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Last year, PWC named AI as one of the eight essential technologies in business and 38% of businesses employed AI in its systems.
That percentage is expected to grow to 62% this year.
IDC estimates that the AI market will grow to be more than $47 billion by 2020, and Gartner predicts “more than 40% of all data analytics projects will relate to an aspect of customer experience.”
Let's see how customer experience and Artificial Intelligence are blending together to deliver superior customer needs and increase customer satisfaction in the years to come.
Table of contents
- Are we delivering empathetic customer experiences?
- The shifting battleground: CX
- AI and the zettabytes of data
- Six types of AI engines helping brands create empathetic CX
- The next steps for brands
Are we delivering empathetic customer experiences?
Ever lost or misplaced your phone? Or how about the time when your computer or hard drive crashed and there were key files and data you hadn’t transferred yet to the cloud?
Remember how distraught and helpless you felt?
Now imagine if the customer service person you come in contact with was an imperious know-it-all? Imagine how that experience would add to the mental and emotional strain you already felt. Imagine how you would perceive that brand moving forward? In moments like these, it’s true that you want someone to fix your problem, but more importantly, you want someone to listen and acknowledge your distress.
Apple understands this all too well. The company includes an empathy guide in its training manual for frontline workers providing customers with device support in Apple retail locations.
Source: How To Be a Genius: This Is Apple's Secret Employee Training Manual
The training manual teaches Genius Bar employees how to assess what the customer needs based on their body language, and even suggests phrases to use depending on the customer’s specific needs at the time, like “I can appreciate how you feel…” which is a suggested phrase listed in the manual.
Today, nailing the consumer experience means going above and beyond, taking the time to understand customers, and applying insights to every aspect of the business, from new product development to call center training to designing a comprehensive user experience. The only way that businesses can do this effectively is by having actual empathy for the customer experience.
Here, we define empathy as a brand’s ability to experience their own product or service from the point of view of their customer.
There is no current shortage of brands claiming to deliver empathetic customer experiences.
But have we as an industry actually delivered?
According to Bain & Co., 80 percent of companies say they deliver 'superior' customer service — however, only eight percent of people think these same companies deliver 'superior' customer service.
The shifting battleground: CX
These harsh realities have made customer experience the new battleground at the top of business agendas today.
But while budgets and platitudes may continue to focus on CX inside boardrooms, the speed of technological change and the ephemeral wants and needs of consumers make this a quickly moving target.
And it’s easy to miss.
Consider how we got here: the modern manufacturing age — roughly between 1900 and 1960 — was marked by few pools of capital able to fund and maintain factories. As a result, the marketplace saw little legitimate competition, and an oligarchy of industrialists owned much of the global supply chain.
Starting in 1960 there were transformative changes to the industry, highlighted by globalization, deregulation and free trade deals that made it possible to manufacture goods more cheaply in other parts of the world. Even though customer experience were still important, it was price and distribution that were the real decision-makers for customers.
Then, in the 1990s, information and technology became readily available and accessible to the average consumer. This Information Age brought with it a shift in power dynamics, from sellers to buyers as customers now had information to easily compare brands.
The customer experience became a part of a business’s product and service — part of research & development, content marketing, public relations, social media, mobile presence and usability, and website design.
Today’s connected life — there are 4.3 billion mobile phone users worldwide — will see mobile experiences as the key touchpoint for businesses. Modern consumers want a seamless and integrated digital customer experience that ties into various devices and screens.
That means everything in the customer experience, from targeting to messaging needs an empathetic approach. Accenture found out that 44 percent of customers are “frustrated when companies fail to deliver relevant, personalized shopping experiences”.
Consumers today are increasingly less forgiving after a bad experience:
According to a study by WOW Local Marketing, 52 percent are less likely to engage with a company after a bad mobile experience — that includes everything from poor design, missing content, and even slow loading times. In Right Now Technologies’ 2011 Customer Experience Impact report, nine out of every 10 customers said they would walk away after a poor customer experience to conduct business with a competitor instead.
But it’s not all dire news. Studies have shown that customers will pay more for a better experience. After all, how many of us have paid for a more expensive airline ticket because we prefer the experience we have with a particular airline? Study after study show that good customer experience will boost even your stock value:
CX is an opportunity.
Source: The Customer Experience ROI Study
Businesses that truly understand the demand for empathetic design in the customer experience know that they can’t do it with human help alone. There’s just no way for humans to deliver against these expectations. Forward-thinking companies are turning to machine learning and massive data to make better decisions on their customers’ behalf.
According to IDC’s 2016 Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, the market for cognitive/AI solutions is expected to experience an annual growth rate of 55.1 percent between 2016 and 2020.
Using the powerful combination of AI and data means businesses have a better chance of giving customers exactly what they want — and even preemptively anticipate their needs before they’re even aware.
It’s this kind of empathy from brands that deepens trust and keeps customers coming back.
AI and the zettabytes of data
Think about how much data is produced everyday, from photos to videos to songs to text messages. The total amount of data in the world was 4.4 zettabytes in 2013 and that number is expected to rise to an astronomical 44 zettabytes by 2020.
To put this in perspective, one zettabyte is equivalent to 44 trillion gigabytes and to break that down even further, one gigabyte is enough data for the books needed to fill a
30-foot shelf. That’s a lot of collected data.
So how can companies make sense of all the information, especially considering most of it is unstructured?
They can’t. Not with human employees in real-time anyway.
In short, this means that brands that stand out during the customer journey have to turn to artificial intelligence to personalize experiences by identifying areas that are relevant to individual customers.
If empathy is all about understanding and being aware of and sensitive to the experience of another, then empathetic customer experiences should be focused on recognition and responsiveness.
Six types of AI engines helping brands create empathetic CX
Empathic thinking requires thinking the right systems in place so that you can proactively respond and resolve issues that come up at the speed of human expectation. While only a few brands employ empathic design well, the ones that do should serve as examples for the rest of the business world.
Below, we’ve listed six examples of AI engines that help brands transform the customer experience through empathetic designs.
WHITE PAPER FREE DOWNLOAD: How the most prolific companies in the world are using AI to deliver a better customer experience
1 / Recommendation engines
Recommendation engines are probably the most common form of machine learning and currently used largely in online retail and media industries and rely on algorithms based on the customer’s past behaviors and patterns. Like receiving a gift from a friend who really knows you. Brands implementing recommendation engines connect the massive data they collect to personalize every aspect of the customer experience.
Perhaps the most influential recommendation engine in the world today is YouTube’s Recommended Videos on their infamous right rail. Of the more than ONE BILLION hours of Youtube we consume, fully 70 percent of it comes from these algorithmic modules. That’s a lot of cat videos, man.
Source: YouTube's AI is the puppet master over most of what you watch
In the future, we could see recommendation engines more prevalent in industries with vast amount of data like healthcare, where AI can help personalize care by taking into account patient history, lifestyle information, medical records, and more. Algorithmically recommended treatment courses, or specific prescriptions are not out of the question.
2 / Predictive Searches
Predictive Searches allow web visitors to get results faster by automatically populating results while the user is still typing. Typically, there is a drop-down list that pops up during the search, which guides users to potential results. When thinking of predictive searches, most of us think of Google’ Autocomplete invented in 2004. Then, in 2010, Google’s search technology expanded to include Instant Search, which uses machine-learning to predict what it thinks you will type, simultaneously streaming results for those predictions in real-time.
Many brands have since adopted Google’s original technology, but there are some drawbacks. In 2017, Google announced that it would drop instant search as its default setting given most searches are now done on mobile, and loading results for predictive searches on a limited screen ends up being a poor user experience.
A Google spokesperson said at the time:
“We launched Google Instant back in 2010 with the goal to provide users with the information they need as quickly as possible, even as they typed their searches on desktop devices. Since then, many more of our searches happen on mobile, with very different input and interaction and screen constraints. With this in mind, we have decided to remove Google Instant, so we can focus on ways to make Search even faster and more fluid on all devices.”
Even after developing an empathetic design — Instant Search — Google continues to find ways to be even more empathetic by thinking about how the product can be fast and fluid on all devices, for all users!
3 / Virtual Assistants
Virtual Assistants, like Amazon’s Alexa, Microsoft’s Cortana, Apple’s Siri, and Google Assistant, range from chatbots to more advanced systems that are changing what customer engagement looks like.
- Google Duplex is a new capability of Google Assistant that can make calls on your behalf and book your next hair salon appointment or table at your favorite restaurant for you. And the person on the other side of the phone won’t even notice she’s talking with a bot.
Source: Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone
Virtual assistants are definitely a growing niche, so why have only 10 percent of enterprises in the U.S. employed a virtual agent? Mainly because there are still a lot of challenges that can interfere with the digital customer experience. For instance, while virtual assistants are improving greatly with natural language, most of them are still far behind humans when it comes to understanding slang, typos, misspellings, or complex grammar.
4 / Natural language processing
Natural language processing is AI that can process massive amounts of natural language data and can therefore, understand human speech the way it is spoken. When thinking of NLP, an Amazon Echo often comes to mind with its voice recognition, but NLP’s technology holds promise in a lot of industries, including healthcare where it can help with faster diagnosis by finding patterns in a physician’s unstructured notes. Think of how many lives can be saved with AI mining our health records!
Think about Skype Translator, that can understand several languages at the same time, in real-time, which can encourage conversations between people who speak different languages.
5 / Sentiment Analysis
Sentiment Analysis evaluates voice inflections to determine the emotions, attitudes, and opinion in normal human conversation to determine what’s actually being said.
Vibe is a product created by Tokyo-based software company AIR that can scan conversations on workplace communication tool Slack to determine team morale. The product analyzes keywords and emojis used during conversations and places the team’s mood into five emotions
At YML, we used sentiment analysis to understand which company is loved the most: Uber or Lyft?
Source: Uber vs Lyft – Who is loved more? A deep dive analysis using Google’s Sentiment Analysis API
6 / Computer vision
Computer vision uses machine learning models to teach computers to see things the way humans see them.
A company doing a great job is Uru, that uses computer-vision to find spaces in videos where native advertisements can appear to create a non-obtrusive, uninterrupted, more organic experience for users.
For instance, the company’s algorithm identifies spaces, like a blank wall or the board of a snowboarder, where ads or brands graphics can appear. The startup has caught the attention of the industry’s top accelerators and investors.
The next steps for brands
The rise of the internet has provided us with various ways to communicate and interact. Even brands now communicate with their consumers through multiple platforms and channels, so it makes sense that customers expect you to “get” them.
After all, customers know brands are tracking, personalizing, and optimizing every step along the customer journey, so why shouldn’t they be more empathetic to CX? With so much competition out there, the only way a brand can have a competitive advantage is by maintaining an obsession with customer experience.
Once a brand has determined that they want to create empathetic experiences, they need to then do two things: (1) focus on results, and (2) focus on small wins.
When focusing on results, brands should identify the tasks they want to tackle first, then determine the optimal technology to help them accomplish those tasks.
This can get a little tricky with various AI technologies competing in the market, from machine learning, chatbots, virtual assistants, robotics, natural language processing (NLP), and much more: in order to achieve business goals, businesses need to think about the big picture, then work backward.
For instance, if your customers want 24/7 support, then it might be worth it to invest in a chatbot experience so customers aren’t waiting to get through to a human employee. Or if customer satisfaction seems to be an issue, consider a technology that can take the data you already have and connect them with what customers may need, based on their individual preferences. Customer satisfaction will surely improve if businesses present relevant recommendations rather than spewing out random offers.
Finally, brands need to focus on small wins if they want a chance at any of the big wins.
How do you know which small win you’d like to tackle first? Identify the low-hanging fruit. What complaints are you getting the most from customers? Tackle that area first.
As soon as you’ve identified the problem, it might be tempting to use AI to solve dozens of issues right away. This is not the best way to start improving customer retention quickly. Instead, if you want to make the most impact, you’ll want to tackle what’s easy to measure and achievable first.
In other words, think big, start small. What small wins can you achieve in the next few weeks or months?
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