To be able to guess what consumers want (and expect) while they’re shopping is every marketer’s dream. Using marketing technology is less stressful for consumers, as they don’t have to work to find out what they need. It’s also less stressful for a marketer, as there’s no need to bombard a consumer with messaging because the need is clear.
Enter artificial intelligence (AI)—which, according to Stanford University, is the “making of intelligent machines, especially intelligent computer programs.” It’s a program that can understand humans but also isn’t confined by human emotions.
In the marketing world, that’s huge: machines don’t waffle over what list segment to choose; rather, they assess potential gain and then make decisions. At a small level, AI can mimic human personalities and adjust itself to suit the needs of the person it’s speaking with. Whether you’re a local hair salon or a national convenience chain, there’s a particular voice for your business, and AI can learn to relay that back to a consumer. On a larger level, AI can process lots of data in a short amount of time.
When The North Face teamed up with IBM’s Watson, the result was nothing short of fascinating: the platform saw a 60 percent click-through rate to the jackets the artificial intelligence system recommended, and a majority of users said they’d use the system again. Consumers didn’t have to scroll through pages and pages of white grids, each containing jackets with different details and advantages. They also didn’t have to use the company’s e-commerce search feature, which may or may not provide the consumer with the correct item depending on what words the consumer typed. By asking the consumer the five W’s, (who, what, when, where, and why,) Watson could accurately suggest jackets that are suitable for a consumer’s needs.
The North Face isn’t the only one experimenting with IBM’s Watson: Staples has also teamed up with Watson to create its Easy Button, which allows anyone with an account to easily ask its AI about shipment statuses, order supplies, or ask questions related to customer service. It’s just like Amazon Inc.’s Alexa or Alphabet’s Google Home.
Like The North Face’s interactive shopping experience, the Easy Button will eventually be able to recommend products to customers based on their needs or what they’ve ordered in the past. Staples also found that employees used the Easy Button—and some creative thinking—to have the AI help out with office tasks like remembering passwords, learning about favorite office restaurants for easy ordering, and giving current weather information. The company also believes that it will eventually provide quick calculations for employees. In terms of other retailers, Macy’s and Sears have their own Watson-powered cognitive computing tools.
AI doesn’t have to be integrated in a website or a physical product like the Easy Button. It can also be used to write better emails. Justin Khoo a senior developer at Email on Acid, said marketers will eventually be able to use AI to write better copy, or have variable copy, that would changed based on the consumer. Instead of consumers receiving the same email from one campaign, AI will be able to target the right consumers within specific target segments without a marketer having to lift a finger.
Machine learning programs, he noted, like Boomtrain and Conversica, should be on the top of every marketer’s mind. Finding the right AI tool to work with, especially if you don’t have lots of money to shell out for Watson, is a necessary part of a marketer’s job in 2017. Without it, more time will be wasted on the tedious work of data analysis when a machine could do the same task much quicker and more accurately. Khoo wrote that marketers should see AI as a plus: it’s not taking over your job but freeing up mental space for creative thinking, and it lets you devote your energy to bigger ideas.
For retailers with brick-and-mortar locations, finding a way to integrate AI is paramount. Some retailers are experimenting with foot traffic data to understand what consumers are gravitating towards in stores. With more data, a retailer can begin to track specific consumers (perhaps ones that have opted into a loyalty program) and understand better what types of purchases they’re likely to make into the future. This is where geolocation technologies can kick in: if a customer bought a pair of shorts in the same location (or even a different location but within the same retailer), a store equipped with predictive AI could serve up coupons specific to that person so he or she might be more enticed to make a second purchase.
In the near future, beacons, sensors, RFID tags, and other such marketing technology will become omnipresent. That means that data will be floating everywhere, all the time in a sea of numbers and parameters. In this scenario, it’ll be best to have an AI-driven approach to make sense of it all. It also means that a machine will figure out how to turn that data into something personal to each consumer, faster than any retail marketer can. In time, AI will automatically help customers choose the best product for their needs and even the best products for people they’re buying gifts for, while also managing inventory and other basic processes. It’s not meant to replace the marketer, but to enhance the marketer’s vision for the brand.
Featured image attribution: The Johns Hopkins University Applied Physics Laboratory (JHU/APL)