Growing up, I wished I had a twin.
Thank goodness I didn’t. In all my fantasies, my poor hypothetical sister existed purely for the purpose of being in all the places I didn’t want to be but had to (in class, at the doctor, helping my father with yardwork, or doing dishes for my mom), so I could do the things my heart desired: jam out on the piano, play with the dog, or sleep over at a friend’s house. With a helpful twin by my side, gone would be the days of math homework and feigned enthusiasm for kickball. Goodbye, household chores. Hello, freedom.
Fast-forward a couple of decades, and, thanks to machine learning, this marketer finally (sort of) has the chance to achieve her dream—and I didn’t have to hire a full-time personal aid (or, I dunno, an unfortunate look-alike) to do it. Today, this branch of artificial intelligence serves as that trusted “same brain” tool that knows what I need out of my data and delivers it to me, almost without asking.
With the advent of AI and its warm welcome as a new form of marketing technology, we’ve had a lot of information and potential thrust at us in a seemingly short period of time. As a result, as explained by Carson Sweet of CloudPassage, “many companies are asking for machine learning tools to solve problems—even if they don’t have a clear idea of what these tools can do.”
Today, let’s dig into the second part of that statement, and see how brands (and marketers) are using these tools to work better, smarter, and faster than ever before.
According to CIO, Adobe has made an organized series of machine-learning-based moves over the course of 2016—all under the umbrella of its Adobe Marketing Cloud. The company’s suite of AI tools, which includes Adobe Analytics’ Segment IQ and Segment Comparison for Analysis Workspace, has a primary focus on audience insight. As Thor Olavsrud wrote:
Segment Comparison for Analysis Workspace is the first in what Adobe promises will be a series of audience analysis and discovery tools within Segment IQ. It uses machine learning techniques to perform automated analysis on every metric and dimension to which you have access. Nate Smith, senior product marketing manager, Adobe Analytics, says this allows Segment Comparison to uncover the key characteristics of the audience segments that are driving your company’s KPIs.
Through this tool, Olavsrud continued to explain, marketers will no longer be subject to broad-brushed generalizations of wide audience segments. Instead, it will essentially help pull out nuanced audience characteristics that are important to a particular brand.
Through the use of Saba Cloud, Hyatt Hotels has embraced the potential for this technology as a powerful employee training tool. While I can’t promise a robot will be there to clean up the inexplicable pile of pizza boxes you accumulated in your hotel room over the course of your vacation, I can say this much: the attendant who does could be aided by an artificially intelligent (and always learning) robot mentor. Westworld, this is not. But it’s undeniably effective and—let’s face it—super cool.
At Hyatt, the Saba Cloud learning system provides a set of machine learning algorithms called The Intelligent Mentor (TIM). The automated mentor observes employees’ behavior on the system and accepts their contributions to deliver personalized recommendations on content, connections and courses, according to Didi D’Errico, vice president of brand advocacy at Saba.
The software becomes smarter and more predictive as users engage with it, D’Errico said. The more an employee uses the system, the more recommendations are provided.
If you’re anything like me, you find that the act of scouring the internet for health and fitness advice is somehow more taxing than your daily workout. And with the ever-changing trends in diet and regimen recommendations, it seems like yesterday’s news is outdated by the time I’m hitting the snooze button the next morning.
Under Armour gets that, and it’s changing it: by building IBM Watson into Record—the brand’s health and fitness app.
UA’s app, which is designed to track a user’s health and fitness based on data from a variety of places (smart watches, third-party health apps, and data entered by the user into Record directly, to name a few), will now be able to provide diet and fitness advice. According to Business Insider, “It will base its coaching on the results of other people who have similar health/fitness profiles, as well as data pulled from things like nutritional databases, physiological, and behavioral data.” In essence, the app will serve as a personal trainer that exists at users’ fingertips.
Paralysis by choice is much less of a problem when you head into your favorite brick and mortar. More often than not, if you’re looking for a new pair of sneakers, you can head to your local shoe store, have a chat with the store clerk or a shopping assistant there, and leave satisfied with your new runners in tow.
In the digital shopping realm, however, this area leaves much to be desired. Suddenly, you’ve spent an hour poring over page after page of wedges and sandals you could wear on your next cruise—but you don’t know what’s too high, or which will be best for your excursions, or what color will go most perfectly with the three new dresses you also tossed in your digital cart.
In the Financial Post, Fluid’s former CEO Kent Deverell said it best:
“Essentially, digital commerce and digital shopping is great at things like price, shopping and convenience, but what it doesn’t have is what you get when you go into a really good retailer, and you talk to a sales associate, and you have a great conversation. A sales associate asks you meaningful, pertinent questions and then usually fairly quickly can recommend a set of products that are the right fit for your needs.”
That’s why The North Face has opted to use Fluid’s Expert Personal Shopper (XPS)—a virtual personal assistant—to help make suggestions for online shoppers, build out profiles based on their interests, and create an immersive user experience. This not only creates a welcoming and helpful environment for digital users, extending The North Face’s experience out into the virtual realm, but it helps the brand collect data it can use for future website iterations and products down the line.
Retailers aren’t limited to this marketing technology in the digital sense—as Macy’s has proved with its Macy’s On Call tool, there’s plenty of opportunity to use them to enhance the in-store experience tenfold.
According to Forbes, Macy’s new artificial shopping assistant: “is a cognitive mobile web tool that will help shoppers get information as they navigate 10 of the retail company’s stores around the US during this pilot stage.”
Shoppers can use the app as they would use a human assistant, asking questions (via natural language) about brands, product locations, and in-store facilities. “The initiative is based on the idea that consumers are increasingly likely to turn to their smartphones than they are a store associate for help when out at physical retail,” writes Rachel Arthur for Forbes.
Through artificial intelligence, brands are kissing grunt work goodbye and saying hello to the parts of their jobs they love most: the passionate, creative storytelling that connects with their audiences on human levels. As you look to take your marketing technology to new heights in 2017, let that passion and humanity be your guide. Think “connection”—then let machines handle the rest.
Check out the previous post in the series: “Marketers: You’ve Heard of Machine Learning, But What Is It?“