When Man and Machine Work Together: How to Best Use Machine Learning
It’s clear that most retailers and brands can’t survive on brick and mortar revenue alone. Whether through a website, app or both, consumers are accustomed to online shopping and desire to get the most out of their digital experiences.
Now a multi-billion dollar industry, artificial intelligence goes hand in hand with e-commerce. To prove this point, a McKinsey Global Survey on the adoption of the technology reported 47% of business executives said their companies had embedded at least one machine learning capability in their business processes, and 30% said they are piloting machine learning.
Following suit, machine learning is becoming less of a standout feature and instead is a critical digital tool for retailers looking to improve their e-commerce sites, engage customers, secure more sales and ensure their return.
Despite concerns around job cuts and a world overrun by robots, machine learning wouldn’t be able to exist without human touch. As this technology evolves among brands and retailers, machine learning will supercharge the customer experience, rather than replace humans.
Three ways to use machine learning and humans together
When humans and machines work together, retailers can maintain true accuracy through human touch, ensuring they create a richer environment for the online shopping community by offering faster search, more customized results, and enhanced customer service. Here’s how humans and machine learning can work hand in hand to deliver a powerful shopping experience.
1. Increase speed using machine learning when it comes to finding products Searching for products is a common pain point for shoppers. Despite the digital revolution, our natural tendency is to ask someone for help when we are on the hunt for a specific pair of shoes or need help to find that “perfect” outfit in mind for an upcoming occasion.
Online shoppers don’t have this option. In today’s world of instant gratification, they’re left with search bars that are slow to load products or even bring back irrelevant results.
Leveraging machine learning on an e-commerce platform not only improves the accuracy of search, making it more conversational, but it also speeds up the process from browsing to purchasing. As customers are able to identify what they want quickly, their chances of making it through the checkout with the items they intended to buy, and possibly more, are heightened.
However, site search is only as good as the data the system ingests. Using an algorithm powered by machine learning can remove the challenges posed by incomplete or incorrect product data. What’s more, data enrichment improves accuracy and relevance in order to provide the right products that a shopper searches for, ultimately decreasing the time they spend looking through irrelevant items.
2. Assist shoppers with enhanced customer service Receiving assistance while shopping online goes beyond search. For example, adding a chat feature with service 24/7 is another benefit retailers can reap from leveraging machine learning. The inevitable questions ranging from shipping to payments to product information need to be answered promptlyto keep customers’ interest. Chatbots powered by machine learning make fast responses a reality, pushing shoppers closer toward conversion.
In addition to chatbot features, there’s also been a rise in voice search, as shoppers look for the same online assistance that in-store employees can provide. The key to improving a customer’s search experience, regardless of the conversational commerce channel, is to ensure that it accounts for natural nuances in human language.
Shoppers want to feel cared for, not stranded on a site with no idea where to find what they want. An easily accessible chat feature is a surefire way to start gaining their trust. Instead of an impersonal connection and generic responses, retailers and brands can use machine learning algorithms while collecting data and tracking behavior to make each interaction more relevant and personalized.
3. Curate more personalized results On top of faster search and increased assistance, retailers can use machine learning to monitor every action taken on a website and use this data to create more personalized recommendations for each shopper. By keeping track of browsing and purchasing history, retailers can provide more relevant results from the time a potential customer clicks on the home page and continues throughout the rest of their shopping journey.
However, retailers must be respectful and cautious of tracking only publicly available user data, or if they are collecting any specific information, must be explicit and transparent about their collection. Since e-commerce sites tend to store a lot of sensitive information, this is absolutely critical to preserving user trust.
Again, machine learning algorithms hold the key to making this type of personalization possible. Rather than mere segmentation, machine learning allows for true individualization, taking into account specific data to hyper-target recommendations for each customer.
What’s more, retailers can learn more about a customer with each site visit. Displaying items related to past purchases or adding features such as “frequently bought together” or “related to items you’ve viewed,” provides shoppers with support in an often chaotic environment where finding a particular size, style or brand can be a challenge.
Machine learning’s future is right around the corner; here’s how to prepare
In the coming years, machine learning and similar innovations such as machine learning and robotic process automation will become less of a futuristic concept in retail and more of a crucial adaptation for retailers.
We already see this mindset in practice by big-name retailers such as Walmart, which is making substantial investments in artificial intelligence and machine learning as a key instrument to growing its business. Similarly, Amazon continues to impress customers with cashier-less stores and drones that deliver up to 5-pound packages within 30 minutes in the works.
Despite these advancements with technology, the human role within retail won’t be eliminated; instead, they will change to become more strategic and collaborative with these technologies. As man and machine meet, the two will fuse for total merchandising control.