The average customer is online pretty much all the time, creating an abundance of data for today's businesses. If marketers aren’t using that data to offer more efficient experiences, it might be time to ask what it’s for in the first place. Here are a few ways brands have stopped sitting on their data and actually used it to solve some of the industry’s most annoying problems.
Now that our summer sojourns to unplugged locations are over, most of us are back to the grind. Now, more than ever, that means being connected to the internet at all times: at work, at home, at play, and on our commutes. In fact, according to recent Pew research, 26% of Americans report being online “almost constantly.”
And all that connectedness is creating a unique problem: an overabundance of data. There are 2.5 quintillion bytes of data created every day. 90% of all the data ever created was generated in the last two years.
With all that data rolling in, you’d think companies would have a crystal-clear picture of who their audiences are and what they want. But while we might be rolling in data, only 15% of companies say they can consistently identify their audiences. And those plugged in audiences giving up unprecedented amounts of data? They’re fed up. Nearly half report feeling annoyed by ads on social media, and 42% expressing distaste ads that appear on a company’s own website.
But the great thing about the data age is that it provides solutions for companies looking to connect the dots and create them. Here are a few ways brands have stopped sitting on their data and actually used it to solve some of the industry’s most annoying problems.
Customers have always hated waiting, but in the digital age, when most information is available at the touch of a keyboard, that hatred has increased exponentially. Just three years ago, customers were willing to wait 13 minutes for relevant information. Now? Two-thirds of consumers are only willing to wait two minutes. And a full 13% of respondents to a recent survey on wait times said that “no wait time was acceptable.”
The word “chatbots” is a bit overused in industry parlance at the moment, but one benefit to bots that’s not talked about enough is the feedback loop they create for actual human customer service reps. According to Michael Brehm of i2x, “There’s an overestimation of what chatbots can do. But chatbots are built for a very specific type of conversation, so it’s very narrow AI.”
However, more and more, companies are using chatbots for twenty-four hour answers to simple customer questions and also to collect data in order train real-life customer service reps on the best answers for more complicated inquiries. This frees up more qualified human beings to answer questions and ultimately improves the customer’s overall brand perception. And while many AI solutions are cost-prohibitive, chatbots are actually fairly affordable for small-to-medium businesses (SMBs), with many companies offering simple machine learning solutions for customer service.
Even if you’ve only looked at a single digital marketing study in the past five years, you’ve probably still internalized an ubiquitous industry maxim: consumers like video. Some recent studies say that as many as 73% of consumers have bought a product after watching a video, and 81% of companies using video saw an increase in sales after incorporating them.
However, as consumers are increasingly inundated with video, they’ve also grown a bit bored. According to consumer surveys, 52% of audiences think pre-roll video is “interfering,” and 51% think less of brands that use auto-playing video ads.
So if consumers buy more after watching videos, but don’t want to be forced to watch videos, what’s the solution for serving ads that won’t just annoy them? AI. Recently Samsung experimented with AI-enhanced video by adopting LoopMe’s data management platform, which used machine learning to identify targets with older handsets by analyzing data based on location, demographic, and device. Samsung was then able to target not only demographics that might be interested in their product, but specifically those customers who needed a new handset. As a result, more than a third of the targeted audience watched the video tailored specifically to their needs, a 20% increase in effectiveness against a control group.
Customer data generally comes from a variety of touchpoints—interactions on social media, website visits, email lists—the possibilities are endless. But some of the most important data customers give about themselves lies in their actual visits to physical stores. What’s a better insight into customer behavior than the times of day they’re visiting, what they’re buying, and even who they’re visiting with? For years, many businesses have left that data unused until AI made it easier to connect the dots and use point of sale (POS) data in conjunction with other data to deliver relevant messages.
For example, casual dining restaurant chain T.G.I. Friday’s recently used AI to connect its POS data to other customer insights, and the result was highly targeted insights that could predict not only when customers were dining but what their favorite foods were. As a result, online orders tripled.
The average customer is online pretty much all the time, which means they’re being asked to give up their data every time they click an article or open a website. If marketers aren’t using that data to connect the dots and offer more efficient experiences, it might be time to ask what it’s for in the first place.
