The last four months have underscored the importance of humanizing communications with customers. Some brands have responded to the pandemic with what feels like endless variations of the same marketing message, which boils down to: “We’re in this together in these challenging times.” In reality, 80 percent of consumers said they’d be more loyal to a brand that really understood who they were and what they were looking for. Why has this level of human understanding remained out of reach for so many marketers?
In short, personalization is hard. Brands often have millions of customers across websites, apps, catalogues, stores, and various other channels.
Artificial intelligence and machine learning could change that — and the marketing technology world is on the cusp of a big revolution. Machine learning aims to solve things that are easy for humans to do, but complex for computers. And to do this, machines need to mimic human intelligence. Programs need to be able to learn, forget, and adapt.
When it comes to making sense of marketing data, machines excel – and, working in tandem with marketers, they add the personalized, human touch that consumers today crave.
If you move in the same social circles, it’s frustrating to keep re-introducing yourself. Imagine seeing someone several times, yet every time giving your name and answering basic questions, over and over again.
This is effectively what many brands do – they don’t have a unified view of their customers across all channels, such as websites, brick-and-mortar, apps, and catalogs. With information caught in silos, customers are a mystery. In fact, according to a recent Customer Experience Trends Report from Acquia, 83 percent of marketers reported that their customer data was siloed. So instead of greeting a loyal customer as an old friend, they offer a new sign-up discount. Instead of sending a long-time customer their favorite product to re-stock, they market a completely different product.
From a technical perspective, brands have tons of duplication – customers whose information is entered several times via different forms, with different email addresses, or in various channels, from websites, to mobile apps, to brick-and-mortar purchases. While human assistance is needed to clean the data, algorithms can quickly match and consolidate profiles.
Most people today would probably agree that marketing emails can feel like spam. Many brands send far too many emails. They also send them to the wrong people. For example, if you’re a shoe company marketing men’s shoes to a female customer who has never indicated interest in those products, you are, in effect, spamming the customer.
The problem here is segmentation and customer profiles.
Machine learning is extremely good at recognizing patterns, especially at scale. In minutes, they can process records and pick out patterns for millions of customers, while the proverbial old-school butcher might be able to keep track of and customize orders for only a few hundred customers. In this way, machines can scale up human relationships. Once brands have their channels and data unified in a digital experience platform, machines can make marketing more human by unifying all these interactions. This means your biggest fans feel recognized for their loyalty, while the neophytes are welcomed with open arms.
Intelligent algorithms can solve these problems. They can segment with great precision, allowing brands to target audiences with information that applies to each customer. Computers can understand a users’ buying patterns – and all shoppers are different. For example, some people like to add items to a cart and leave. Others like to browse and browse again. Yet these people may all, eventually, buy the same thing. With machine learning, brands can segment these users into finer slices, offering them information – such as discounts, offers, and follow up emails — that feel human and are truly personal.
So how can brands that are run by humans, selling products to humans, deliver a personal, human touch when so much of consumer brand interactions have moved online?
Many brands today are at a crossroads. Their customers, in many cases, are interacting with them solely through digital platforms. Even though it remains to be seen what will happen after this global pandemic, many of these digital habits will remain engrained for years. Customers have proven they want personal interactions with brands, but without feeling “creepy” about it — in fact, that same 2019 Acquia Customer Experience Trends report found that around half of consumers surveyed were uncomfortable when brands knew information that hadn’t been shared. There’s a clear line between personal and creepy, and brands can’t cross it.
Machine learning can be a part of the solution. Algorithms can unify data much better than humans, understand behaviors at scale, and make recommendations. By giving users the opportunity to interact and share information, and then personalizing experiences based on that information, brands can make every interaction feel intentional, genuine, and valuable.
In short — machines can make marketing feel less like a generic empathy play and more human.
Omer Artun is Chief Science Officer at Acquia and is the creator of Acquia AgilOne. He holds a Ph.D. in Computational Neuroscience and was a consultant with McKinsey & Company, consulting high-tech and retail companies on strategy development. He specialized in analytical areas such as pricing, direct marketing, customer segmentation. Omer was an Adjunct Professor of Marketing at NYU Stern School of Business, teaching graduate-level relationship marketing and analytical marketing courses.