Conversational AI: An Introduction

Guest post by Nine Connections

Is customer service dead?

The other day, my 73-year old father, was grumbling about something he read in the news about automation of processes at the local bank.

“People don’t talk anymore. In my day, customer service meant talking to someone, saying hello, asking how your day was. Now it’s a recorded voice or reading tiny print online. Customer service is dead.”

That got me thinking. The role of a traditional customer service representative has evolved over the years. Once the domain of primarily the service and hospitality staff, the role of customers and our relationships with them has seen several costume changes — the phone IVR, surveys, forms, smiling uniformed people, you name it. But even as the modes change, the role of customer services and engagement has only just increased. Today customer relationships have become a full-fledged industry. 70% of buying experiences are based on how the customer feels they are being treated. The more people feel they’re being listened to, the happier they are and the more money they’ll spend — or that’s the hope. An Adobe report even suggests that customer service can deliver a higher ROI than marketing. Customer service, once upon a time, used to be about happy people, lots of solicitous questions and a solution with a smile. While it’s certainly true that the human factor seems to have declined over the years, the key tenets of a human interaction customer service have remained — conversations, solutions and a smiling demeanor.

So can a bot — the latest entrant into the customer service role — actually deliver these admittedly-human qualities?

Chatbot as the perfect concierge

Businesses that recognize how much time consumers spend on messaging apps such as Facebook Messenger and Slack have developed automated messaging technology. In 2015 messaging apps surpassed social networking apps, and chat apps have higher retention and usage rates than most mobile apps. Today, brands are looking at bots to become the next concierge, to understand what the customer wants, which direction they’re headed on, to involve them in interesting content, spread brand awareness and indeed, carry on conversations with a smile. But is all of that realistically possible?

On paper, it’s the perfect solution. Bots are machines, easily duplicated and incapable of human drama. They can be taught to function perfectly with a specific set of rules or through machine learning. These capabilities, limited as they are, can be trained to emulate the perfect customer service person’s skills — kindness, patience and solution-oriented. A machine can be taught to never be sarcastic and to always have a listening ear. And because it doesn’t have human failings such as fatigue or just being an asshole, it’s becoming an increasingly widespread phenomenon.

Any company with a chatbot interacting in the marketplace has the opportunity to gain valuable customer information. This has benefits in several areas — more personalization, targeted marketing, sales strategies as well as manpower allocation.

Not there yet

While bots were also a hot topic at the recent Corporate Social Media Summit, the jury were admittedly slightly skeptical. Bots are still very much in their nascent stage. And there have been several failures. In the rush to develop the next Siri or Cortana for their businesses, what most companies have ended up with are simplistic, underdeveloped tech with limited capabilities and faulty data. Of course there are the filthy people of the internet. It took less than 24 hours for Microsoft’s Tay to turn into into a filthy Nazi racist troll and two weeks for the cute little hitchbot to become roadside shrapnel. Even if the world were a perfect place, everyone was sunshine and unicorns and keeping empathy and other qualities aside, the actual functions and solutions given to customers by these bots need to work. That requires very skilled developers — but even they aren’t free of error.

Having said that, the possibilities for a bot are immense. Even though the big tech companies haven’t quite cracked how to make it work. Our co-founder Chris has a strong vision on the problems with Conversational AI, and perhaps more importantly — he offers solutions. He will share his vision 26th August on Startup Friday (still some spots left) and will start to share his vision in our blog series about Conversational AI that’s coming up here on Medium.

I know I will be paying attention. My own life have tons of bots — from the local Asian store from online magazines to Facebook to even my fitness wearable. Chatbots might very well be the face of the future one day. Now if they only knew how to smile.

6 Best Machine Learning Courses for Beginners

Machine learning. Yes, it’s one of the most popular buzzwords in tech today. And no, it does NOT involve robots replacing humans and sitting in classrooms. We’ve gathered 6 of the best machine learning courses for someone who has no idea what machine learning is.

According to TechTarget, “Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.” Machine learning is behind the creep who can identify the faces and locations in your photos. It powers those smart thermostats that can learn about your daily routine and automatically adjust the temperature when you leave the house and come back. It’s the wizard behind the curtain for many speech recognition technologies. It’s the technology in Woveon that learns your behavior and helps you prioritize your customer conversations.

Sound fascinating but a little overwhelming? Want to learn more about machine learning but scared about not being able to understand? Don’t have a technical background? No worries! There are tons of people out there in your same exact position, and they’re scraping the Internet for resources that are easy to understand. We’ve got your back with these…

6 Best Machine Learning Courses for Beginners

Machine Learning

Stanford University

Price: free, $79 for certificate (financial aid available)

Length: 10 weeks

machine learning courses beginners stanford

This machine learning course has received excellent reviews from students. It offers a structured combination of readings, videos, practice quizzes, and graded assignments (with flexible due dates if you work better with deadlines!). It is very suitable for beginners because it even reviews the mathematical background you’ll need for the course, such as linear regression with one variable and linear algebra. It even talks about cool applications of machine learning, such as smart robots, computer vision, medical informatics, etc. (‘Cause who doesn’t want to know more about smart robots that we can command to do our work for us.)

Professor Andrew Ng is the Co-Founder and Co-Chairman of Coursera as well as the Chief Scientist and Vice President at Baidu. His work focuses on machine learning, particularly deep learning. He was a leader in the creation of Stanford’s Massive Open Online Courses platform and the “Google Brain” project that developed deep learning algorithms.

 

Learning from Data

California Institute of Technology

Price: free

Length: 10 weeks

beginner machine learning courses california institute technology

Yaser S. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science, Co-Founder of the Neural Information Processing Systems conference, recipient of several national and Caltech teaching awards, such as the Feynman Prize, and co-author of Learning From Data, a bestseller on Amazon. He has 9 years of experience as a technical consultant for Citibank.

 

Principles of Autonomy and Decision Making

Massachusetts Institute of Technology

Price: free

Length: self-paced

mit beginner machine learnings course

Brian Charles Williams is a Professor of Aeronautics and Astronautics who built the first fully self-repairing, autonomous space explorer, Remote Agent, that was put into use in May 1999 onboard the NASA Deep Space One probe. In 2000, as part of the Tom Young Blue Ribbon Team, he evaluated future Mars missions. He is also part of NASA Jet Propulsion Laboratory’s Advisory Council.

Emilio Frazzoli is a Professor of Aeronautics and Astronautics and Director of the Aerospace Robotics and Embedded Systems (ARES) group. He received the NSF CAREER award and IEEE George S. Axelby award.

 

Machine Learning A-Z™: Hands-On Python & R In Data Science

Udemy

Price: 205 AUD (Bonus when they have promotions to get the course at $12.99AUD!)

Length: 40.5 hours

beginner machine learning courses udemy

Kirill Eremenko holds a Bachelor of Science degree in Applied Physics and Mathematics from the Moscow Institute of Physics and Technology and a Master of Commerce, Applied Finance, and Professional Accounting from the University of Queensland. He was trained at Deloitte Australia and has 5+ years of experience as a Data Science management consultant in transport, finance, retail, etc. He teaches Data Science and Forex Training courses on Udemy.

Hadelin de Ponteves has degrees in Mathematics and Engineering and a Master of Research in Machine Learning. He was a Data Engineer at Google and has 4 years of experience in data science and consulting.

 

Understanding Machine Learning

Pluralsight

Price: $29/month or $299/year for entire Pluralsight platform, 10-day free trial

Length: 40 min.

beginner machine learning course pluralsight

David Chappell is Principal of Chappell & Associates, a law firm that advises technology firms on law and business. David has consulted for Stanford, Target, HP, Microsoft, and IBM, spoken at 100+ events, and led seminars in 45 countries for tens of thousands of attendees. He also teaches courses on IT Innovation and Cloud Computing on Pluralsight.

 

VideoLectures.Net: Machine Learning Section

Price: free

Length: self-paced

beginner machine learning course videolectures

Includes 3,500+ videos of experts in the field delivering talks at conferences, workshops, summer schools, activities that promote science, and other events. Think of it as a database of machine learning courses.

VideoLectures.Net is a project backed by University College London. It was a recipient of a UNESCO World Summit Award in the e-Science & Technology category at the United Nations Summit on the Information Society.

BONUS

Think you’ve mastered the beginner level machine learning courses? Feeling ambitious for a new challenge? Check out this intermediate level course.

Intro to Machine Learning

Udacity

Price: free

Length: 10 weeks

beginner machine learning course udacity

  • Katie Malone graduated from Ohio State University with an undergraduate degree in Engineering Physics and from Stanford with a PhD in Physics. She works as a data scientist in the research and development department at Civis Analytics, a startup in consulting and data science software.
  • Sebastian Thurn is the founder of Udacity and a Research Professor at Stanford who invented the autonomous car and led the Google Glass project. He has been recognized by Fast Company as the 5th Most Creative Person in Business and by Fortune as one of the 50 Smartest People in Tech.
  • Udacity aims “to bring accessible, affordable, engaging, and highly effective higher education to the world” and provides Nanodegree credentials and programs that are catered to aspiring data analysts, mobile and web developers, etc.

Did you find these machine learning courses helpful? Comment below the most interesting thing that you learned about machine learning!

Looking to apply your newly acquired knowledge on machine learning to Internet marketing? Make sure you make a plan first! We have for you 8 Marketing Plan Templates to Blow Your Competitors Out of the Water.

Want to use a software platform that employs Machine Learning? Check out Woveon today!

Quick Intro: Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning may seem like buzzwords, but they are potent technologies whose capabilities businesses have yet to fully understand. We can safely say that these technologies will end up revolutionising not just technology but the whole world, and this is not an overstatement. These technologies promise to be so destructive that businesses all over the world, and especially in Australia, are starting to realise that they will have to reinvent their organisations to succeed. When you look at countries spending money on automation and AI, Australia spends the 2nd highest amount of money on this field – second only to the United States – yet it lags behind in several key areas. If you run a business in Australia, you need to understand how machine learning and artificial intelligence will fundamentally change the way you do business. We know you keep running into advice and warnings about how we all need to start preparing for an AI world, and the best place to start would be understanding the basics of these technologies.

Understanding Artificial Intelligence

quick-intro-ai-ml

Our computers are much better at many tasks than us humans can ever hope to be, which is why we use them so much. When it comes to creating a database or doing a calculation, no human being can even come close to the efficiency, accuracy, and the blazing speed of a computer system. Yet, there are many problems which normal computers cannot even hope to solving, and many situations in which they are useless, because of the way our systems are designed.

Computers are machines which we can program. We can tell software and hardware to do x in case y happens, or do z if y does not happen, and so on. Our computer systems are smart, but they are not intelligent. Intelligence here is defined as the ability to create a solution when you face a new problem you haven’t solved before.

Computers are much more efficient than people as long as you can tell them exactly what to do. You have to program the computer for different situations. As long as the situation is something the computer is programmed for, the computer will be able to accomplish it without any problems. If the problem includes something the computer has never seen before, it will not be able to do anything. This is important because it is the main reason computers are still so limited, and why we need people to do all the work.

Tasks that Require Intelligence

In order for a computer system to be artificially intelligent, it will need the ability to understand things, instead of just learning them. Consider this task: you run a company that makes boxes. You want to quality test them using a computer. You can do this easily – you simply have to tell your computer what a perfect box looks like, and how much deviation from perfection is acceptable. It will not be hard for a computer system to compare a box made by you to the model of a perfect box in its memory and determine whether the product is acceptable, or if the shape is too deformed and the product should be rejected. Computers will be much better at detecting these imperfections than people – exponentially so.

Now consider another task – you are running an event for pets, and you take a lot of pictures. Half of your customers brought their dogs to the event, while the other half brought their cats. You want to be able to categorise photos based on which animal was in them – you want all the cat photos in a separate folder than all the dog photos. This problem will not be a problem for a human being at all, who can tell at a glance whether the picture includes a dog or a cat and categorise accordingly.

A traditional computer system, on the other hand, will struggle at this task. How does it detect ‘dogs’ or ‘cats’? Do you store every possible body type of a cat and a dog so that the computer can compare the animal in the picture to the models? This would be impossible, simply because of the variety present in animals. Size, shape, colour, any disability – any of these things can prevent a computer system from accomplishing this task.

Neural Computing

The main reason that humans are better at some tasks is the way our brain works. We don’t store information in absolute terms, we store it in relative terms. We have a general idea of what a dog looks like, and what a cat looks like, and we compare what we see to this general idea. Neural computing emulates this way of thinking. Instead of knowing exactly what a dog or a cat looks like, a neural computer has a general idea of what these animals look like, which allows it to make the right decision like a human.

The difference between Machine Learning and Artificial Intelligence

The terms machine learning and artificial intelligence are sometimes used interchangeably, but they mean different things. A true artificial intelligence system will be general purpose – it will be able to solve any type of problem you ask it to, the same way that a human being can. Except, it will be able to do what takes a human, several years, in the span of seconds, simply because computing power available to it and the memory in it will be vastly more than what a human brain has.

Machine learning is a limited application of artificial intelligence. It means creating a system which can learn through feedback – imagine a car going through an obstacle course, and every time it crashes, it realises it shouldn’t do what it did the next time. Run it through the course enough times and it will be perfectly ‘trained’ using experience/learning.

Artificial intelligence is still years away, while machine learning has been a reality for years. Machine learning allows robots to accomplish tasks such as managing a warehouse, and putting products in shelves in a supermarket almost perfectly, which will result in a lot of job losses for people, and an increase in efficiency for business owners. Self-driving cars are also based on machine learning. Our businesses are already fairly automated – every company uses a database and digital communications in some capacity – but machine learning will allow us to automate much more, and thus replace many more people with a few machines.

Customer Experience, Artificial Intelligence and Machine Learning – Thoughts from Oovvuu, Canva & The Minerva Collective

Artificial Intelligence (AI) and machine learning (ML) is all the buzz right now, and rightfully so with the significant contributions it has made to redefining many aspects of business. However, many people are still skeptical about the application of AI and ML to enhancing customer experience.

Some would argue that machines cannot possibly take over customer service, something that has a heavy focus on human interaction. Machines lack the empathy and emotional intelligence core to providing a great customer experience. On the other hand, many also see the benefit of applying AI and ML to automate repetitive tasks, allowing humans to dedicate more time to, well, being human.

We reached out to some experts from Oovvuu, Canva and The Minerva Collective to pick their brains about the issue.

customer-experience-automation

What is the current state of customer experience, and how do you see it evolve with AI & ML technology?

Present customer experience is “all over the place, with wildly varying results. Two customers using the same service can have completely different impressions of their experience, and in many cases the service is clunky and poorly structured” says Anthony Tockar, Data Scientist and Co-founder of The Minerva Collective. The unfortunate reality is that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. In fact, companies only hear from 4% of its dissatisfied customers. With so much choice available to consumers, it’s much easier to find another company with similar offerings than spending time complaining or calling about a problem. Which is why there is a very real need to focus on customer experience, a factor that is becoming increasingly important to retain the modern customer.

Paul Tune, Machine Learning Engineer at Canva, believes “there are two trends in improving customer experience:

  • A trend towards tailoring for the individual, as more data is gathered about each customer at a large scale, and;
  • A trend towards providing a smooth experience for customers across multiple touchpoints by anticipating their needs. “

To demonstrate how customer experience has evolved, Paul continues with an example. “Early recommendation systems, such as the recommendation engines developed at Amazon and NetFlix in the early 2000s, provided recommendations at a much coarser level, chiefly for specific groups of customers. The granularity of recommendations in the near future is going to be much finer. For instance, an engineer from NetFlix I spoke to recently, mentioned that a subscriber’s favourite character for a TV series would appear in the menu when the TV series is selected. This means having to learn more about each customer and predicting their habits. We also see this in the form of smart personal assistants, such as Alexa and Siri” he says.

Ricky Sutton, Founder and CEO of Oovvuu, adds on that whilst AI and ML “certainly has an element to play [in customer experience], it also lacks a key element…empathy. So my thought is that it will evolve. The more AI is used, the more it learns and the better it gets, but human-level empathy remains a pipe dream for now.”

human machine touch

What is the biggest lesson you have learned from applying smart technology to customer experience?

For Anthony, the lesson has been the need for people using smart technology to properly understand it – “My experience is that people often don’t trust what they don’t understand. The latest technologies have been great for grabbing headlines, but only the most forward-thinking businesses are serious about applying them to derive value. This isn’t necessarily a bad thing – domain knowledge is essential for good data science, and blindly relying on new approaches has many inherent risks. There is a lot that has been learned about customer experience over time and there is a need to explain smart technology to business people using the right language to allow them to fully realise its value.”

To Paul, what matters most, is the customer’s end-to-end experience. Meaning that all the touchpoints with the customer should be seamless. For him, “the challenge with integrating smart technology to improve user experience is similar to managing any other complex system: with more moving parts, there is a higher chance of failure in the system. Naively applying machine learning to improve customer experience is misguided. Machine learning works best if it is complementary to the customer experience, serving to enhance the experience of a great product.”

“At Canva, our goal is simple: we want to give the customer the best experience in empowering them to create and design. To that end, there are two aspects that we focus on. Firstly, how do we make the content that they need for their designs easily accessible. Secondly, how do we anticipate what resources might be helpful for them in the future. We achieve these goals by improving our search and recommendation services to enhance customer experience.”

The biggest lesson for Ricky is that “AI turns humans into super-humans, but only for certain tasks.” – “When we started Oovvuu, we hired editors to read articles and find relevant videos, and they were able to read one publication each and find 40 relevant videos per day. That same person using the AI tools that we created, can now read 100,000 publishers, and 300,000 stories a day, covering 26 million topics and find relevant videos from more than 40 global broadcasters. AI is mind-blowingly powerful for automating manual human tasks, but humans remain better at all the things that, well, make us human.”

ai-ml-customer-experience

What are some challenges for businesses who try to integrate AI & ML technology and customer experience?

Anthony, Paul and Ricky all agreed that a huge challenge for businesses is not having a solid data infrastructure, or a deep understanding of what exactly should be measured to achieve business goals and customer satisfaction.

“Many companies approach us seeking to use AI as a ready-made silver bullet for a business problem. Others come to ask to play with AI, so they can find a business opportunity. Neither really works.” Ricky said. “For us, the solution was to know what business problem we were trying to solve: namely, to put a relevant video into every article being published worldwide. We then used AI to solve it, but what we started with was very basic and not up to the job. We have had a team nurturing the teaching for almost 1,000 days to get it where it is.”

Anthony went on to add that “there is no silver bullet – good data scientists are required to translate these algorithms into business value. Having a solid data science strategy is essential, and through good leadership, increased data literacy and an understanding of how to build a high-performance data science team, businesses can harness these technologies to forge a competitive advantage.”

Paul concludes with another common challenge many businesses face when adopting AI & ML into their processes – the volume of data. “Present machine learning techniques rely on a relatively large amount of data to provide good predictions” he says. “While there is fundamental research being carried out presently to (hopefully) reduce the amount of data required to train these machine learning models, the current main technological limitation of requiring a huge amount of data is here to stay for the foreseeable future.” But “fortunately, this effect can be mitigated if the data collected is of sufficiently high quality.”

Are you implementing AI and ML technology in your business? Share your story with us in the comments below!

Customer Connection Web Diagram

About the Contributors

Anthony Tockar The Minerva Collective, AI, Machine Learning, Customer Experience, Woveon-476307-edited

Anthony Tockar

Anthony is a leader in the data science space, and has worked on problems across insurance, loyalty, technology, telecommunications, the social sector and even neuroscience. A formally-trained actuary, Anthony completed an MS in Analytics at the prestigious Northwestern University. After hitting the headlines with his posts on data privacy at Neustar, he returned to Sydney to practice as a data scientist while co-founding the Minerva Collective and the Data Science Breakfast Meetup. He also helps organise several other meetups and programs for data scientists, in line with his mission to extend the reach and impact of data to help people.

Paul Tune, Canva, AI, Machine Learning, Customer Experience, Woveon

Paul Tune

Paul Tune is a Machine Learning Engineer at Canva, responsible for developing solutions for tailoring and personalising content for Canva’s customers. He has several publications in prestigious computer science conferences and journals, including the ACM SIGCOMM conference in 2015. His interests include deep learning, statistics and information theory.

 

Ricky Sutton, Oovvuu, AI, Machine Learning, Customer Experience, Woveon

Ricky Sutton

Ricky is founder and CEO of Oovvuu, an IBM and Amazon-backed start up that uses artificial intelligence to match videos from global broadcasters with publishers worldwide. It’s mission is to use AI to insert a relevant short form and long form video in every article. In doing so, it aims to tell the news in a new and more compelling way, end fake news, and in doing so, repatriate billions from Facebook and Google back to the journalists and broadcasters who make the content.

 

customer service charter template download cta woveon

Let Your Humans Be Human

Guest blog by Colin Priest

There’s an industrial revolution under way in businesses across the world, and it is all about automation. Businesses are embracing machine learning and artificial intelligence to make better decisions automatically. And the reason for this revolution is the comparative strengths of humans and computers.

Computers are strongest at repetitive tasks, mathematics, data manipulation and parallel processing. So long as a task can be defined as a procedure, a computer can do that task over and over again, without getting tired, giving the same results each time. Computers can manipulate numbers and data in volume much faster than any human.

digital-transformation-how-to

Several years ago I went back to university to do a masters degree, and after a 25 year break from university I was out of practice at mathematics. Imagine my excitement and relief when I discovered that now there is software that will do algebra and calculus for me! And computers can do more than one thing at a time. Have you ever tried to rub your belly and tap your head at the same time? I can’t do both actions simultaneously. But modern computer networks are powerful, able to routinely do dozens of different processes at once.

This does not mean that humans are obsolete. What humans are much more skilled than machines at are communication and engagement, context and general knowledge, creativity and empathy. When I have a frustrating problem, I want to talk to a human. Someone who will understand my exasperation, listen to my experience and make me feel valued as a customer, whilst also solving my problem for me. Humans are much better at common sense than computers, instantly recognizing when a decision doesn’t make sense. And humans can be creative. I recently heard music composed by a computer, and I’m sure that song won’t make it into the Top 40!

Customer Service communication -cmo-marketing-customer service skills-conversation management

Recently I had a conversation with the manager of a call centre that dealt with hundreds of customer service issues each day. In order to ensure the quality of the service and advice, the call centre operators were given scripts and were commanded to follow those scripts without changing a word. The problem was that both staff and customers became frustrated. Staff felt bored and unchallenged, and customers with non-standard problems felt like they weren’t being heard. Staff turnover increased, and customer satisfaction levels dropped.

The manager then tested using chatbots to answer simpler questions from customers, freeing up the human operators to deal with non-standard enquiries. This was a situation where computers had a comparative advantage over humans. The call center processes were fully defined, operating at scale, and the scripted answers were correct. The results spoke for themselves. Computers were much better at helping with the repetitive enquiries, and humans were better at dealing with the unusual enquiries. Staff engagement increased, as did customer satisfaction.

This has implications for human resources and process innovation. Processes that require humans to do repetitive, well defined tasks can be replaced by artificial intelligence. This frees up staff to do what humans are best at:

  • asking the right questions,
  • applying common sense,
  • creating new solutions,
  • evangelising new ideas, and
  • generating sales and profit.

Let your humans be human. Free them from repetitive tasks. Change job descriptions to focus on human strengths, and hire people who best embody the comparative advantages of humans. Look for human processes that are well defined and repetitive, and enhance the process by introducing artificial intelligence. Some ways company have started to incorporate artificial intelligence and machine learning into their processes include:

There are even some companies out there that have started automating the automation, like DataRobot. Instead of hiring and training up a data scientists, the arcane process of building predictive models, once the sole domain of data scientists, can all be automated. The system automatically builds predictive models based on your data, freeing up your humans to be human.

 

Based in Singapore, Colin is the Director, Customer Success and Lead Data Scientist, APAC for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Over his career, Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. He frequently speaks at various global conferences. Colin is a firm believer in data-based decision making and applying AI. He is passionate about the science of healthcare and does pro-bono work to support cancer research.

Navigating Your Way Past The “Trough Of Disillusionment” For Artificial Intelligence In Customer Experience

Guest blog by Steve Nuttall

The hype around Artificial Intelligence technologies is at its peak. According to the 2017 Gartner Hype Cycle, emerging technologies such as deep learning, machine learning and virtual assistants are at the “peak of inflated expectation”. Cognitive expert advisors have passed this peak and are now descending towards the “trough of disillusionment”. This occurs when interest wanes as experiments and implementations fail to deliver.

emerging technology hype cycle gartner 2017

The benefits of AI for customer experience management are potentially game changing. AI has the capability to analyse vast amounts of data in real time from various sources, including human behaviours and emotions. Expectations are high because this capability can then be used to create seamless and personalised customer experiences that are optimised to the device and channel of choice.

Pragmatists and battle hardened cynics will recall that when automation was first introduced into customer service channels, the results were often spectacularly underwhelming. So, is the application of AI to customer experiences destined to fall into the trough of disillusionment before climbing the slope of enlightenment? Or is there a path to follow to avoid the pitfalls of unmet expectations?

Intelligently using Artificial Intelligence for Customer Experience

In order to find out whether the application of AI to your business’ customer experience will take a downturn, it is necessary to first ask yourself: What is driving your organisation’s AI strategy? Is it because:

  • AI is all the rage in your industry and your organisation is fearful of being left behind?
  • If you take the lead in implementing AI, it will make you look smarter/cooler than your colleagues?
  • It sounds like a cool and fun toy to experiment with?
  • Your organisation needs to catch up with your competitors who have been early adopters of AI?
  • AI is a great opportunity to reduce the cost to serve our customers?

If the answer to any of the above is Yes, then the trough of disillusionment beckons.

Alternatively, if you are deploying or considering AI because…

AI can enable your people and optimise your processes to operate more intelligently and efficiently, in order to provide individualised and predictive experiences for your customers at scale

…..then a brighter future awaits.

For these technologies to have any chance of success you should have a clear sense of purpose of how to you intend to deploy AI to drive CX in your business. Here are three ways you can use AI in a purposeful way to create meaningful customer experiences.

1. Use AI to enhance your knowledge of the customer

Customer Connection Web Diagram

An example would be using data analytics to anticipate the needs of individual customers at each moment of truth and key stage of their journey. Some specific examples oh how businesses are using AI to enhance customer knowledge:

2. Use AI to create stronger emotional connections with your customers

Using AI to recognise a customer’s emotional state helps agents better respond to the customer during an interaction, thereby creating stronger emotional connections.

3. Use AI to empower your service agents

artificial-intelligence-robot

Not only can AI empower agents with emotional intelligence to reply appropriately to customers, it can be used as a tool to connect service agents with the right information in the organisation’s knowledge base in real time. Examples of why this can be powerful to a business: 

A recent study Fifth Quadrant CX undertook for Oracle showed that CX leaders acknowledge the potential of AI and are more advanced in trialling and implementing these emerging technologies to enable better customer experiences. AI is being used to combine data from multiple sources to create individual profiles for each customer, enabling agents to take immediate action on what customers want. Consequently, CX Leaders are outperforming their counterparts by creating emotional connections with their customers through more predictive and personalised customer experiences.

As a result, nearly two thirds of CX leaders say their organisation’s revenue growth outperforms their industry counterparts, compared with only a quarter of CX laggards. The proof is therefore clearly in the pudding: when applied in a purposeful and meaningful way, AI technology can enable organisations to increase agility and overcome competitive threats and leverage this advantage to drive acquisition.

 

Steve Nuttal fifth quadrant customer experience head of researchWritten by Dr Steve Nuttall – Head Of CX Research, Fifth QuadrantSteve has worked in various leadership roles as a market research insights professional for over twenty years in Europe, Asia and Australia. He leads Fifth Quadrant’s program of CX strategy research and is an international speaker and presenter on best practice customer experience. Steve assists organisations to deliver their customer-centric strategies and business performance goals including designing and implementing programs to help optimise the customer experience.

Making Returns on the Conversational Economy

Article by Adam Rawot, CEO Woveon

I remember reading an article almost ten years ago talking about how teens were sending over 40 texts a day on average. The tone of the article was incredulous, but the statistic pales in comparison to how we exist online now. Speaking personally, it’s not implausible I send off 40 messages before 10 AM in my morning inbox check in. Sarah Guo, a partner of Greylock, expressed it succinctly when she took to Medium: “More than a decade ago, academics such as Thurlow described a “communication imperative”—human beings are driven to maximize their communication volume and satisfaction. More recently, researchers have described it as compulsion.”

While constant connectedness is old news, technology has finally achieved a point it can leverage this behavior. As with all big shifts, there will be survivors and those who don’t adapt fast enough. Companies will need to change to a conversational mode of thought to maintain the experiences users expect and deliver the individuality anticipated.

People Always Talk

multichannel communication

Nearly 25 years ago, Harvard Business Review wrote “today if you’re not on the phone or talking with colleagues and customers, chances are you’ll hear, “Start talking and get to work!” In the new economy, conversations are the most important form of work.” Conversations are how we track knowledge flows. Conversation flows are how people create value, share information, and illustrate how companies operate.

A cited example is McKinsey. McKinsey prides itself heavily on the intelligence of its members, and by an extension the true value of McKinsey over other firms is its extensive knowledge base. That knowledge is curated and developed through internal conversation and shared through internal conversation. In short, McKinsey is conversation.  

We are entering a new age for product development – one dictated by the conversational economy. Broadly, the conversational economy is the catchall phrase for companies, products, and ideas built on, alongside, or relying heavily on a conversational interface. More simply, they are services that leverage conversation.

This definition is board, and intentionally so. While some apps like iMessage, Snapchat and email obviously fit into this definition, conversation works as a backbone in services like Facebook, customer service complaints, and online advertising as well. Finding a common backbone helps derive a working model for these services.

Between the myriad of mobile apps used every day, access to the internet, and the seemingly innate human need to feel connected, conversation based platforms are dominating our lives. We have effectively destroyed the asynchronous quality of day to day life. We persist online, and, consequently, our conversations with one another never really begin or end. This data stream is a jackpot for product creation.

Smarter Everyday

Artificial intelligence, in the eyes of the public, has snuck past an important threshold. Presentations by titans like Facebook and Google have assured that we are moving away from the robotic idea of natural language processing in a rigid sense to natural language understanding. In other words, instead of responding to a keyword or a phrase, computers are beginning to be able to understand sentence, paragraphs, and intent.

There are a variety of causes for this – improvement of machine learning and deep learning, Moore’s Law, and rate of mobile and app data collection, to name a few. Algorithms and software are taking on their own intelligence. Just the idea that failed outcomes can make systems better is an astounding twist compared to five years prior.

Additionally, we’re in the middle of the boom of ambient computing, the idea that our environments and surroundings are responsive. We don’t have to open our phone or flip open a laptop to be connected. On the way to work I may pass a few smart cameras, a plethora of listening iPhones and Galaxy phones, an Alexa, Chromecasts, and more. Despite this, I would characterize myself as one of the less connected people in my demographic. At every step of my day my voice can be heard, position tracked, and activity monitored. Being connected no longer has much to do with if our phone is on our person or if we’re behind a keyboard.

Although passive collection has subtly pushed past our natural aversion to share information with technologies we don’t understand and people we don’t know, this one-sided trade has come with the expectation of usability. When software doesn’t work or apps crash, we no longer blame ourselves, we blame companies. We are inundated with choices, but that means that we have little tolerance of poor experiences. Users are more empowered than ever in that they don’t have to subject themselves to experiences they don’t want or content they’d rather avoid. We so demand these freedoms that events like net neutrality rapidly cause public outcry and social faux pas by companies like EA tank sales.

Computing, connectedness, and data almost completely undermine how product managers need to think about designing products. The need to leverage conversation to deliver value has emerged as one of the most critical company problems. IDEO acquiring a data analytics company, giants like Apple acqui-hiring boutique companies with human-centric software, and Salesforce pushing Einstein all serve as mine canaries that even the most established companies are racing and struggling to adapt.

Buying In and Cashing Out

As George Box famously cited – all models are wrong, but some are useful. Where is the utility of viewing products as ongoing conversations?

A helpful place to start is in how companies have historically fended off competition. These ‘moats’ include things like brand loyalty, unique data sources, and intellectual property. However, as technologies like AI are more readily available via open source projects, cloud hosting and computing are only a few clicks away, and systems of engagement continually emerge, the traditional ideas of tech defensibility are evaporating. In a Greylock article on Medium, they wrote “In all of these markets, the battle is moving from the old moats, the sources of the data, to the new moats, what you do with the data.”

In another words, companies are now finding defensibility through the experiences they create. To create these experiences for customers in the conversational era, companies will have to harness existing behavior, respond personally, and work faster.

Harnessing existing behavior is an exercise in invisibility. The real frontier for conversational companies to generate solutions for problems before the consumer is even aware. For example, Facebook realized that people asked for recommendations on their newsfeeds. Instead of creating a new service, they had posts automatically update with reviews and locations. They created a new card that changed automatically depending on what a user wrote. As expressed by a product designer at Facebook: “We didn’t try to invent a completely new behavior; rather, we found an existing one and made it way better.”

To cite an example within my own career, food industry companies often lose hours if not days within food recall investigations. Tracking a faulty shipment through several distributors can be tricky. We worked to create a product that reads the complaint before the owner may even be aware it exists and start and investigation. By the time an owner is even aware there is a problem, a report is ready. By approaching complaints, invoices, and shipments and messages between companies, value can be created seamlessly in a second layer.

As I’ve written about before, personalization is an increasingly critical element of producing customer lifetime value. Harvard Business Review started to notice this trend in their research on customer service: “Even as artificial intelligence becomes embedded in everyday interactions; human conversation remains the primary way people make complex purchases or emotional decisions.” The fatal error in a lot of software products is focusing on company efficiency over consumer experience. While these changes may boost bottom line in the short term, they encourage competitor entry and consumer drop off.

Conversational apps have an obvious outlet for personalization, and the power behind them allow easy switching between automation and human elements. More simply: “these intelligent agents will facilitate one-on-one conversations between consumers and sales or customer service representatives rather than simply replacing human interaction.” Imagine a case where someone sits on a delayed flight and sends out an angry tweet. A conversational built system could find the message, tag it, and route it to an agent. While the agent delivers a personal response with an update, the system has already sent an alleviating reward of extra miles to the customer. The captain may be alerted of sentiment on the plane and deliver an announcement. While an autoresponder may have been cheaper, the customer will now remember the exceptional level of immediate service and is more likely to return. As information and computing become free, the real commodity becomes the personality of the person on the other end of the line.

In the shorter term, there’s a simpler way to think about AI adoption – people don’t trust what they don’t understand. In the classic product management advice, it’s best to start with a problem and move to solution. Leveraging conversation is a means to building a better product, but that doesn’t change what the bottom lines should be. In other words, “Bots do not need to be human to be human centered.”

Outside of the shift in new product priorities, another major implication is how we use the technologies we use currently. In a blog post, Dan Rover (sp?) declared that bot won’t replace apps, but inboxes were the new home screen. Our email, text messages, and more were queues demanding our intention and driving our usage.

Companies leveraging platforms like WeChat have been able to effectively create micro services and apps for things like ordering that have integrated seamlessly with how we act now. Bot companies that are able to daisy-chain onto conversations to do scheduling and commuter planning have shone in venture capital funding. It’s not inconceivable the next unicorn will have nothing to do with creating a new platform but layering effortlessly onto the ways we talk with those platforms now.

Speak Now

We talk online all the time, but computing has finally let us create value from that. Companies need to invest in ways to leverage these conversations to deliver seamless and personal content. This means focusing on personnel and focusing on alleviating frictions than automation. Companies that don’t value the communication imperative and connectedness of customers will soon find themselves lagging in experience, and, later, sales.

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A prime example of this is Amazon Web Services’ fast climb to dominance. Legacy systems like Oracle required costly deployments and developers, and setting up cloud instances on AWS is only a few clicks away. IaaS records have shown Amazon’s sheer dominance. Oracle, trying to defend by housing data and curating an elite brand, couldn’t compete with Amazon’s engagement accessibility.

Perhaps the most obvious implication of smart conversational apps is efficiency. However, despite all the news and hype around an artificial intelligence singularity, businesses – and their customers – still revolve around the interactions person to person. This means that products needs to be resolved around facilitating conversation, collecting information, and iterating form that information. The AI boom has made it easier than ever to facilitate personal conversations no matter where customers are online.

AI Technology and Customer Service

Recently I wrote an article for LinkedIn titled “Can we maintain the human touch with customer service?” I couldn’t help think about how fast we are moving with Artificial Intelligence that the question still remains, I am not worried about 5 years from now or what new customer interactions will be digital, but how will businesses maintain the reality check with their customers? Surely digital chat bots and automated ticketing systems will ask random customers surveys about what they thought about their service response and the level of happiness to refer another customer. To implement is very easy but to keep the human connection with your customers will be the challenge.

ai_technology_customer_service_woveon

At Woveon, we watch and analyse through thousands of conversations all uniquely handled by diligent customer service agents who, assisted with technology, work tirelessly around the clock to acknowledge, understand, listen to and resolve the incoming customer conversation. Clearly customer service has the human touch here! Even with today’s AI technology surpassing standards in reliability, accuracy and now business intelligence the human touch in AI must not be far away? This is an important consideration looking at the technology landscape today, companies are working on delivering smarter customer service solutions, from chat bots that understand your sentiment and can adapt to your tone and writing style to automated enquiry systems that can help recommend products while you shop online. Yet still, customer service and particularly conversation management is still a human “touch”, something that is defined intrinsically in the term “customer experience”.

Let’s take the example of creating an outstanding customer service experience. Data tells us that outstanding customer service increases brand loyalty. Examples include begin a conversation with a podcast, send personal messages, create a lifestyle and get back to your customers. We’re not talking about getting back to your customers via a bot or automated reply email, but rather using an actual person who understands your customers and can understand the fine details and semantics of human feelings. Remember, customer service is all about listening to your customers and putting yourself in their shoes. Great customer service professionals can quickly adapt and understand the customer’s frustrations and calm their emotions. Being present and responding quickly in human is very different to doing this via a scripted automated response. However, in the enterprise world, a study by Oracle put it at 8 out of every 10 businesses who are already implementing or about to implement AI as a customer service solution by 2020. Nearly 40% of all enterprises are already using some form of AI technology with Forrester predicting a 300% increase in AI investments, the disruptive power of AI will impact every part of the business from customer service to sales and support. So are businesses going ahead at this the wrong way?

Having interviewed several CTOs and CMOs working with the technology, there is no rushing into the game looking for the holy grail. For most, the best step moving forward is in assistive and adaptive technology or to assist with data collection and analysis. AI technology is encapsulating more and more human qualities as technology advances. Bots are often deployed to collect data based off human input and use it to optimise the customer’s experience. This is particularly applicable to personalisation. Human teams then need to help filter, sift through and make sense of all the personalisations so the system can make better judgements in the future. Artificial intelligence predicts and prioritises the user’s interests according to their searches and similar inputs given by other users. This, when compared to the pros of human service, has similar benefits to empathy and experience. For example the human touch can continue on more serious, complicated customer challenges whereas standard, mundane everyday enquiries can be handled by AI bots. An example is AI assistance to lessen waiting periods for customer inquiries. KLM, the flag carrier airline of the Netherlands, used DigitalGenius’ AI system to answer customer’s questions faster. The AI units interpreted the questions and answered them with a quick edit of the preformed answer to relate directly to the language used by the customer. It was also able to adapt to the platform for the inquiries, pumping out longer responses to emails but limiting Twitter responses to 140 characters. Digital customer service seems to be directed towards matching human interaction but with the removal of prominent flaws.

So can we maintain the human touch in customer service? Having been a product manager and worked in technology since the first dot com (no I am not that old, I was just young when I first got into technology), we can expect to see customer service significantly enhanced with AI bringing the human touch to a new level. The amount of data that AI and ML will help sift through to help “advise” and “suggest” to a customer service team will break new boundaries. Customer service teams can then be deployed to work on escalated or prioritised items that result in a big sale or help close the deal. Customer service, intuitively is tied closely with the human touch, a computer cannot learn years of successful customer interactions without first being taught and guided by humans. This is a realistic fact.