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Artificial Intelligence Is the Future of Everything, Especially E-Commerce

Kristin Maranki

Walmart announced yesterday that its products will be made available to shoppers on Google Express, Google’s online marketplace. Industry experts see this as a move to compete directly with Amazon’s dominance online.

Google and Walmart say the partnership is more about preparing for the future of e-commerce, in which they see not only voice search — but voice shopping — playing prominently. Eventually, Walmart customers will be able to buy products by searching and shopping with Google’s AI-powered assistant, Google Home.

Voice search is just one of the ways artificial intelligence will impact the future of online retail. Revenue generated from the direct and indirect application of AI software will grow from $1.38 billion in 2016 to $59.75 billion by 2025, forecasts Tractica. There is sure to be more to come from retail’s hefty investment in technology.

AI-powered assistants and beyond, how will e-commerce marketers and consumers benefit from artificial intelligence? Here we unfold the trend and some of the most important current and future applications of artificial intelligence in e-commerce.

Voice Search and AI Assistants

If you’ve ever chatted with Siri, you know she has a way to go to improve her understanding of your requests. AI assistants on the market, like Google Home and Amazon’s Echo, have gotten better at understanding and delivering on our requests, but there is still plenty of room for improvement there, too.

To surface results that match searcher intent, algorithms must be trained to recognize and interpret numerous patterns in our natural language.

Voice recognition has been a tough problem to solve because of the countless variations in the way people speak. Unlike typed searches that map to relevant keywords, voice searches are more complex. They tend to be longer and less direct. To surface results that match searcher intent, algorithms must be trained to recognize and interpret numerous patterns in our natural language.

That’s where artificial intelligence comes into play.

A specific type of artificial intelligence, called a neural network, attempts to emulate how the human brain learns and makes decisions. It is the primary type of algorithm used in automated speech and facial recognition. (It powers Facebook’s facial recognition when you upload photos of people.)

For voice search and AI assistants to really take off, experts agree the experience needs to feel more like interacting with a human, and not a computer. Google Home is certainly making strides there. When users ask the device to add items to their cart, Google leverages all the data it has on its users to predict brand and retailer preference and price sensitivity when returning results.

For voice search and AI assistants to really take off, experts agree the experience needs to feel more like interacting with a human, and not a computer.

When the technology can interpret nuances of our speech, tone, and intent, we’ll see massive strides in voice search. And our interaction with AI assistants could move beyond the transactional.

Hyper-Targeting With Machine Learning

Finding the right consumers is becoming easier for marketers who can extract insight from the data available to them.

Marketers can go well beyond high-level demographics to target shoppers. Machine learning algorithms — another sub-category of artificial intelligence — find patterns in both online and offline data to understand what consumers would likely be interested in purchasing. Search history, purchase history, social profiles and interactions, and geolocation (thank you, smart phones) are veritable goldmines of consumer data.

Targeting with the help of machine learning is precise, and it can be contextual. Let’s take beacons as an example. The sky opens and rain begins to pour on your walk to work. You then receive a notification that a retail pharmacy nearby has plenty of umbrellas on sale, just when you need one. These benefits apply to retailers with a store presence, and to all e-commerce marketers looking to reach consumers across channels at the right times.

Search history, purchase history, social profiles and interactions, and geolocation are veritable goldmines of consumer data.

Machine learning makes it possible to piece together customer journeys and predict future purchases. When applying machine learning to social network data, like Facebook’s, retailers can target based on pages liked and even what friends have purchased. All of this data can fuel purchase recommendations retailers can serve on their websites, in emails, or in social channels.

Personalized Customer Experience

Artificial intelligence is making the search for customer insight faster and less subjective. In B2C retail, big market research surveys aren’t necessary to understand customer preferences anymore. Customer data is everywhere, and machine learning can help retailers determine the kinds of experiences consumers want.

Product recommendation engines run on continuously learning algorithms, which use every new action a shopper takes to personalize suggestions. The better you get at suggesting products consumers are interested in, the stickier your site becomes and the more loyalty you foster.

Artificial intelligence can help personalize your website experience for individual customer preferences. Large retailers employ data science teams to pull in data about purchase trends, customer loyalty, demographics, and browsing patterns that enable retailers to anticipate demand, identify high-value customers, and deliver relevant and personalized offers at the right time.

Customer data is everywhere, and machine learning can help retailers determine the kinds of experiences consumers want.

Even if a data scientist or a team of engineers to sift through customer data is out of reach, machine learning technologies on the market can surface gems of customer insight that can inform your marketing strategy. Remember, retailers of all sizes have access to their own data — it can be purchase behavior available through a CRM, or views of website click journeys through a free Google Analytics account.

Chatbot-Enabled Customer Service

Gartner forecasts that 85% of customer interaction will be handled without a human by 2020.

While that might be surprising to some, a data scientist wouldn’t flinch at this prediction. Improvements in artificial intelligence will enable chatbots to interpret what a customer is asking for in a wider variety of scenarios.

As it stands now, chatbots need a good deal of human oversight and are best employed in well-defined scenarios where mostly routine responses are expected. But just like most things that rely on artificial intelligence — bots can be trained. Part of what makes the evolution of chatbots possible is the plentiful training data available from online chat support.

Gartner forecasts that 85% of customer interaction will be handled without a human by 2020.

And because bots don’t need sleep or days off, chatbots will help retailers provide more reliable — and in the long run, cheaper — customer support. For retailers that need it, 24-hour customer support would be in reach.

Luxury retailers that offer impeccable in-store customer service and personal shopping assistants could employ a very smart chatbot assistant to deliver that high-end customer care to online shoppers.

Do you cringe at the thought of having chatbots handle your customer service? Consider this: Having a portion of the customer service function housed within a data center can help keep that function humming in the midst of employee turnover or when that unexpected snowstorm hits and your staff can’t make it to work.

Artificial intelligence has come a long way since the Turing test, and its implications in e-commerce retail are far-reaching. The long and short of what artificial intelligence can offer e-commerce marketers and consumers? E-commerce that is more convenient, more responsive to consumer needs, and ultimately even predictive of them.

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