, Amazon may have countless hammers to offer their customers, but a smaller retailer can provide an invaluable experience to consumers by helping them pick the right hammer for their specific project.
There are distinct advantages to this data collection and aggregation because, Goldberg explains, “you know more about how your customers use the product, you know more about the path they took to consider the product, so there there’s data out there that you can collect.”
Data is a goldmine for retailers that are able to leverage it appropriately.
Get ready for the future of online shopping
In order to use AI and ML most effectively, retailers need to feed unique data into algorithms and train them. This takes time to perfect, so in the meantime Goldberg suggests retailers prepare.
“Get your data policies in place, get your archival policies in place, get your privacy statements in place so that you’re telling customers what you’re going to collect and how you use it, which gives you permission to use it to then train these machine learning models to create unique experiences,” he says.
The future of retail will be highly personalized and center on the aspects that enhance the customer experience, while minimizing backend friction and costs. As new retailers pop up each day, effective uses of data will help category leaders attain and maintain their status as consumer favorites.
