Machine Learning in Ecommerce

The rise of online shopping is undeniable, and so is the competition between internet retailers. To get ahead of the curve, ecommerce businesses are increasingly turning to artificial intelligence to better understand their shoppers’ needs, create a more personalized customer experience, and boost sales revenue as a result.

The sector has for long been leading the way for the adoption of artificial intelligence. By making extensive use of the latest machine learning solutions, from recommender systems to augmented reality, online retailers are completely transforming how we shop online.

It’s estimated that AI investments in the sector will continue to grow, totaling to a compound annual growth rate of 42.8% from 2019–2025.

Recommender systems

Recommender systems are why Netflix always seems to have something that suits your tastes to watch, and Spotify keeps finding music you might like. They have been a key factor in the commercial success of giants with a large number of users, but they can be implemented with success on ecommerce platforms of any size.

The AI-powered systems analyze consumers’ activity and browsing data, and create product recommendations tailored to their individual needs and preferences. To widen the range of products and services presented to the user, they also take into account the behavior of consumers who display a similar taste.

On the one hand, recommender systems are a great way for marketers to increase their sales volumes. On the other, they’re helping consumers broaden their preferences and make more relevant choices in a world of product overload.

Content personalization

Businesses that want to thrive in the highly competitive online retail sector need to go beyond generic offers or even basic personalization, such as page layouts. Thanks to machine learning, they can offer their customers exactly what they want and then some.

According to a report by McKinsey, personalization can deliver five to eight times the ROI on marketing spend and increase sales by 10% or more.

Accenture’s Personalization Pulse Check report found that businesses’ number one challenge in meeting consumer needs is “learning how to uniquely serve everyone without overwhelming anyone.”

The paper also said that nearly half of the surveyed consumers left an online shopping website and went on to make a purchase on another site simply because it was poorly curated. The vast majority (91%) were also more likely to shop with brands who provide them with personalized offers and recommendations.

One of the ways businesses can inject some AI into their one-to-one marketing is by providing individualized incentives, such as birthday discounts.

For instance, to reduce the cart abandonment rate, which varies between 60% and 80%, online retailers can use artificial intelligence to analyze customers’ digital body language while they’re still on the site, and then provide them with the right messages to encourage a purchase.

Open Time Content is another AI-powered technology that helps marketers contextualize engagement. It populates emails with content at the time they’re opened for the ultimate real-time retail experience.

To make the offer as appealing to the consumers as possible, it combines their previous shopping history with retailer’s latest offers, and updates each time the email is accessed.

Chatbots

Long gone are the days when talking to a machine was frustrating and weirdly amusing at the same time.

Thanks to natural language processing algorithms—which analyze and draw insights from chatbot conversations to keep improving their performance—the quality of exchange is often on par with that of human-to-human interactions.

For businesses, using chatbots means huge savings when customers require quick, simple answers. And since these are the most common inquiries, chatbots can do the job just fine.

In case the users need information the chatbot can’t provide, it can instantly put them in touch with one of the customer service operatives, making sure that no inquiry goes unanswered.

Dynamic pricing

You’ve certainly noticed how the price of your Uber ride home keeps fluctuating depending on the time of day, weather conditions, and availability of drivers. Although the carrier’s dynamic pricing is legendary (after all, the company has over 100 experts working on them), the technique can benefit any business willing to invest in it.

According to McKinsey, companies that implement dynamic pricing report 2–5% sales growth and 5–10% increase in margins as well as higher levels of customer satisfaction.

The idea behind the practice is that customers get a personalized price, dependent on a specific set of circumstances. It can include pricing data from different online sources, including a company’s competitors, as well as customers’ individual shopping habits.

Machine learning is an essential element in this process: it makes it possible to identify shoppers’ data patterns and to predict how responsive they might be to new prices.

AI-powered A/B tests

A/B or split testing is a common practice in marketing and ecommerce. It consists of showing a group of users different versions of, for example, a website or an article headline. Then, you analyze which version drew more clicks and encouraged more interaction, and why. This version is the winner of the A/B test.

Manual implementation of the tests is often very time-consuming, and it takes a while to produce results. By injecting artificial intelligence into the technique you can make your product self-optimize in real time and deliver the best and most effective variation to your users, without the need to manually set up and fine-tune the A/B test.

The AI component allows you to test a potentially infinite number of variables at any given time. Notably, it can also take into account users’ preferences and previous browsing history to determine the most suitable combinations.

Shoppable augmented reality—AR/VR

Even though online shopping has been rapidly gaining ground, many consumers, in particular while shopping for clothes or beauty products, still stick to brick-and-mortar shops. The reason is simple: trying on an item before you purchase it not only saves you time and money, but it also makes the shopping experience fun.

However, thanks to artificial intelligence, consumers no longer need to leave their homes to discover new styles and curate their wardrobes.

Augmented reality (AR), often used together with AI and dubbed the future of retail, allows shoppers to virtually try on products such as clothing, eyewear, or makeup—in real time.

For instance, Pinterest recently partnered with cosmetic giants such as Estée Lauder, YSL Beauté, and Lancôme to launch a shoppable AR feature that allows users to “try on” different lipstick colors using the app’s built-in camera. The platform will also display similar lip shades on skin tones that match the user’s and let them explore related looks.

The UK fashion retailer River Island has used AR to enable its customers to visualize what their homeware products could look like in their own home using their smartphones.

The effect lets users view selected products in 3D and place them anywhere within their home. Shoppers were also given the option to take a screenshot and upload it to social media for a chance to win the products.

Although the use of AR in online retail is not common yet, the spread of 5G is likely to accelerate its growth.