As a marketer, if you were to envision the ideal customer journey–the online experiences and interactions that a potential customer could have with your brand–what would that look like? The gap between those ideal digital or physical interactions and what’s possible based on the available technology has always been wide (billboard ads anyone?). But in the last few years the emergence and democratization of machine learning and artificial intelligence in the marketing world has narrowed this gap more than any technology before it.
Let’s rewind for a moment. Since the first online display ad was served on hotwired.com (now Wired) back in 1994, to the demographic, behavioral, and other types of data that’s now used to target ads to audiences online, we’ve been on a fast track to being smarter, faster, more scalable than ever in terms of delivering prospective customers content and products that they want when they want them. But the problem comes down to this: until recently, the interactions between marketers and their audiences have still been like Groundhog day — where instead of applying what we’ve learned about customers based on various types of digital interactions and touchpoints, we start from scratch in many ways, showing them the same ad or content disregarding various signals or data about what their interests, wants, and needs are or may be. The use of machine learning however, has propelled us into a new era of marketing where each day, hour, and millisecond is different and more informed than the last, with billions of data points at your disposal, getting us closer than ever to the holy grail of marketing — to confidently deliver the “right message to the right person at the right time”.
A 2017 survey of retail marketers in the US and UK found that the top barrier to adopting AI in marketing was “confusion or lack of clarity for what AI can be used for.” But 51% of marketers in another study said that they would be investing in AI and machine learning in the next 2 years, and use of AI is expected to grow at a higher rate than all other types of marketing technology such as marketing automation, social listening, and more. At the end of the day, marketers’ goals are pretty simple: generate brand awareness, increase engagement, and generate leads and revenue. So given that marketers believe in the potential of machine learning (based on the expected increase in investment there), how are they to overcome the hesitation in adopting it?
The good news is that marketers don’t need to become data scientists to understand use cases and apply machine learning technology to their own marketing campaigns. A good starting point is to look at how it is used today and what it can mean for interactions with your own customers.
How Does AI and Machine Learning Impact the Customer Journey?
To make it more real, let’s look at technology and tactics that marketers are deploying today to create better customer experiences and ultimately, win and retain more customers.
Discovering useful products or services without explicitly looking for them
If you go to a store or are searching for an item online, it’s great to be able to quickly find what you’re looking for and be on your way. But what if the right product or service was brought to you before you even consciously set out to find it? It’s like someone reading your mind, knowing that you were just about to run out of laundry detergent and needed to buy some more.
Years ago, Target famously (or infamously) began sending out physical mailers to customers that customized promotions based on their previous purchases. A father was outraged after learning that his daughter had received a mailer from Target with promotions for maternity wear, baby clothes, cribs, and more. After furiously asking a Target store manager why the company would be encouraging his daughter to get pregnant, the father called the manager back and apologized; his daughter was in fact pregnant.
Google, Facebook, Target, and most other major retail and marketing companies are investing in AI and machine learning to foresee the needs of their customers and present them with useful products and services at the right time. And technology exists today to democratize the ability for marketers to achieve this through paid search, online shopping, and more.
Finding What They Want Online, Faster
A person interested in taking their family on a cruise might use online search to look for options based on a multitude of factors: destination, accommodations, length, cost, etc. But if during that first search they receive incredibly specific search results — instead of having to try 18 different search variations or go to page 10 of Google — they have a better experience and can move on to making a purchase much quicker. By applying machine learning technology to their search campaigns, marketers at Windstar Cruises have created this type of experience for their prospective customers, and the results show.
You can see for yourself how Google applies this same concept to their organic search results, anticipating what terms you might type in before you’re even done typing.
Real-time Recommendations and Promotions While Shopping In-store
Imagine visiting a store in person — one that you frequent or at least have visited before — and receiving an email or push notification that suggests new items for you based on your own tastes, or offers a promotion based on an area of the store that you’re spending more time in.
Carrefour, a France-based retailer with over 12,000 locations in over 20 countries globally, placed electronic beacons in their stores to track and understand shopper behavior, and uses machine learning to send store visitors personalized promotions while they shop. After testing this technology in just 28 of its stores, the company reported a 600 percent increase in app users.
Dynamic and Personalized Pricing Offers
A couple that is considering a vacation to Hong Kong might have different criteria and ideas about how to buy then two business partners planning to travel there on the same date. In a perfect world, the traveler finds ticket options for what they perceive to be the right value, while the airline sells out the flight at a profit. With the use of machine learning and AI, airlines are among the first companies that have begun connecting market conditions with individual consumer information and behavior to get even closer to finding the perfect price for each customer — a win/win for everyone.
Online Customer Support that Learns from You
As you get to know a friend or significant other over time, you learn about tastes and preferences and can use that information to buy their favorite ice cream or find a thoughtful gift for their birthday. Shouldn’t business be capable of the same to better cater to our needs? Support agents often chat with web visitors for several minutes (a Zappos support agent once spent almost 11 hours on the phone with a customer!), which represents a huge opportunity to learn more about their customers.
Floral retailer 1-800 Flowers has brought this line of thinking to their online chat support experience. Using machine learning and language recognition, the retailer provides a unique selection of product options to the customer based on what the customer typed into the online chat.
How Will it Change Customer Experiences in the Future?
Retailers sending you items before you buy them. Back in 2014, Amazon was already thinking about “anticipatory” shipping, to begin sending items before the consumer even purchased them. This kind of foresight could create an incredible customer experience in a variety of ways — automating the delivery of anything from groceries to toilet paper to other critical day to day items.
Image recognition as a method for finding what you need. People like to take photos for all sorts of reasons, in fact globally over 1 trillion photos are taken a year on our smartphones. What if we could reliably use photos to identify an unlabeled product, or allow businesses to read your images to learn more about your tastes and preferences?
Experts weigh in. After asking dozens of marketing executives and thought leaders to share their thoughts on the future of machine learning in marketing, the key theme that surfaced was that ultimately it will automate the ability to create more unique, personalized experiences with target audiences.
If there is still any question as to why now is the time to explore how to apply machine learning to your marketing strategies, remember this: machine learning is what will truly scale the marketer’s ability to each the right audiences, learn from them, and create personalized communication and experiences for them.