Complicated-sounding terms like “machine learning,” and “sentiment tracking” are thrown around a lot in today’s marketing and customer experience circles, with new AI-powered tools being announced every week.
Though most business owners know that these types of software are important, they may be turned off by the tech-heavy language. However, you don’t need to be a professional programmer to understand the basics of text analytics and how it can drastically improve your CX efforts. We break it down for you below.
Text Analytics Definition
First things first, let’s define what text analytics actually is. Text analytics is the process of extracting quantitative data from written (qualitative) information. The goal of text analytics is to analyse qualitative feedback quickly and at scale to uncover trends and patterns. There are a large variety of ways in which this process can be useful for businesses, and we’ll outline some examples below.
What Do Businesses Use Text Analysis For?
Customer Feedback: Perhaps unsurprisingly, the main reason businesses use text analytics is to analyse customer feedback. Sending out surveys asking customers to rank responses on a numerical scale can only get you so far. The more valuable analysis usually comes from freeform responses that can be gathered through detailed customer feedback surveys, social media mentions, online reviews, and more. By using text analytics tools, businesses can cut down on the employee bandwidth needed to manually sort and categorise responses from these important feedback sources.
Risk Management: Enterprise-level companies, such as financial lenders, are starting to use textual analysis to identify risky investments or lending practices. These types of tools can quickly pull information about a specific company, including news articles and reports, and categorise this information based on a set of pre-determined rules to see if industry experts view them as a good investment.
Targeted Marketing: Text analysis can also be used to better refine audience segments for more accurate marketing. These tools can gather demographic and psychographic information about a user’s interests and buying habits online in order to build out more detailed personas that can be served with the right types of ads.
How Does Textual Analysis Actually Work?
Now that you have a general idea of what businesses use text analytics for, we’ll dive into what the AI is actually doing. This is just a basic overview to help you better understand the process, as machine learning is, without a doubt, a very complicated subject. After all, natural language processing mimics the workings of the human brain!
Step 1: Tagging and Chunking
The first step machines take to understanding language is to go through each piece of textual feedback and tag each word with a part of speech, such as noun, verb, adjective, etc. Next, the sentence is chunked into phrases based on where these parts of speech occur. These are usually categorised as noun phrases, verb phrases, and prepositional phrases. If you’re a little rusty on elementary school grammar, prepositions describe spatial relationships, for example “on,” “after,” “into,” etc.
Step 2: Parsing
Once the sentence is tagged and chunked, the bot will separate it into different elements or sections based on the defined phrases. This step is important because a single piece of customer feedback can have multiple meanings or sentiments. For example, the phrase “Love your product, but it’s a bit expensive” consists of multiple elements including “product,” “price,” “positive emotion,” and “slightly negative emotion.”
Step 3: Rule Setting and/or Topic Modelling
Now that you understand how the AI reads and categorises pieces of qualitative information, we can get into the analysis part of the process. There are two main ways to go about this: rule-setting or topic modelling. With rule-setting, the AI is going off of a pre-determined set of rules laid out beforehand. For example, the word “expensive” could have a rule attached to it that directs the AI to add this review to both the “price” and “negative emotion” categories. The pro of using rule-setting is that you can be reasonably sure of accurate results from the beginning, but the con is that the set-up time takes much longer.
Topic modelling is what’s known as “unsupervised” machine learning (whereas rule-setting is supervised) because the AI essentially learns how to categorise and analyse on its own based on recurring themes and sentiments. Just like humans, the bot will learn as it goes and gets more accurate over time. The pros and cons of this method are just the opposite of rule-setting: There is virtually no set-up involved in the beginning so you can start using the tool right away, but it can take some time before results are accurate enough to be truly actionable for your business.
Take a look at the infographic from Chattermill below for a visual illustration of these points, along with 5 more real-world examples of how companies can use text analytics to improve CX.
From sustainability and demand for fast, free delivery, to data analytics and artificial intelligence, Customer Experience is shaped by many internal and external forces.
We are in an age of vast online choice, rapid price comparisons and supply chain optimisation technology. It means customer experience is now a vital differentiator, whether in the consumer or B2B sectors. It’s how organisations keep customers coming back for more.
A PwC global survey of 15,000 people found almost one-in-three customers (32 percent) will walk away from a brand they are in love with after just a single bad experience. In the US, 65 percent of consumers find a positive experience more influential than any advertising campaign.
What then, are we likely to see emerge as trends in Customer Experience over the next 12 months?
1. On-board with virtual customer assistants and chatbots
Virtual customer assistants (VCAs), will be a prime area of investment throughout 2020. The artificial intelligence behind these technologies becomes more sophisticated and capable by the hour. By VCA we mean a bot that is capable of more than scripted conversations and can handle tasks such as booking, ordering or password resets. As we move on, VCAs will clearly expand their range of duties.
There could well be a rush to implement VCAs as companies seek to lower the barriers to conducting business. But the interactions handled by virtual assistants will require a great deal of care.A key decision will be whether VCAs are for self-service onboarding or for the support of current customers.
In the B2B sector, chatbots will continue their expansion, always available for customers who want more information on how to use products or require urgent attention. The obvious advantages of chatbots are that they work 24-hours a day, reduce costs and cut the impact of calls and queries on the rest of an enterprise.
2. Handovers from chatbot to human will improve and be more transparent
As we see, AI has surpassed its initial through the hype stage and we are now at the point where VCAs and chatbots supplement the more nuanced work of humans, especially for routine transactions. The human touch – being able to talk to a real member of staff – remains critical to ensuring the quality of customer experience.
Where this can fall down is in the crucial point of handover between application and human. It can fail completely or involve pointless repetition and delay. We will see customer experience technology honed to ensure handovers happen at the most relevant time and in a way that is totally transparent. The customer will know when and why they are engaging with a bot or a human.
3. AI will start to unseat the rating scale in surveys
AI is also set to transform survey data, capable of analysing huge strings of text very quickly and accurately, according to the parameters it has been set. This will reduce reliance on standard scale-based benchmarking as the focus in customer experience feedback shifts to getting the customer talking.
The precision of AI will give brands the ability to explore what matters most to their customers. From a line-bar with value-based metrics and a scoring system, customer experience professionals will move to the analysis of what customers are actually saying. They will be able to extract insights that are genuinely actionable and which will deliver positive results.
The number of channels and third-party platforms used by brands and their customers expands relentlessly, but with AI, all these touchpoints become listening posts. AI will allow companies to analyse the sentiments as well as the data, understanding customers at scale, without any fear of overload.
4. Key turning-points instead of whole journeys
In the next year, brands will focus on the most decisive moments in the customer journey, rather than trying to address each step in minute detail. That has only led to the dissipation of resources. Brands will use insights from customer feedback to hit the moments of biggest impact – when exactly customers are ready to make decisions about purchases or renewals or when they run into difficulties with a process or product or even its packaging.
With these insights, businesses can intervene with an offer, chatbot assistance or advice from a member of staff at exactly the right moment. Interventions will be at the contact point between processes to ensure customers do not drop off. Hard data insights will replace best guesses.
5. The value of data and insights will rocket
As the year progresses, AI will increasingly help customer experience departments demonstrate the value of the data and insights they collect from their customers. At the end of a call, live chat, online booking, or service centre interaction, there are opportunities to ask consumers for their feedback AI applications will extract the key themes from this content and quantify the ROI each theme will generate, either in terms of revenue or Net Promoter Scores or another satisfaction metric.
As the value of insights becomes more obvious, companies will use AI to extract insights from social media, analysing what customers and influencers are saying about their products and brands.This will be part of a big push for the implementation of digital and customer experience management platforms by enterprises.
6. Opening the gates to changing content
2020 will see the role of content evolve. From the conventional transactional exchange of content in return for details and data, we will see the removal of barriers on many company websites. In practice, this will mean less gating of content by brands.
More brands will have sufficient confidence in their content, knowing that over time its quality will attract attention from the right potential customers. But content too will become shorter and more “instant”, chopped up into short, easily digestible but slick videos or podcasts. Organisations will seek to inject more authenticity into their content, giving it greater character and personality.
US tech firm IPsoft has unveiled what it claims is the most “advanced digital employee on the market” in the form of an updated avatar that replicates human conversational behavior.
The firm’s Amelia digital employee system is already one of the market leaders, with B2B customers told “thinks like a human but works at the speed of a machine”. Now the AI system has been updated to feature a new-look avatar (pictured), which IPsoft say “provides the most human-like digital experiences in the industry”.
This new version of Amelia brings together her sophisticated cognitive capabilities – built to mirror the functions of the human brain – with the latest advancements in avatar technology. Amelia is designed to deliver the visual elements of human interaction – conversation, expression, emotion, and understanding – to everyday user experiences, driving deeper customer connections and greater business value.
Utilizing advanced Natural Language Processing (NLP), Amelia is able to understand natural language, follow context switching, and independently execute complex tasks to resolve user requests. Her state-of-the-art affective computing and sentiment analysis enable her to recognise and adapt her responses based on the mood of the user and the context of the situation.
Dube said: “By 2025, I believe that you could pass a colleague in the hall at work and not know if it’s a man or machine. Amelia’s new lifelike avatar takes us one step nearer to closing that gap between physical and digital colleagues to create a truly hybrid workforce.”
Customer engagement software specialists Freshworks has shed light on how customers interact with AI tech such as chatbots in an insightful new report.
In AI in Customer Service: A Survey Report from Europe, responses from 6,000 customers and 800 senior business leaders across the continent were analysed to provide answers on what consumers think of the growing technology.
The research reveals that 41 percent of European consumers “see no benefit of chatting with a bot”, while 29 percent said answers from bots “did not help solve their problem”.
For brands, Freshworks has found that 25 percent are currently using artificial intelligence solutions to improve customer service.
The research was commissioned to highlight the disconnect between what brands believe they are providing, and what exactly customers themselves say about the services.
Among the experts providing insight in the report is CX advisor and author Adrian Swinscoe. He said: “There’s been a significant gap in brand and customer perception of the type of service being delivered and received for some time.
“The addition of new technology and new channels, in many ways, is exacerbating the situation, as customer expectations increase, and businesses have to manage more ways of communicating than ever before.”
Love it or hate it, when December hits there’s one thing as sure as mince-pie overload – it’s also the time of year we all get our crystal balls out and start predicting what’s on the horizon for the coming year.
The last ten years have seen a rate of change in customer service faster than the last 100 years combined. Salesforce have dubbed it the ‘fourth generation’, where boundaries between the physical, digital, and biological worlds have started to blur. It’s fundamentally changed what consumers have come to expect from brands. No wonder that 80 percent of customers consider their experience with a company to be as important as its products.
The upshot here is that businesses are still clearly missing a trick in how to differentiate their brand and delight their customers base – but will next year be any better? Well there’s huge scope for improvements, but ironically the ones that may fair best are the ones that focus on the basics over buzzwords.
And we all know the buzzword of the moment – artificial intelligence (AI).
It’s had by far the lion’s share of the word of mouth this year but there is still confusion as to the role it can play, especially in complex environments, as well as the consumer acceptance of it.
There’s also lessons to be sought given the changes taken place in the cyber security market. Even the least savvy of Internet user has seen the furore over election manipulation, foreign state hacking and increasingly credible phishing and social engineering attacks. This has influenced strategy because providers operating online channels have had to enhance the protection of data with pin numbers, confirmation codes, captcha boxes and two factor authorisation. The problem is that these things make for a terrible customer experience.
I can also see huge scope for improvement when it comes to receiving customer feedback, which is instrumental for CX programmes to succeed.
According to Microsofts’s 2018 State of Global Customer Service Report, nearly all customers (90 percent) have a more favourable view of brands that give them the opportunity to provide feedback. However, less than a quarter (24 percent) of customers are given the opportunity to provide feedback regularly.
In 2020, I think that the traditional ‘long format’ survey will become largely obsolete. I was speaking to a major retailer last week who said they had sent out 3,000 surveys and had just two responses.
Social media listening is useful, but they are a very self-selecting audience in terms of response. Forward-thinking brands will need to embrace the concept of “react with a gif”, “react with an emoji” and seek out more seamless ways of embedding feedback options into their day to day customer interactions.
Lastly, I believe the concept of gratitude from providers towards their purchasers and audiences will be really important. The “got to have this” type out outbound and social marketing we see has led to a purchase frenzy especially amongst beauty and fashion and lifestyle brands, but I often wonder if many consumers do not feel that their loyalty is rewarded.
Forrester’s report explains this well: “Consumers will evolve from recipients of a brand experience to participants in it.”
As we look ahead, deeper relationships between brands and consumers, with genuine rewards for staying loyal, feel like they will become important. It’s the modern equivalent of the corner shop throwing an extra item in with your shop because they know you – personal service, to delight and reward each customer.
2019 has proven to be a successful year indeed for the conversational AI experts at ContactEngine, which is leading the vanguard in changing the fundamentals of how 21st-century customers interact with brands.
It’s been a year in which their trophy shelf found itself a little squeezed and in need of an extension, with added honours including a Sunday Times Hiscox Tech Track 100 title, and a UK Customer Experience Award, collected at Wembley Stadium in October and presented for the firm’s successful partnership with BT Enterprise.
The Gold category win for Best Use of Technology is testament to ContactEngine’s position at the cutting edge of what is the most exciting – and often most misunderstood – tech affecting the modern Customer Experience: artificial intelligence.
Their sophisticated algorithms offer intelligent omnichannel customer conversations, and the firm’s founder, Dr Mark K. Smith, is a man whose passion for excellence is evident as he explains what his company stands for, and where the advanced computing involved behind the scenes can lead for both businesses and customers.
Speaking with CXM, Dr Smith described his firm’s work with BT as an example of what ContactEngine does for an organisation with a duty to communicate with countless customers through various channels.
“We start conversations and invite a response from our client’s customer,” Dr Smith explains.
“With BT, it was specifically to improve a process by better communication; by trying to reduce the amount of cancellations that would have occurred had there been no communication. It was to reduce the amount of calls someone would make to a call centre due to a lack of communication. Our goal is to increase the engagement rate and then ultimately to see if we can make customers happy as consequence of that.
“To put it simply, we start conversations – all automated – and invite responses. From there, we carry on the conversations using our own NLU (Natural Language Understanding) and a machine learning algorithm we call ALAN (Advanced Language ANalysis), and we deliver efficiency gains for our clients, making their customers happier.”
From this description, ContactEngine couldn’t be more suited to Digital Customer Experience if it tried, but surprisingly, the PhD that provides Dr Smith with his title stems from a science of an altogether less-computerised kind.
“Up until my late-20s I was a career academic, and my PhD is in Biochemistry,” he says, before explaining how he adapted his skills in that particular field to AI tech development.
“The biological sciences are all about generating mass data sets and trying to seek out trends in that data. Of all the sciences, biology and biochemistry present quite a lot of mystery, so you generate a lot of data and look for trends. That’s very similar to what AI is actually, so you can post-rationalise it. I love technology and always have done.”
Describing the business’ origin in the telephony-based live-streaming of events, Dr Smith said using the tech that would eventually lead to ContactEngine’s current offering through streaming the 2011 World Transplant Games in Australia led to the realisation that proactive communications could solve many of the problems faced in business.
“Firms such as Virgin Media used the tech for corporate social responsibility work – in their case broadcasting from the top of Mount Kilimanjaro through their social media channels.
“They asked us a simple question: can you improve the way we communicate to our customers in this omni channel-way that you have provided to us in a social media context?” continues Dr Smith.
“The answer for people like me is always ‘yes’ to any questions to do with IT. The real question is ‘how long and how much’ but the answer is always yes! You see, unlike my biochemistry days, computers can always be made to work.
“We stepped into the world of customer communication, and almost accidentally built an omnichannel outbound comms tool, so we can start conversations by phone, email, text, instant messaging, or collecting video.”
This powerful tool was soon utilised by clients such as American telco giant Verizon, which learned the value of implementing the technology and what it means for a company’s bottom line.
“When an appointment was missed, it cost Verizon well over 100 dollars. If you can improve communication with the customer to stop that from happening, then you can save that 100 dollars,” he explains.
“If a company has 100 million customers, then that’s a lot of money to be saved, and we can charge them a fraction of the money they save.”
As AI becomes ever-more central to even the most basic of customer communication, Dr Smith tells us that despite fears among some about where the tech will eventually lead, it will remain a benign benefit to society.
“I’m no great believer in singularity,” he tells us, referring to the theorised future in which AI outgrows the need for human masters and snowballs into an uncontrollable overlord.
“It’s not that I think it will happen in the future either – it just won’t happen!
“I start from the position of a rationalist – I’m not a believer in ‘Skynet’ or other fantastical problems that AI could bring. It’s important to realise that AI is often the only solution in areas where a human simply cannot compete.
“If you have a company with 100 million customers, as many do, it is impossible to have enough people to communicate well with all those human beings. You cannot do it!
“Computers are the only way you can do that, and what’s most interesting about the world of AI for us is a subset known as machine learning.
“This takes vast data sets – bear in mind we are dealing with hundreds of thousands of people a day – so we have vast amounts of data and responses to the questions we ask. If you have vast amounts of data, then you have some really tremendous possibilities for teaching your algorithm to be human-like.
“Machine learning is simply taking an algorithm and giving it sufficient data for the next piece of information it receives in order for it to have a pretty good stab at it in a manner which exceeds the way a human can respond.
“Think of the ‘100 million customer challenge’ and you’ll see why you want to have a proactive outbound conversation – only made possible through computers, not people.
“We automate a way to simpler conversations. A machine is better than a human for 95 percent of customer conversations. But there will always be the five percent where a machine just won’t cut it.
“Take an example; I was with an insurance company recently, and they said that with their life insurance product, they would only usually get one phone call, and that was from a bereaved partner.
“Now, it’s not wise to put that call to a machine, as a machine will never display empathy. They may display ‘faux empathy’, but a customer will catch that out pretty rapidly. A human needs to be involved in that conversation, and these calls were often taking up to two hours.
“However, once that conversation is completed, it’s perfectly reasonable for the machine to take over in order to inform the person of progress on their claim, or any other information.”
Other fascinating aspects of the tech behind ContactEngine includes a profanity filter, which detects when a customer needs to be transferred to a human as a matter of urgency, in order for that person to be talked to and returned to a level of calm where their issue can be resolved.
“Interestingly, there’s not an enormous amount of research about when humans are best and when machines are best, but I believe that by working together they can vastly improve the Customer Experience, and the Employee Experience of call centre staff also,” Dr Smith continues.
“We have a case with a European bank which commissioned us because they were losing their call centre people because they were doing too many cold calls after a certain customer process had failed.
“The customers were saying ‘why are you calling me a week after this happened? I’m really not interested in talking to you’.
“Machines fill that knowledge gap and can filter customers who actually do want a conversation with a human, then we broker an appointment for them.
“So what happens in this case? The person in the call centre has a better Employee Experience, potentially staying in their job for longer, while the Customer Experience was vastly improved also.
“It’s about knowing when humans are best, when machines are better, and knowing the exact best moment to flip between them.”
So with a successful foundation in telcos, where next for ContactEngine’s revolutionary CX tech? Clients already include household names including BT, Virgin Media, and Whirlpool, and the future looks seriously promising.
“We have enjoyed success in other areas including retail and banking; we have a foundational communication product that can be used in any industry, so we need to spread our wings and grow in other sectors,” Dr Smith says, adding that work is already underway with a “large UK retailer”.
“On a technical front – what fascinates me about what we’ve done is, if you talk to companies in the UK and beyond, roughly speaking, three-quarters of them will be handling their AI over to the usual suspects.
“They will be using Dialogflow from Google or Watson from IBM. We made a conscious decision many years ago to build our own machine learning algorithm, and we did that because we wanted to be white box, not black box, and we wanted to be explainable.
“We have the benefit of: when you start a conversation, there are a limited number of intents that come back to you, so it’s quite easy for us to visualise and explain the decisions we made.
“We wanted to use labelled data sets for one client and not share that label data set with another client. We felt that was a GDPR problem. So, we built our own machine learning, and rather interestingly, when you take the training data we use to feed our algorithms, and you present that to others that I mentioned, we actually out-perform them!”
On the horizon for ContactEngine and its clients is the next generation of ALAN, with multi-intent capabilities, and developing further the concept of ‘human-computer rapport’, where the next customer conversation is informed by the earlier exchange in a more human-like way.
“We are incredibly excited for the future, and to see what 2020 has in store for us after the amazing year we have just had.”
The UK is trailing behind Europe in customer service as brands race to adopt AI technologies to transform how they engage with customers, according to new research.
Customer engagement software firm Freshworks found that just over half (54 percent) of UK senior decision makers state their business currently uses AI – in areas such as chatbots, virtual assistants, Natural Language Processing (NLP), and facial recognition – for customer service departments, compared to 97 percent in the Netherlands, 86 percent in France, and 81 percent in Germany.
However, this investment does not yet seem to be far-reaching for UK customer service. The Freshworks study, which surveyed over 800 senior decision makers in customer service departments, found that only 20 percent of UK businesses have invested more than £250,000 in AI for customer services in the last 12 months, compared to nearly half (46 percent) of German companies, 41 percent of French firms, and 35 percent of Dutch organisations.
Across all territories, chatbots (37 percent), NLP (34 percent) and Robotic Process Automation (31 percent) were the most popular AI technologies for businesses to be adopting to improve their customer service.
The report suggests people do not want to take on responsibility for bringing AI in to overhaul current systems. Over a quarter (26 percent) of senior decision makers in the UK claim no one is driving AI deployment within their customer service department. Yet, C-Suite executives are leading the integration of AI in the vast majority of Dutch, French, and German companies (97 percent, 95 percent, and 91 percent respectively).
Addressing the brand perception gap
The findings also suggest a large gap between business and consumer perceptions of how good their customer service actually is. Eighty percent of senior decision makers surveyed in the UK believe their customer service departments to be excellent, while only nine percent of UK consumers have no frustrations when dealing with customer service agents.
According to the research, a quarter (25 percent) of businesses are using AI to improve their customers’ experience of the brand, for example using AI-powered chatbots to resolve issues quickly by filtering through simple questions and channelling the trickier customer scenarios through to human service agents. Yet, one-in-four (25 percent) of the 1,871 British consumers surveyed who have previously used customer service channels said that being left on hold for too long is their biggest frustration.
UK General Manager at Freshworks, Simon Johnson, said: “Our research shows that British brands’ deep distrust in AI risks leaving them lagging behind Europe in their approach to customer service. It’s incredibly difficult for brands to keep up with consumers’ expectations, but it’s non-negotiable that they constantly evolve their technology to include AI and Machine Learning and approach to keep their customers engaged and happy.
“For those who get it right, it can be a game changer that distances them from the competition.”
Companies know all about the importance of visual content.
According to a 2018 study from Venngage, 56 percent of marketers surveyed said that between 91-100 percent of their content contained visuals. Delivering the right compelling visuals quickly is critical to achieving the desired visual hook. However, many images and videos can be optimised to provide better user experiences and higher engagement levels.
Too often it’s the technical details that derail your visual storytelling efforts. Nothing is more frustrating than having invested a lot of time and resources creating beautiful visuals for a campaign only to discover that audiences aren’t seeing them how you intended. When high-quality images are cropped in the wrong places or displayed incorrectly in social sharing, for example, response rates and brand image suffer.
The browser and its long tail
Another recent report revealed that 75 percent of consumers expect a consistent experience wherever they engage with brands – website, social media, mobile, or in-person. This is easier said than done. One big reason for consistency failures is the browser long tail, which refers to the different versions of browsers people use.
Cloudinary recently published its State of Visual Media Report to help people understand how visual content is being consumed. Analysing billions of media transactions across a sampling of more than 700 of our customers, we were fascinated to discover just how many different types of browsers are in use worldwide.
While Chrome and Safari, as expected, dominate the browser market (45.9 percent and 4.1 percent respectively in the UK), there are significant regional differences across lesser known variants. For example, the research shows that Nokia Symbian smartphones are still popular in some regions and that Nintendo devices DS devices share more than 15,000 images per day. There is even image traffic coming from the very old legacy office software, Lotus Notes.
This is important as not all browsers support every image or video format you might use for your campaign. JPEG, GIF, and PNG are the most popular image formats used on websites today. However, developed in the 80s and 90s, when they’re not properly optimised they may not always be the best choice as they are quite heavy in file size and don’t offer the image quality and color spectrum expected for delivering today’s immersive online experience. Newer image file formats such as WebP and HEIF offer advantages worth exploring.
The same applies to video formats. The old H.264 video standard is pretty common but newer more lightweight formats such as VP9 and H.265 are anywhere from 30 to 50 percent more efficient.
Now for the long tail of browsers out there, JPEG and GIF for images and H.264 for video are the lowest common denominator that work with almost every browser. Does this mean you have to compromise your visual storytelling efforts just because some of your users still stick to their legacy BlackBerry web browser?
The browser’s long tail doesn’t need to compromise visual storytelling
Fortunately, the answer is no.
Your web developers don’t need to abandon the unlimited visual possibilities that come with newer image formats. Newer AI-based image and video management solutions can automatically detect your web visitors’ visual requirements and their browsers. Based on this information they automatically deliver each image and video in the most efficient format, quality, and resolution – even to a BlackBerry web browser. But these tools can do even more.
Intelligent image detection and cropping
As mentioned earlier, the last thing your brand needs is for beautiful images that you’ve invested dearly in to get badly-cropped and poorly displayed. AI-based image and video management solutions can solve this problem. These tools apply AI smarts to optimally resize and crop images. For example, AI applies algorithms to automatically detect the subject in an image that is most likely to capture a viewer’s attention.
It also analyses the type of browser and device the images are displayed on. Based on all this combined information, brands are able to deliver images and videos that will drive greater customer engagement.
Visual are great for boosting engagement and fostering long-lasting connections.
With a little help from AI you can be assured that the browser long tail doesn’t degrade the user experience so that your visual storytelling efforts really pay off.
Most UKemployees anticipate a positive impact from artificial intelligence (AI) in the workplace, a new report from Genesys has revealed.
The global leader in omnichannel Customer Experienceand contact centre solutions studied the evolving relationship between employees and technology in the workplace. They found that 64 percent say they value AI, but the exact same percentage believe there should be a legal requirement for companies to maintain a minimum percentage of human workers and for relevant bodies to implement regulation around it.
The survey also found that while employees welcome new technological tools, a significant majority (86 percent) expect their employers to provide training for working with AI-based tech, as less than half of all respondents say they possess the right skills.
When asked whether they would use augmented reality (AR) or virtual reality (VR) for job training, more than half (53 percent) of employees said they would be willing to do so. This finding is significantly higher than those who would be open to being trained by an AI-powered robot, with just over a third (35 percent) of employees accepting this method.
The convergence between humans and technology is increasing, as reflected by the fact 41 percent of millennials say they spend at least half of their time at work interacting with machines and computers rather than humans. These findings suggest that when it comes to implementing new technologies, employers will need to find the right balance between tech and human workers.
When it comes to how employees expect to use new technologies, 58 percent would like to use a digital or virtual assistant to support them in managing tasks and meeting deadlines. This appetite for virtual assistants suggests that the widespread use of technologies like Amazon’s Alexa or Apple’s Siri in workers’ personal lives is opening people’s minds to the possibilities that similar AI-driven assistants can bring to the workplace.
Meanwhile, almost a quarter of workers believe AI will have a positive impact on their job in the next five years, and
69 percent say technology makes them more efficient at their jobs. Forty-three percent say new technological tools in the workplace save time and allow them to focus on other things.
Mark Armstrong, interim Vice President for UK and Ireland at Genesys, said: “Employees across the UK are ready to embrace new technologies in the workplace. The research shows that UK workers understand the benefits of AI and are overwhelmingly positive about its potential impact. It is also evident that employees understand that businesses will need to leverage AI and other emerging technologies to maintain longevity, as only 21 percent believe their companies will remain competitive without it.”
Customer Experience is shaping the future of ecommerce.
AI is no longer science fiction – it’s real present-day technology and many ecommerce businesses are already using some form of AI to understand their customers better and provide an enhanced CX.
2019 is shaping up to be the year that AI becomes truly prominent in ecommerce. Investment in AI and machine learning is increasing across the board, just as the developments in technology are expanding the range of uses. According to IDC, the global retail industry is set to spend $5.9 billion (£4.45 billion) on AI systems this year.
AI is changing how people find products and shop online; there are systems that can analyse millions of daily interactions to create targeted incentives to individual customers.
In a highly competitive and dynamic marketplace such as ecommerce, the drive to integrate these emerging technologies is accelerating. AI has the power to be a game changer in ecommerce by pushing CX to the next level. Virtual assistants, product recommendations, voice search, and augmented reality are just some examples of these technologies.
AI is a trend, but it is not a fad, so brands that wait to implement it into their business will rapidly find themselves eclipsed by their forward-thinking competitors.
What can AI do for your ecommerce store?
It’s predicted that ecommerce businesses that personalise successfully could see profits rise by 15 percent by 2020 (source: Gartner).
There are various ways of capitalising on AI and machine learning platforms for your business but the most significant advantage of AI is the level of hyper-personalisation that becomes available to your customers.
The demands of online shoppers are evolving at a spectacular rate, faster than human retailers can respond to them.Consumers now expect personalisation as standard as they are used to experiencing it on a daily basis. For instance, the recommendations Amazon and Netflix make based on the user’s prior interactions. Eighty percent of watched content on Netflix comes from algorithmic recommendations, according to findings by Mobile Syrup.
Meanwhile, McKinsey found that 35 percent of Amazon’s revenue is generated by its recommendation engine. Your business may already be collecting data on the online behaviour of your visitors but vast amounts of data can be overwhelming and pretty useless if you don’t know how to analyse it for the purposes of putting effective strategies in place.
An effective AI system has the capacity to filter through petabytes of consumer data to predict online behaviour, and offer individually specific recommendations that are buyers find relevant. This level of intelligence is vital in delivering a personalised shopping experience to the consumer.
Quite simply, stores that have not deployed ecommerce personalisation will lose out on revenue. Brands that engage this tactic can transform their online stores in a way that serves the customer’s needs and best interests. And do it efficiently and even cost effectively!
Humans cannot compete with AI when it comes to deconstructing big data. AI facilitates multiple ways to segment your audience to gain intelligent insights that allow retailers to personalise in a range of different ways.
Product recommendations: Algorithmic recommendations that update in real time, depending on the visitor’s behaviour. Buyers expect the ‘you may also like this’ feature to show items that are relevant to their tastes. Personalised merchandising sorts the product display to show customers products that genuinely appeal to them.
Personalised website content: Presents visitors with various configurations of online content according to their personal preferences. This can even include personalised navigation of the site, with a personalised home page, which is proven to increase conversions.
EA (Evolutionary Algorithms, a subset of AI): This can carry out sophisticated content testing and optimisation at a rate that humans simply cannot, by assessing which layouts and content drive the highest conversion with different customer segments and then configure the online experiences to the individual in real time.
Customer-centric search: Using tools such as natural language programming, searching online is becoming more intuitive to what the customer is actually looking for during online searches.
Currency auto-detection: Detects and presents the correct currency for your visitors and converts the prices accordingly so there are no surprises or extra steps for the customer.
You can even use AI to tackle fake reviews by finding and removing bot generated reviews by competitors. Negative reviews and lack of customer trust impacts sales. Ninety percent of shoppers surveyed said that positive reviews influence their online buying decisions.
The benefits of creating such a personal and convenient Customer Experience are vast. By increasing the level of visitor engagement through recommendations and customised content you reduce bounce rates, improve conversions, and increase sales. And better still, generate repeat business.
These tools eliminate the need for time-consuming content testing, and reduce the amount of money spent on ads. They can create valuable efficiencies across operations that free up precious time to focus business at the strategic level. An AI engine that continuously monitors all devices and channels has the ability to create a unified universal customer view; for the first time its possible to deliver a seamless cross-device and cross-platform experience.
AI systems can be integrated with digital marketing solutions and can be utilised to build unique customer experiences that are consistent across all marketing channels.
For instance, since implementing AI, clothing brand Footasylum saw a 28 percent increase in email campaign revenue from hyper-personalised marketing communications.
Marketing copy that speaks directly to the customers based on their purchase history, search queries, and page visits, is an extremely effective tool for cart recovery and post-purchase promotions.Abandoned cart emails achieve a 4.64 percent conversion rate, according to Proteus Themes.
Personalised ads on social media as a tool may not lead to people buying directly from the SM platform but they do drive relevant traffic that increases conversion rate so should not be left out of the AI strategy.
Privacy and ecommerce personalisation
Of course, the price of hyper-personalisation to the customers is their private data, but even cautious shoppers will part with their intimate details for a high level of customisation and ultra convenience.
Building value-based, trusting relationships with your customers is essential for long-term loyalty, so remaining fully transparent on how and why you collect people’s data and the security levels in place are vital.
Invespcro found that 57 percent of online shoppers are comfortable with providing personal information to a brand, as long as it directly benefits their shopping experience.
Chatbots and virtual assistants
Chatbots are a machine learning technology that interacts with shoppers in a chat environment simulating human conversations. Chatbots and virtual assistants are being deployed across online retail to mimic the personal touch of a shop assistant.
They learn and evolve to become better at assisting the visitor with customer service needs and product queries. They can even be trained to say ‘thank you’ and ‘sorry’, making them more forgivable if they do frustrate a customer.
Chatbots present an effective and low-cost way of providing customer service, 24/7, which reduces the need for expensive humans. Virtual assistants are impacting the way customers make purchases and provide retailers with a creative opportunity to deploy across the customer journey.
Where to start?
Investing in AI may still seem like an enormous undertaking, but there are several ‘off the shelf’ platforms available, many of which offer 30-day free trials. By testing out different applications on your site, you can see which works for your business. The real question is, can you afford not to invest in ecommerce personalisation for your business?
The smart approach is to do so with the intent of ensuring your customers feel personally valued and focus on providing them with an engaging and seamless shopping experience that will build long term brand loyalty.
There are rare occasions when technology breaks out of the bonds of the geeks in the basement and impinges on the national consciousness.
The debate around AI is just one of those occasions.
Politicians, business leaders, trade unionists, and media commentators have all weighed in on the subject: AI is going to create jobs; AI is going to destroy jobs, AI is going to make us more efficient, AI is going to make us money…and so on.
Above all, it’s particularly prevalent in business.According to research from Accenture, the use of AI can improve enterprise efficiency by up to 40 percent. No organisation is going to turn away from gains like that!
Naturally, the CRM market hasn’t been immune from these pressures.Adherents of ‘AI-is-the-future’ point out many ways that AI will transform the way businesses interact with their customers, for example by pulling together information and insight on customers from a multitude of sources, websites, and different social media, without any need for human intervention.
But AI offers more than pulling together and making sense of disparate information; it can use a range of different techniques to ascertain a customer’s views and desires to help customise approaches to them.
It sounds like a utopia for marketers: better customer engagement; more sales; more profits with less human intervention.
What’s stopping them?
Sadly, like many utopian dreams, there are some practical matters to deal with. The biggest of them all is the way in which information on customers is scattered around many disparate sources.Enterprises have been used to storing data in corporate silos and pulling it all together is not the most trivial of tasks – companies have rarely been designed to work that way.
And it’s not just a question of simply collating all the information, but also understanding how it’s sorted and what common formats there are. Some of the data could come from financial records, some from email output, some from SQL-based databases, while some could be image or video unstructured data – there’s a wide variety of possibilities and somehow they all these have to be pulled together.
It’s not purely about technology. Companies need to think about how they gather information and how they work together – it may need a completely new mindset.Small businesses understand this instinctively – there’s much more co-operation (and fewer specialist roles), larger organisations are not geared up for this way of working, and each department will often zealously guard its domain.
In an ideal world, companies should set up a cross-functional team to manage the implementation of technology such as CRM (Customer Relationship Management). This CRM team should be working with different departments to work out ways in which they could share skills and data to ensure that everyone is working with a common purpose.
There’s another consideration too: people skilled in AI are really thin on the ground. The use of AI requires some specialist expertise in gathering and interpreting the data. What many organisations mean when they say that they’re using AI is that they’re making use of algorithms – just one part of the AI armoury, but not everything.
In fact, this is one of the issues when it comes to talking about AI. The concept is often confused with the other elements – for example, deep learning, machine learning, and neural networks.Technologists are aware of all the distinctions, but very often business commentators aren’t.There needs to be full comprehension of what all the terms mean when we’re talking about AI engagement.
Of course, AI will have a considerable future when it comes to customer engagement, no-one denies that. But there’s a lot of work to be done first. AI shouldn’t be treated as some sort of magic bullet that will immediately transform company fortunes; we have to be careful that we don’t succumb to the hype too easily.
What companies should be doing is making sure that their CRM systems are configured correctly and are pulling in all the relevant information; businesses may think that they need a magical touch of AI but the answers could be sitting there in their own, existing software.
There’s a long way to go before CRM investment in AI bears fruit.For that to happen, there’s a need to have an enterprise-wideCRM platform (as opposed to a functional/departmental CRM) installed and all data in one place. And, on top of that, for a corporate culture that understands that the days for data silos have passed.
Until that happens, AI is going to be something that generates the column inches in the paper but not a concept that will have an effect on the way that we handle customers. Its day will come – but just not yet.
Far from replacing people, artificial intelligence (AI) has the power to enhance employee engagement and productivity and customer interactions in one go.
New technology inevitably changes lives. However, rather than fear robots replacing front line customer service representatives, it’s time to think differently and embrace automation to elevate the status of the contact centre agents and make their jobs more engaging.
The aim of AI isn’t to replace people with robots. As former Oracle director Paul Reader said: “Automation is not the future, human augmentation is.”
From contact centres to factories, AI tools such as bots can reduce costs and increase team efficiency in a matter of months. Automation can be a game changer for customer communication and overall job satisfaction.
Change the mindset, starting with your people, and AI will soon become a friend, not a foe. View this technology as the strategic enabler of employee productivity and satisfaction and see service levels, customer loyalty, and profits soar.
Here are a seven super AI initiatives to turn your mild-mannered agents into Women and Men of Steel.
1. Eliminate the mundane
AI liberates agents by taking away the repetitive or mundane tasks, leaving them free to enjoy the challenge of tackling complex or emotionally sensitive calls that only humans can handle.
It’s a smart move – by elevating the role of agents, you give them the career they deserve and in motivating them to train and hone their skills, they soon become the superheroes that every contact centre leader wants on their side.
2. Build caller context
This can take many forms, for example a bot sitting on the front of an IVR menu asking preliminary questions while the customer is waiting or analysing previous customer conversations to build caller profiles. This gives live agents the valuable intelligence they need to answer customer queries with greater speed and efficiency when a call is transferred to them from their virtual colleagues.
The latest AI tools can even identify sentiment and notify the agent of a customer’s emotional state of mind. Depending on the outcome of an interaction bots can direct the call to the best-skilled available agent at the appropriate moment.
3. Provide a warm handover
Using Natural Language Processing (NLP), AI can understand the initial query and so provide a warm handover to a live agent who already knows what the person is calling about and doesn’t have to ask any unnecessary questions, one of the biggest irritations for customers.
4. Good memory, good rapport
Today’s AI tools are so sophisticated that they can measure customer satisfaction levels based on tone of voice and vocabulary. They speedily recognise repeat callers from voice and then use this intelligence to flag up pertinent information to customer service agents and alert managers to recurring issues that require multiple repeat calls.
Memorising the Customer Experience based on historical evidence drives proactive call resolution and builds customer trust.
5. Deliver your best ever service
All forms of AI technology such as bots perform like the model employee – they never get tired, are never sick, and because they don’t suffer from emotions, never have a bad day and they don’t need holidays!
Always predictable, they offer customers a great, consistent service any time of day or night and there’s no limit to the number of users one bot can talk to at once. No matter how many people are already talking to it, yours can answer them right away in natural language – leading to lifelong, positive customer relationships. Meanwhile, agents benefit from additional time to deal with more difficult and complex cases that only humans can handle or can even ask bots for advice on how to respond.
6. Humans and bots in harmony
When AI works hand-in-hand with the live agent team, contact centres benefit from all the perks of a human workforce plus the consistency of artificial intelligence to boost first call resolution for enhanced Customer Experience.
7. Agent assistance
Help new agents hit the ground running and become superheroes in a matter of days. The beauty of AI is that it acts as an agent’s personal assistant. Let new joiners ask questions and allow experienced agents to share their customer success stories with an agent assistant to increase the company knowledge pool.
Agents can even ask the bot questions while in conversation with a customer to deliver fast, efficient responses.
Global companies are expecting to apply artificial intelligence (AI) within their organisations in the next few years, but are lagging behind when it comes to discussing the ethics of the technology, it has been revealed.
New research from CX and contact centre solutions firm Genesys has revealed that more than half of all employers questioned in a multi-country opinion survey say their companies do not currently have a written policy on the ethical use of AI or bots, although 21 percent expressed a definite concern that their companies could use AI in an unethical manner.
Genesys, which is sponsoring the upcoming 2019 UK Customer Experience Awards, questioned 1,103 employers and 4,207 employees regarding the current and future effects of AI on their workplaces. The 5,310 participants were drawn from six countries: the UK, Germany, the US, Japan, Australia, and New Zealand.
Almost two-thirds (64 percent) of the employers surveyed expect their companies to be using AI or advanced automation by 2022 to support efficiency in operations, staffing, budgeting, or performance, although only 25 percent are using it now.
However, in spite of the growing trend, 54 percent of employers questioned say they are not troubled that AI could be used unethically by their companies as a whole or by individual employees (52 percent). Employees appear more relaxed than their bosses, with only 17 percent expressing concern about their companies.
Twenty-eight percent of employers said they are apprehensive their companies could face future liability for an unforeseen use of AI, yet only 23 percent say there is currently a written corporate policy on the ethical use of AI/bots.
Meanwhile an additional 40 percent of employers without a written AI ethics policy believe their companies should have one – a stance supported by 54 percent of employees.
Meanwhile, just over half of employers (52 percent) believe companies should be required to maintain a minimum percentage of human employees versus AI-powered robots and machinery. Employees are more likely (57 percent) than employers (52 percent) to support a requirement by unions or other regulatory bodies.
The Genesys survey found that millennials (ages 18-38) are the age group most comfortable with technology, yet they also have the strongest opinions that guard rails are needed. Across the countries, the survey questions about AI ethics resonated more with millennials than with Gen X (ages 39-54), or Baby Boomers (ages 55-73).
Whether it’s anxiety over AI, desire for a corporate AI ethics policy, worry about liability related to AI misuse, or willingness to require a human employee-to-AI ratio – it’s the youngest group of employers who consistently voice the most apprehension. For example, 21 percent of millennial employers are concerned their companies could use AI unethically, compared to 12 percent of Gen X and only six percent of Baby Boomers.
Steve Leeson, VP UK & Ireland, Genesys, said: “As a company delivering numerous Customer Experience solutions enabled by AI, we understand this technology has great potential that also comes with tremendous responsibility. This research gives us important insight into how businesses and their employees are really thinking about the implications of AI – and where we as a technology community can help them steer an ethical path forward in its use.”
He continued: “Our research reveals both employers and employees welcome the increasingly important role AI-enabled technologies will play in the workplace and hold a surprisingly consistent view toward the ethical implications of this intelligent technology. We advise companies to develop and document their policies on AI sooner rather than later – making employees a part of the process to quell any apprehension and promote an environment of trust and transparency.”
Artificial intelligence (AI) has become engrained in our day-to-day lives without us even noticing.
From basic voice assistants that can play music by just saying one word, to self-driving cars – there’s no turning back from the world of AI. Today’s tech-savvy consumers have grown to love AI so much due to its ability to improve overall Customer Experience and resolve issues in a timely way. As a result, businesses are jumping on board the AI journey at an unprecedented pace. There is little doubt in AI’s ability to dramatically transform CX, so why isn’t the same attention being given to the Employee Experience?
Today’s workforce has changed dramatically compared to that of previous generations. More employees are working remotely than in traditional offices, and recent research shows that by the year 2020 more than 50 percent of employees will enjoy the benefits of working someplace other than a traditional office.
In addition to where we work, how we work is also changing. While millennials have had access to cell phones and the internet for virtually their entire lives, even generations that have not grown up with this technology are embracing well-designed, easy-to-use applications. Employees across industries expect technology to make jobs easier and more productive, however, the bar for what companies believe is user-friendly technology is often far too low.
Even companies that are forward-thinking and want to move beyond antiquated systems, are struggling to implement technology that is as easy to use as Alexa, but also seamlessly fits into the current processes and workflow – and it’s having an impact on retention and employee satisfaction. Research suggests that a majority of employees that are looking for new jobs are doing so because of broken company processes, including being able to connect with support departments like IT and HR.
A direct correlation
One wrong Customer Experience can create a lasting impression. Therefore, businesses are now so focused on providing exceptional CX that Employee Experience becomes an afterthought. Businesses know that if they want to compete with the Amazons of the world, they need to go above and beyond to ensure a superior CX.
They have done this by pulling out all the stops and implementing new technologies that allow consumers to do things like virtually design homes with furniture they’re considering buying or try on clothing in a virtual dressing room.These innovations have changed the game when it comes to Customer Experience.But behind the curtain, employees are under constant pressure to provide this experience and are not equipped with the same flashy technologies to help them do their jobs.
In fact, the technologies designed to support the modern workforce often times do the opposite – they hinder employees’ productivity, efficiency, and, as some would claim, even the ability to produce meaningful work.
In a business-driven world where time is money, no-one should struggle to figure out technologies that are supposed to ‘support’ them and make their lives easier. The reality is that many existing support solutions today are outdated and actually work against the employee, inhibiting the ability to help them and the business thrive.
The workplace of the future
In what ways can businesses improve Employee Experience whilst also giving their employees the freedom to do the best work? We already know that workplaces of the future are likely to be increasingly more remote, as more companies choose to run their businesses from co-working spaces or have no office space at all. With the workplace becoming more fluid and dynamic, and employees working out of home offices or coffee shops, in varying locations, businesses need to be prepared to support employees across state lines and time zones.
We also know that future of the workplace will be increasingly more digital, as the technical innovations that alter the way we live outside the office will become expected in the professional environment as well.
Businesses need to reimagine the workplace the way they’ve reimagined the customer journey.Emerging technologies like AI-powered chatbots, for example, are helping with everything from onboarding and training, to providing assistance during meetings, to helping solve common employee questions that often plague IT, HR, facilities and other support teams at organisations.AI is helping businesses save time and energy – while still ensuring employees have help every step of the way.
Inundations of Help Tickets
A great example of AI in the workplace is in IT, which isn’t surprising with IT being the backbone of technology exploration and vetting at organisations.These teams spend a good majority of their days working through cluttered support queues full of repetitive tickets – whether its password resets, email access or printer setups. These are questions that can often be found in knowledge management systems or intranets, but when employees have questions – especially if those issues are hindering them from getting work done – they would much rather ask their IT buddy than go searching through a sea of URLs and documents to find the answer.
This endless onslaught of requests cuts down on the amount of time the IT team can devote to higher-value problem solving or long-term strategic initiatives. Not to mention, it must be incredibly frustrating when ten people in one day ask you how to access a remote server – copy and paste at its finest. IT teams, which are already stretched thin,are drowning in these requests day in and day out, and it becomes a problem for the entire business operation.
And IT isn’t the only one affected by this cyclical support queue. While the help desk team is busy working its way through tickets or dealing with an unexpected ‘fire drills’, employees who are waiting for support grow frustrated with resolution time.
Sometimes they even turn to unauthorised solutions that bring their own security implications. Employing an AI-powered support partner to help answer these questions removes the pain of searching through outdated and hard-to-read knowledge articles, empowers employees to self-serve and opens up the IT team to work with the employees who need them the most. Thanks to Google, today’s workforce is programmed to take a DIY-approach to problem solving and often prefers self-service, so organisations need to embrace and capitalise on this – and AI is one of the ways to help bring it to the workplace.
Time is money
The famous saying, “time is money”, must be remembered. However, if businesses don’t focus on Employee Experience, they will be diminishing their success in the long-run, creating lasting inefficiencies for the bottom line. Now is the time to start removing friction from the day-to-day by using tools that will enable employees to do their best work. Ultimately, these efforts will allow businesses to thrive as employees will feel motivated to become more productive and simultaneously more satisfied.
At its best, science fiction taps into our contemporary anxieties to predict the fate of humanity.
An episode of Doctor Who, for example, featured robotised mega-corporations, human irrelevance, and despair. The Doctor may be sci-fi fantasy, but the issues are real.
Artificial intelligence (AI) technology is reshaping many sectors – for both good and ill. Gartner predicted that artificial intelligence would generate $1.2 trillion in business value in 2018 – an increase of 70 percent from 2017. But on the negative side, it creates much anxiety about the elimination of jobs, and prolonged focus on the cost and job-cutting aspects of AI has overshadowed how the technology can help human employees.
The CX example: how tools can hinder trade
In the customer service sector the rise in AI, decision-support, automation, and chatbots has exploded across the industry, driving multi-channel customer experience (CX). But adoption of these technologies for employee engagement has been slow. Contact centres have some of the highest employee turnover rates in the world, and there’s been troubling analysis suggesting new technology is inhibiting employee performance, engagement, and satisfaction.
Gartner analysis reveals service representatives use the mindboggling average of 8.2 different systems and tools during a customer interaction. Small wonder, then, that talk-time is up nearly 14 percent while call volume has remained the same.
We have amazing systems driving less-than-amazing experiences for the people charged with using them. A primary source of the problem stems from something obvious. We’re measuring the wrong things.
Just exactly what should we measure?
In our rush to capitalise on AI technologies, we’re failing to evaluate the way they ultimately integrate into human workflows. In the customer service sector, technology is better at handling many discrete tasks but does not replace human representatives.
It’s becoming standard practice, for example, for companies to host automated, largely self-service interaction options for customers that are always available. Digital account portals supply constant access and handy personalisation capabilities, while well-designed chatbots and virtual assistants are excellent at taking orders, payment processing, status checks, or informational queries.
But for more complex requests that require human nuance and context, technology-enhanced services can complicate the situation. When dealing with the customer, human agents are at a loss without access to what transpired during those digital interactions. And even when human agents can access those systems, they shouldn’t be flipping back and forth between applications and databases while attempting to deliver proper support to a customer.
This problem is perfect for AI solutions. Analytics engines that deliver historical and/or relevant customer information to support agents automatically and in real time can speed rather than delay productive conversation. Natural Language Processing (NLP) systems recognise spoken keywords and supply agents with useful prompts or notes, sparing them from app fatigue and task-switching. Virtualised on-demand training systems can keep them stimulated and engaged.
This employee-centric AI deserves more study and development. Systems that aren’t generating a positive Employee Experience will negatively affect the Customer Experience they deliver. Exploring ways AI can better serve employees is the solution. And measuring how employees view these tools should be the first metric for success, not an afterthought.
Collaborate to work out what best to measure
Applying AI to better serve the employee is crucial, but should be measured and managed with caution, given the enormous amount of data available.
One of the greatest struggles from an AI development perspective is determining how often a system should prompt the employee and whether there should be a trigger. Can such a mechanism be ranked? Do we allow the employee to turn off certain notifications because they’re annoying?
There’s a risk of overdoing AI assistance for Employee Experience. It could get very frustrating, very quickly. The only way to arrive at balanced employee-centric AI application is through collaboration. The people using the technology should have representation at the development table, which is also an excellent way to increase job satisfaction.
The future will require us to adapt what we measure
As AI technology becomes integrated into the enterprise, we must adapt how we gauge human performance. In CX management, technological innovation dictates that businesses restructure how they view customer contacts and the human staff who perform those jobs.
Contact centre positions will no longer be entry-level or outsourceable roles. With automation handling all the basic contact tasks, human customer service becomes a more specialised profession. Savvy and emotionally intelligent customer service employees with thorough understanding of a business and its technology will be a necessity. They’ll be managing only the most important, complex, or delicate customer concerns.
Today’s metrics, such as talk-time or calls-per-hour, provide little quantification under such circumstances – but the quality of this work will largely determine a company’s reputation among human beings.
Explaining to a child how to cross the street in front of their school without being hit by a car only takes a few repetitions and their knowledge can then be generalised to most roads and vehicles.
It would instead require huge quantities of images for an AI to learn the same and it would make mistakes as soon as confronted with situations which are slightly different from what it had seen in his training data set.
The current breed of artificial intelligence – in its most advanced version – is built upon a metaphor of the human brain as a computer made of interwoven neurons. Through a ‘training’ process, the system can ‘learn’ to ‘recognise’ identical patterns without being programmed by a human and then apply this ‘knowledge’ to real world situations, more and more with a better accuracy than humans themselves.
The limit of this metaphor is that it takes a huge quantity of data to obtain this type of result and those hard-learned skills are confined to the very domain where the AI was trained.
The abstraction and generalisation capabilities of humans are still a mystery to AI researchers, but an element that may guide them in their quest is the emotional nature of human beings. We memorise much better when feeling strong emotions than in ‘boring’ situations. Children’s ability to quickly learn how to properly cross the street is certainly related to their feeling of danger and somehow fear of what could happen if they made the wrong decision.
A machine obviously doesn’t feel – we’ll leave to the sci-fi fans the debate of whether consciousness could emerge as a property of complex systems such as neural networks. AI is high on IQ and low on EQ some might say. But progress in mimicking the functioning of the human brain could require an acknowledgement and a modelling of the emotional nature of homo sapiens.
Current AI algorithms are not yet able to learn from less data and improve their abstraction and generalisation capabilities using emotions. But they are improving at recognising them within humans, exploring correlations between symbolic representations of emotions and human expressions, whatever their format.
Progress being made
Some research has already be done on the range of human emotions, thanks to the EU-Emotion Stimulus Set, and people like Houwei Cao, assistant professor in the Department of Computer Science at New York Institute of Technology, who is busy working on algorithms that can read emotions.
Initial efforts were called ‘sentiment analysis’, trying to guess an individual’s state of mind based on what they write or say. This has now taken a larger perspective by adding language patterns, voice tone, facial movements, sentence structures, and eye motions into the mix.
For instance, a mouth shaped in a particular way, plus voice with a specific pitch compared to its baseline, plus use of words tagged as being positive, equals happiness. Of course, to the average philosopher, that is a rather partial and limitative definition of happiness. But it only needs to be operational in the specific context where it is used.
Emotional AI applied to customer engagement
Indeed, those efforts are improving AI’s relevance to the business world and the fields of application are numerous.
Whether it’s customer engagement or support, a hiring process, or addressing disputes, emotional AI can play an important and useful role for humans. Employees can base their interactions on its insights, adapt their response to emotional changes in the customer and have a more effective communication with the person on the other side of the line or table.
For instance, the stakes are high for the call centre industry: born out of financial necessity so businesses can afford to serve and support large customer bases, it often turns out to be a source of frustration for users despite well-scripted conversation scenarios followed by the responding agent. When there’s pressure, good manners and empathy can be forgotten. Emotional AI can act as a reminder to employees, so it doesn’t happen.
It is also true of the sales forces whose likelihood to convert a prospect into a customer is directly linked to their ability to empathise with the individual(s) they want to strike a deal with. Indeed, approaching another human with an offering that is rational (adapted to its needs and budget for instance) but presented without taking into account their current state of mind is at best a waste of time and at worst a loss opportunity.
Emotional AI can help a business stand-out from its competitors for the quality of its customer engagement.But what will be the acceptance of emotion-driven algorithms by humans?
There will be challenges
In the age of GDPR and stringent privacy rules, considerations about voice, face, and writing being processed by emotional AI algorithms is something that businesses will need to explain to customers, since there is a very thin line between individual mood monitoring and intrusive Orwellian surveillance.
Will a customer value consideration for his or her feelings or mood by a computer as much as genuine empathy expressed by another human-being? If after asking how I am doing – something most people won’t need an AI to remind them to ask – the next question about my latest holiday is in fact an AI-scripted line, the whole introduction might sound a bit phony.
Eventually, could overly relying on AI to read other individuals state of mind turn us all into sociopaths unable to properly relate to other humans, like GPS has slowly but surely decreased our ability to use a map to navigate in the real world?
However, those questions might be irrelevant in the not-so-distant future. With the growing sophistication of virtual personal assistants – think Alexa, Siri or Google Home – we may soon delegate our buying decisions to those machines. This would imply that vendors’ own AI systems now have to pitch our AI agents instead of ourselves. And the billions spent annually by marketing departments on branding and ads designed to appeal to our emotions would fall flat.
A majority of UK adults are worried for the future of their jobs due to the growth of artificial intelligence (AI), a new report has revealed.
According to the findings of think tank Fountech.ai, 67 percent of 2,000 adults polled are worried AI will result in machines taking people’s jobs. Meanwhile, the survey also shows that 58 percent find the use of AI tools such as those used by Amazon and Netflix to recommend products to us “creepy”, and 59 percent are nervous about the way their personal data is collected and used since the rise of AI tech.
However, according to the poll, 62 percent believe AI will do more good than harm to the world, while 37 percent admit they do not fully understand what AI means.
Furthermore, only 30 percent claim to regularly use technologies powered by AI. This is despite the fact that popular tools such as Google’s search engine, Siri, most major email providers, and Facebook – as well as the aforementioned Amazon and Netflix platforms – all use AI.
One-in-three (31 percent) respondents said they do not think AI will ever be able to truly replicate the cognitive ability of humans. Nevertheless, three quarters (74 percent) want to see the UK government do more to govern the way AI technologies are developed and used.
Nikolas Kairinos, CEO and founder of Fountech, said: “People tend to fear what they don’t understand, and today’s research is an example of this. For decades, AI has been misrepresented in sci-fi movies and literary fiction, but we should not let this blinker our view of how this amazing technology can enhance the world around us.
“AI can solve problems and achieve tasks that we previously considered impossible – it will undoubtedly open doors to countless opportunities so we can make the world a better place. Importantly, as this study shows, the technology must be harnessed and used in the right way – the ethical questions surrounding the development of AI will rightly remain until both governments and businesses show they are applying it in responsible, safe ways.”
It used to be that when someone said “AI”, images of sci-fi films came to mind.
Nowadays, the effect is more prosaic: it’s become de rigeur to use AI as shorthand for “business wonder cure”. When we hear “AI”, we know we’re supposed to see it as the answer to any and all business problems, whatever the industry.
At the same time, we’ve seen Customer Experience become the key differentiator in all sorts of sectors, but particularly retail. We all want to be treated as individuals and expect those we buy from to act as value-add service providers, not just vendors. That fact has fuelled the rise of AI as the perceived silver bullet for retailers – the more data you have at your disposal and the faster you can put it through effective analysis, the more likely it is that you’ll provide a beguiling, personalised Customer Experience and product offering.
However, AI is a term that’s used too freely. It has become confused with machine learning, losing its original meaning of a completely independent decision-making engine. True AI would make decisions based on so much information that you couldn’t know the decision it’s going to make before it makes it – much like a human being.
In contrast to that, current machine learning applications are very controlled – they put a set data flow through a known algorithm which auto-updates based on the results it receives, but they’re nowhere near the level of independence required for true AI.
There are good business reasons why AI remains in the realm of hype rather than frontline use, though. Financial services firms aren’t using AI, for example, because they can’t control its results – there’s too high a risk that mortgage applications could be affected by unconscious bias, for example.
The same risks apply to retailers. A good example of this played out a few years back when Amazon attempted to take the human element out of hiring. It took data about the type of people it had previously hired, processed it, and then set the resulting algorithm to hiring people. However, it ended up only hiring men – because that was what Amazon had historically done. It discounted women based on past experience, and therein lies the problem. AI based on past data doesn’t let you change for the better.
This kind of coded bias is also indicative of the people who’ve made the algorithm – if it’s a non-diverse group designing the algorithm, they’re more likely to almost accidentally code bias in. This is one of the main reasons why pure AI isn’t often used – the data you have might be biased, and you won’t know until it’s running. If it goes wrong, you can’t unpick it.
Think of it like a child growing up: once they’ve picked up negative behavioural traits, it’s very difficult to undo the hardwiring.
Despite all this, genuine AI is a possibility. The key is ensuring that applications have access to sufficient data – and the right kind of data. The more data you have, the more likely it is that you can build something that will provide accurate, non-biased results.
Unfortunately, we’re simultaneously seeing a sharp decrease in willingness to share data, as scandals like the Cambridge Analytica debacle damage confidence in the safety and privacy of large-scale data sharing. As a result, businesses have to navigate a very fine line between feeding enough data into their automated systems to achieve an optimal, non-biased Customer Experience and preserving customers’ privacy and trust.
Take the worked example of calling HMRC. As most of us will know, it can often take forever – it’s not a positive experience. To solve that problem, HMRC recently announced a desire to use voice biometrics for identification to speed up the process and remove the endless questioning. The system would be able to tell your mood, quickly identify why you’re calling, and put you through to exactly the right person to solve your problem.
The idea is a good one, but it depends entirely on people being comfortable sharing their voice data. Without that data, how do you optimise the system? As we move away from data-sharing as the default, how can companies still optimise Customer Experience with AI-driven technology?
Prioritising end users
In a nutshell, the answer is preparation. Lots of organisations come to my practice and say they want their customer journey to take advantage of AI and machine learning, but often they don’t have the tools in place to support it – no security, no data automation, and no clear understanding of the business need they’re trying to address.
They’re starting from the wrong point. Technology is not the end goal – Customer Experience is.
Companies need to start their AI CX journey with basic identity and access management. They need to inform their customers that they value their data and their security, and that they’re going to use their data with set controls in order to then keep them informed and give them proactive customer service, say.
Transparency is key – and you have to have the technology in place to back it up.
The bottom line is that an AI-enabled customer journey should always start with security. It’s essential to be able to protect your customer’s data as you use it. Not only that, but you need to make sure you’re providing a service that actually meets real needs, so find out what your customers want.
Start with their needs, not your technological goals. AI can take customer relationships to the next level, but it must be designed with the end user in mind.
There is certainly plenty to think about with the rising cost of salaries, managing schedules to meet customer demand, looking after staff wellbeing, PCI DDSS compliance, and now the added requirements of GDPR (General Data Protection Regulation).
Initial concerns about how the new GDPR regulations would affect contact centres, in terms of increasing costs and complexity of managing enquires, have to some extent dissipated. For those contact centres taking payments and already PCI DSS compliant, it was a relatively straightforward process to embrace GDPR regulations. They had typically invested in secure technologies, encryption, and working with third party compliant companies in terms of PCI DSS. On the whole they were able to extend their technology and processes to protect personal data and meet GDPR requirements.
However, other organisations are still evaluating how new ways of streamlining processes can help meet GDPR data governance and management regulation, but are uncertain how to choose the best solution. We have identified three ways that contact centres can apply technology to help them remain compliant:
1. Mobile automated identification & verification (ID &V)
Often a significant amount of time can be spent on identifying and verifying the caller. Having a person perform this task is expensive and means that customer data is at risk. A customer engagement platform is an alternative way to offer a cost-effective, secure solution to automate the screening and identification process.
It can take the customer through set identification questions using Artificial Intelligence (AI) to simulate agent conversations, or it can use SMS text messages to authenticate the device being used. On initial registration and once the two-factor authentication process has been successful, the platform will accept and authorise payment requests that are automatically debited from the card holder’s account.
The advantage of this approach is that all information is encrypted and the agent is not exposed to any personal data, thereby complying with GDPR and PCI DSS. The data is processed and stored securely elsewhere. In addition, having signed up to the service, the customer has agreed to a data handling agreement that sets out how their information can be shared with a third party, ensuring confidentiality.
2. Customer self-service screening using IVR
Accepting credit and debit cards via IVR has long proved to be an effective and secure way of taking payments. It allows customers to pay quickly, via their own unique identifiers – a PIN, date of birth, even voice recognition. Again, reducing or removing agent contact time is a more secure way for contact centres and their customers to comply with PCI DSS. Since everything is fully automated and confidential, the client information is stored centrally and securely within the system hosting the data, taking it out of scope for both PCI DSS and GDPR.
Capturing customer data via IVR also enables calls to be routed to the right agent with the correct skills, in the event of a request to speak to an advisor. The agent then has all of the relevant information available to manage the call successfully, but with key identification data screened, thereby ensuring GDPR compliance.
3. Cloud-based third party payment solutions
The third option to consider, and one that has gained significant traction over recent years, is to choose a cloud-based payment service provider. A trusted third party that complies with PCI DSS demonstrates proven adherence to a recognised security standard, which can also help contact centres to meet the GDPR legislation. Companies can apply a process of ‘de-scoping’ to reduce the number of requirements (tick-boxes) for GDPR, in the same way that they might do for PCI DSS compliance.
Of course, like PCI DSS compliance, the responsibility for GDPR cannot be entirely removed from the contact centre, however the effort required can be dramatically reduced by working in partnership with a payment solution provider.
Aligning GDPR and PCI DSS: the route to successful compliance
There is no doubt that GDPR has improved standards around privacy and data protection, but at what cost? Contact centres that have worked hard to blend people and technology to enhance data and payment processes in the last year, have typically done everything they can to comply with both GDPR and PCI DSS.
For the rest, the good news is that it’s not too late to review what’s in place and make the switch, to new technology and/or a third party solution provider, to enable a secure, multi-channel seamless route for customer payments. The choice is there for the taking.
Analysts predict that spending on Artificial Intelligence in the retail sector will reach $7.3 billion by 2022, a majority of which will be poured into customer-facing conversational AI solutions like voice assistants and chatbots.
That’s not surprising, given how the power of conversation is poised to fundamentally transform Customer Experience across industries.
The use of consumer-grade digital assistants has exploded in recent years. Consumers have quickly moved beyond ‘talking’ to digital systems for basic information (weather, traffic, trivia, etc.) and now use them to engage in commerce and other activities. For example, half of respondents to a PWC survey last year said that they had made a purchase via a voice assistant, with an additional 25 percent saying they would consider doing so in the future.
While the thought of increased sales through conversational AI is sure to bring a smile to any business decision maker, one shouldn’t lose sight of this technology’s other benefits – particularly its potential to optimise all points of the customer journey.
The new journey
When it comes to locating information about a product or service, consumers are becoming more interested in simply asking for it, rather than typing or tapping to search for it. Conversational UIs offer many advantages over other digital interfaces, in that they help users to find information quickly, allow them keep their hands free for other activities, and perhaps most importantly, play on the human brain’s natural inclinations for conversation and engagement.
A recent survey found that consumers preferred chatbots over apps when it came to receiving quick answers to both simple and complex questions. The bar for human-like conversational experiences is being raised constantly through platforms like IPsoft’s Amelia, which are helping tilt consumer behaviours even further toward these types of interactions. As this trend takes hold, it would be in a retailer’s best interest to automate and optimise these engagements through conversation – not just to provide the best consumer experience, but to gain substantial competitive advantage.
For example, when a customer is making a purchase or asking a question, modern conversational systems – integrated and automated end-to-end to back-office systems – can tap into individual purchase histories and other data sources to organically up- or cross-sell additional items (e.g. “Hello Mr. James, thank you for purchasing your new smartphone, would you also like to purchase a screen protector as you did with your previous device?”).
Similarly, more advanced systems allow brands to scale and target marketing/messaging campaigns to very specific segments within the confines of a conversation. For example, an AI system fronted by a conversational UI could alert a 25-year-old consumer who has purchased more than $200 worth of goods in the last six months (indicating she likes the retailer’s products] about an upcoming weekend sale.
As these marketing strategies can be modified instantaneously at scale through automation, companies are free to experiment and A/B test different approaches, determining the best response – such as making the same offer to “men aged 18 to 25 in London” versus “any consumers who spent more than £100 in the last year”.
Conversational AI can also be used to enhance the conventional brick-and-mortar experience, using voice-enabled kiosks or mobile apps. These channels can provide in-store customers with instant access to helpful information such as in-store locations of various items, or enhanced services such as scheduling deliveries. When implemented on site, this new functionality benefits customers through access up-to-date information and services, and it also frees employees on the floor from answering routine customer FAQs to work in other areas.
Humans are designed to experience the world through conversation. Up until recently, this inclination was somewhat incompatible with modern consumers’ expectations for 24/7 access to goods and services, given the lack of tested and effective conversational AI interfaces. However, now that AI allows companies to automate and scale conversational engagements, they can completely reinvent the customer journey to engender consumer loyalty and generate new revenue.