We’re on the cusp of a one-to-one marketing revolution, experts say. If that’s the case, why can’t one of my favorite retailers figure out that I am, in fact, female?
Sadly, of all the emails that litter my inbox every day, only a fraction are highly relevant to me. (And many, like the aforementioned retailer, deliver content that’s highly irrelevant.)
But it doesn’t have to be this way.
Extreme personalization has the potential to make brand content much more relevant and appealing to average consumers like me. Like a majority of Americans, I welcome hearing from brands—when the content is relevant. The problem is that most of the time, it’s not.
AI (artificial intelligence) and deep learning software could upend this scenario. Thanks to machines, brands can learn how to market the right products, at the right time, using the right channel, to a specific, individual customer. This kind of extreme personalization has huge potential to help brands break through the noise and forge stronger connections with their target audience.
Sure, using your customer’s name in an email subject line will help improve open rates. But as far as personalization goes, that tactic is barely scratching the surface.
Unfortunately, going beyond the obvious name-based personalization tactics can be tricky. Brands might try to segment their audiences and provide different messages to different demographics, perhaps based on age or location. There’s a problem with this approach, however. Two 20-something women may both live in Atlanta, but they may have highly diverse preferences in terms of music, hobbies, social activities, clothes, and more. That’s why one-to-one marketing is so powerful: It moves beyond messages aimed at demographic segments to messages aimed at the individual, ensuring each person gets a message targeted specifically to him or her.
While one-to-one marketing has great potential, many brands are struggling to figure out how to translate customer data to personalized content and experiences. While 79 percent of brands surveyed by Sitecore place a high priority on personalization, only 12 percent have the ability to collect data at the individual (versus customer segment) level, according to a press release. Around a third of brands say they lack the skills needed to analyze the data collected, and 42 percent lack the ability to integrate data collection.
Meanwhile, 96 percent of consumers say there is such a thing as “bad personalization,” which can encompass brands using outdated information about them, getting personal customer details wrong, or making incorrect assumptions about what consumers want based on single interactions. Creating personalized content isn’t enough—it has to be the right content for the right person.
Psychologically, people are motivated by personalized content. A study from the University of Texas found that people perceive greater content enjoyment when exposed to a “customized online environment.” For customers, better content is a trade-off. They’re willing to give up more personal data, as long as they get a more personalized experience in return. A Salesforce study found a majority of consumers will share data in exchange for personalized offers or discounts (57 percent), tailored product recommendations (52 percent), or personalized shopping experiences (53 percent).
These days, content personalization is the expectation. Call it the Netflix effect: We now expect retailers and other brands to be analyzing our purchases in order to deliver more relevant suggestions, content, or product discounts.
Amazon, already notable for offering product recommendations, is upping the ante with a new feature called “My Mix.” Amazon already offers suggestions based on past purchases and browsing history, but “My Mix” is a little less like your shopping to-do list and a little more like Pinterest. “My Mix” is populated based on items you heart across the site, offering a Pinterest-like discovery experience for new products. The shop is refreshed several times a day, Tech Crunch reports.
Not all personalization attempts are successful. I recently received an email from Airbnb reminding me of a trip I took a year ago. The trip, I should note, was fantastic; the email content, on the other hand, was anything but. Instead of prompting me to dream about future destinations, the email included a few rental listings for seemingly random big cities. For all the times I’ve used the platform—for quick weekend trips to two-week stays in international locales—I was surprised the email wasn’t more personalized to help me discover new destinations. The context was there with the one-year reminder of my trip, but the content wasn’t up to snuff.
B2B brands are diving into the personalization game, too. Online video platform Vidyard created a personalized video for one of its customers, including a handwritten white board with the customer’s name and a unique script, as detailed by HubSpot. The video goes beyond the expected by taking content personalization to the extreme.
These examples highlight several aspects of the future of personalization. Importantly, brands are moving away from marketing to segments and instead marketing to an audience of one. Thanks to the power of machines, marketers can understand how to reach those individual customers at the right time, on the right device, with the right message. Instead of guessing about what messaging is working and what isn’t, AI can help understand individual preferences based on actual consumer insights.
While users have demonstrated a willingness to trade data for tailored messaging, that doesn’t exonerate marketers from treading lightly with how they use that personal information.
Marketers need to be upfront about whether they collect customer data and how they use it. They also need to give customers the ability to opt out, if they desire. No consumer wants to feel like their personal data is being used inappropriately. But part of mitigating the “creepy” factor of using consumer data is showing consumers the benefits of sharing that data. When people see the “Because You Watched” section on Netflix, they appreciate the fact that Netflix is helping them discover great movies and TV shows based on their viewing patterns. Showing users that sharing data provides value can help ameliorate privacy concerns.
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Featured imaged attribution: Joshua Earle