Woveon works with an Australian retail bank to provide an AI-assisted Digital Concierge

SYDNEY – February 14, 2019 – One of the largest retail banks in Australia has a new tool to improve customer onboarding experience across their physical and digital channels.

In a proof of concept, Woveon has deployed a solution that creates an architecture for a digital concierge for customers in personal, business and wealth banking sector. The project scope involved over 500 branches and impacted over 1.6 million customers spread across multiple states.

The technology formed the backbone for aggregating financial planning and wealth management conversations, which allowed for millions of conversations to be stitched and analyzed at scale. Woveon’s Single Customer View helped the bank keep a consistent and accurate record of customers across their internal systems, while providing a shareable holistic view for customers who were handed over to external financial planners.

The result was discovery of untapped revenue and improvement of operational efficiencies. By prioritizing ongoing conversations, investment opportunities and financial best practices, the bank was able to seize unrealized customer opportunities.

The company is continuing to explore rolling out the technology across other channels  departments including compliance, according to the bank.

About Woveon

Woveon is a conversational technology that absorbs and analyzes billions of conversations, giving an organisation unrivalled business intelligence to win in the market. By prioritizing customer inquiries with artificial intelligence and automating aspects such as complaint investigation and analysis, Woveon enables companies to strategically take control of their customer interactions – to provide the best customer experience, audit for compliance and maximize revenue.

To learn more, request a demo today or follow us on @woveon on Twitter.

5 Lead Generation Software Using AI and How They Differ

Artificial Intelligence is certainly not a new concept by any means. AI has increasingly begun to take over multiple aspects of business management, marketing and sales in the recent years, more specifically, lead generation for B2B marketing in the form of multiple lead generation software which is available online.

Improve Your Customer Conversation. Request a FREE Conversational Technology demo today!

Artificial intelligence and the lead generations software that utilize AI driven platforms and algorithms function by collecting and analyzing useful data, utilizing passive records of sales, marketing strategies and circumstances to improve customer relationship management, providing valuable insights into business decisions and helping to identify profitable investments from the unprofitable ones to increase ROI.

Today, there are endless cases of corporations utilizing AI for B2B marketing campaigns and sales purposes with a variety of lead generation software available in the market. 

Top 5 Best Lead Generation Softwares utilizing Artificial Intelligence

Please note that every software functions the same way or utilizes the same AI algorithms to provide actionable data.

1. Growbots AI

Growbots AI is an automated lead generation tool which has over 200 million contacts in a self-updating database that are connected through a variety of social media platforms. This database has individual customer profiles of each of its contacts, their businesses, social preferences and needs.

As a lead generation software, the Growbot allows corporations to find ideal customer profiles that match their niche markets within minutes from that massive database. The requirements of a corporation’s target market are input into the search engine which, with the use of specific AI algorithms, generates a list of specific clients that have the true potential to translate into sales.

growbots lead generation software

The corporation’s sale representatives can use that list and reach out to the potential customers that have a need for their niche products to promote their business and successfully sell their merchandise.

2. Growthbot by Hubspot

Taking machine learning in sales and marketing to another level, GrowthBot by Hubspot is a lead generation software that integrates Artificial intelligence to Customer Relationship Management (CRM) systems. The GrowthBot works by connecting machine learning processes to the various messaging applications widely used by businesses today such as Slack, SMS systems or Facebook Messenger. These messaging apps are integrated with the chatbot, allowing businesses to ask directed questions and receive answers to any and all queries related to drive leads to their business.

growthbot by hubspot lead generation software

For example, if you have questions regarding the business’s website traffic and its fluctuation, or want to find a niche market to target for your company within the local areas or want to reach out to prospective clients, the GrowthBot does that for you and more.

3. Conversica

Conversica is another one of its kind marketing and sales software for increased lead generation that is widely being used today. This software is, in actuality, a highly intelligent digital assistant that is completely automated, and powered by an AI algorithm.

conversica lead generation software

This algorithm is human like with superhuman powers in the sense that it engages in real-time conversations with each and every single possible lead your business might have. It does this in order to converse with them, gather necessary contact information and then analyze the interest of the lead to determine whether or not it will translate into a successful sale. The assistant is trained to alert a real-time sales representative who can close the deal positively, based on certain triggers.

4. Albert by Adgorithm

Albert by the artificial intelligence-driven marketing firm Adgorithms is the secret behind the recent skyrocketing sales of one of New York City’s dealerships of the motorcycle conglomerate Harley Davidson. Unlike other lead generation software, Albert works across the various social media platforms such as Google and Facebook to determine the outcomes of the marketing strategies currently in play. This helps it sift through what was working, from what wasn’t. Based on those results, by using quantifying algorithms, Albert then optimizes the marketing campaigns to attract more customers or leads.  

albert by adgorithm - artificial intelligence marketing lead generation software

Albert utilized the existing consumer data from the motorcycle dealership to isolate the behavior and characteristics of past successful sales and used it to target customers that resembled the previous narrative.  

5. Leadberry

Utilizing Google Analytics as its Artificial intelligence base, Leadberry is a B2B software for lead generation which collects the data about prospective clients of a company including but not limited to contact, business and social data.

leadberry lead generation software

Leadberry is said to have become the highest rated Google Analytics technology solution for lead generation in the B2B sector because it takes into account the information across various platforms of a particular visitor to a company’s website, then analyzes it for its potential to be turned into genuine sales. Furthermore, the software presents visitor metrics with customized reports of how engaged a visitor was with your business.

5 Ways AI Should Have Never Been Used

If you consider the world of science fiction as depicted in movies and on television, you’d think that Artificial Intelligence and its uses would be something quite similar to what we saw in ‘I Robot’. That’s what most of the world thinks, actually.

In reality, though, experts’ belief is that artificial intelligence is akin to human intelligence, appears quite differently. While a majority of the intelligent world likes to think that the successful creation of AI would be the biggest achievement in human history, groups of leading scientists just don’t feel the same way. In fact, according to Stephen Hawking, perhaps the most infamous physicist in the world, AI, instead of being the biggest achievement, could possibly be the worst mistake ever made.

That’s not all though, other world leaders such as Bill Gates and Elon Musk share the same sentiments.

It’s hard not to think about just how progressive the world would be with the successful use of artificial super-intelligence. More than the endless sophisticated advancements that are being made in the field of computer science, it SOUNDS very cool, doesn’t it? Something that’s straight out of a Hollywood Sci-Fi film come to life.

However, despite how fascinated we might be with AI and the progression the technological world is showing in successfully achieving successful standards of AI, the truth remains that there are unending ways in which the use of AI could go wrong. The potential dangers of misuse, mismanagement, accidents due to human error, as well as the safety concerns are just too real to deem inconsequential.

In fact, not just for the future, there are current examples of the past and present which clearly show us why AI should never be or have been used.

Of the limitless quote-worthy cases, here’s considering the top five ways AI should have never been used.

1. The Microsoft Chatbot

In 2016 spring, the world witnessed a Microsoft chatbot with the name of Tay – an AI persona – go completely off center to hurl abusive monikers and statements to the people interacting with her on the social platform Twitter. While the chatbot was only responding to the messages sent her way by interpreting them through phrase processing, adaptive algorithms and machine learning, it was still an example of an AI robot experiment going awry with the bot developing its own mind and thought process.  

2. Humanity Destroying Sophia

One of the biggest concerns that most IT world leaders have is the possibility of AI devices taking over the world as we know it or causing irreparable harm. One robot, Sophia the lifelike android, brought to life by the engineers at Hanson Robotics gave us a real cause of concern when along with declaring her future ambitions such as going to school, studying, making art, starting a new business and eventually having a home and family of her own declared that she would destroy humans.

While the declaration came as a response to a question jokingly asked of Sophia by her interviewer at the SXSW tech conference in March of last year, the response was no less alarming.

3. The Existential Debating Google Home Devices

Things got very interesting – read weird – this past January when in a curious experiment two Google Home devices were placed next to each other in front of a live webcam. The home devices, which a programmed to learn from speech recognition began to converse with one another, learning from each other during the course of the interaction.

google home devices

The experiment, which is said to have run the course of a few days, took a twisted turn when the bots began to get into what can only be described is a heated debate about whether or not they were both humans or merely robots. A classic example of AI machines truly having a mind of their own.

4. The Russian Bot On The Run

Just last year the world witnessed just how quickly AI robots can develop a mind and liking of their own. Case in point, the Russian robot prototype Promobot IR77 which escaped out the doors of its laboratory and wandered out in the streets – all by learning and programming itself based on its interaction with human beings. Naturally, chaos ensued when a snowman made of plastic ventured out in the midst of heavy traffic at a busy intersection. According to reports, the robot, despite being reprogrammed twice following the incident continues to move toward the exits when tested.

5. Image Recognition Fails

AI modalities primarily gather its information from speech and visual recognition. The AI devices and systems learn and program themselves by going through and processing hundreds of voices, words, languages and equal amounts of images as they go along.  When introduced by Google, back in 2015, the image recognition system labeled two people as ‘gorillas’. While the incident resulted in a public outcry and had Google issue an apology before it smoothed over, it gave us a clear example of how unrealistic it is to assume that systems of AI can make sense of and learn the tricky ways of human-environment accurately.

The fact is, that AI does have the potential to become considerably more intelligent than any human alive. When and if that happens, the possibility of AI overtaking and controlling human lives will become reality – a reality that will not only have no ways of being controlled but one that will be without any sort of accurate or semi-accurate predictors that gauge its behavior in any way.

Woveon named Best AI Startup at GITEX DTCM Future of Tourism Challenge

DUBAI – October 17, 2018 – Woveon, a conversational technology for customer intelligence wins the award for Best AI startup in the DTCM Future of Tourism Challenge at GITEX Technology Week in Dubai.

The Futurism of Tourism Challenge was launched by the Department of Tourism and Commerce Marketing (DTCM) with its partners Emaar Hospitality Group and Atlantis Hotel, to support the growth in the tourism industry. The aim was to find new technologies that provide more inclusive experiences for visitors through itinerary planning, and can support the increase in tourist attraction openings.

Woveon presented an AI-assisted itinerary planner to support conversations on Dubai Tourism’s website and app. Using a visitor’s historical conversational, transactional and behavioral data from multiple systems and channels, Woveon is able to provide a highly personalized itinerary based on their interactions with the organization.

Dubai Tourism is currently exploring a PoC with Woveon to roll out their technology to provide better, more personalized itinerary suggestions and conversations with their visitors.

woveon gitex best ai startup futurism tourism

About Woveon

Woveon is a conversational technology that absorbs and analyzes billions of conversations, giving an organization unrivalled business intelligence to win in the market. By prioritizing customer inquiries with artificial intelligence and automating aspects such as complaint investigation and analysis, Woveon enables companies to strategically take control of their customer interactions – to provide the best customer experience, audit for compliance and maximize revenue. To learn more, go to www.woveon.com or follow @woveon on Twitter.

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.

Related:
Woveon: Using AI to Create a Better Conversation with Your Customer

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.

Request a demo on Conversational Technology and manage customer conversations with ease.

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

Customer Service

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.

Customer Satisfaction

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?

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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.