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.”
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.
Artificial Intelligence (AI) plays an important role in Customer Experience, marketing, and personalisation; It has the power to generate predictions about what goods and services customers are likely to want, when the demand will arise or propensity to switch will occur, and which platforms they are most likely to purchase on.
As a result, AI has become critical for brands wanting to improve their capabilities to offer personalised experiences, offers and recommendations. What’s more, marketers should not overlook the importance and the business case for investing in AI technology to deliver high-quality personalisation at scale.
Personalisation is key to customer loyalty, customer experience and increasing sales, with Accenture recently finding that 91 percent of European and American consumers are more likely to shop with brands which provide relevant offers and recommendations. Clearly, enhancing a brands ability to engage with customers on an individual basis is critical for brand success.
However, investment alone is not enough – in order for AI to provide effective personalisation, it must be implemented and used in the right way.
Organisations need to develop more sophisticated AI capabilities which can collect and analyse data in real timeto offer tailored services and products. This capability must be advanced enough to work for customers as they move through an omnichannel journey which often spans across websites, apps, email and high-street stores, for example.
While it is true that AI has the potential to bring about this ‘new era’ of personalisation, the technology alone cannot guarantee quality personalisation. For that, business leaders must appreciate the value of AI, develop a clear strategy for implementation and ensure that it is monitored and improved by experts who truly understand it.
Make the business case for investment
One common barrier to the adoption of sophisticated AI and machine learning technology is business leaders being deterred by the cost of the initial adoption process. While it’s true that AI can remove the manual, time consuming, and often overwhelming challenge of managing and understanding vast amounts of data, just like any new technology, the initial set-up process can be expensive and labour intensive.
Also, the skills required to implement this level of technology mean that organisations may face the added time and expense of employing dedicated data scientists, and adjusting towards a culture where marketers and technologists must work closely together As a result, organisations may feel that there is not a viable business case to invest.
To counter this point of view, take a step back and consider the long-term benefits of the initial investment. If AI is implemented strategically, the future pay-off will be enhanced capabilities for personalisation, improved Customer Experience, and increased sales.
Even as far back as 2014, McKinsey found that maximising customer satisfaction, which today largely comes from offering personalised experiences, would result in a 15 percent increase in a brands revenue. More recently, research from Econsultancyfound that 93 percent of companies see an uplift in conversion rates from personalisation. Together with a well implemented AI strategy, brands can personalise even more effectively and potentially see even greater conversion rates.
Develop a clear strategy first
It is important to understand that simply having AI and machine learning capabilities does not guarantee that a brand can offer a higher quality of Customer Experience. In order to see significant improvements, organisations must first decide what CX problem they are aiming to solve, which data sets they need to collect and monitor, and how they are going to use the data to remove the particular pain-points that customers face.
Whether a brand wants to convert more website views to purchases, increase the number of customers returning to the site, offer a smoother transition across different touchpoints, or improve online self-service, these priorities must be decided from the outset. Then, the right data can be collected and harnessed to address the issue.
With an overwhelming amount of data being generated and collected by companies today, this is an effective way to streamline efforts and ensure the most important issues are dealt with first.
Shoe retailer Footasylum provides a great example of the benefits of strategic AI implementation. It focused first on the specific pain-point of friction in the customer journey between stores and the web by using AI to link in-store purchases with online systems such as loyalty schemes, to create a single customer view. The brand can now predict which customers are most likely to purchase particular products and when. As a result, it has seen an 8,400 percent return on ad spend. Footasylum’s next mission is to breathe life back into the high-street by using AI to enable the web to automatically share valuable customer information with brick and mortar stores.
Lay the foundations for advanced AI
In order to get a good understanding of which CX problems to address first, brands should undertake background research with marketers identifying which personalisation processes are currently creating the most conversions online, and which are less successful.
Another important foundation is to ensure that all data sets are integrated and consolidated. In order to offer recommendations in real time, brands must be able to predict consumer needs and use data to meet them at the right moment, on the right platform. AI can be used to accurately forecast where the customer will be in their decision making. Without access to all of the data about any given customer, there will always be a limit to how successful these predictions can be.
In conclusion, it is key to remember that although AI has great potential to offer tailored experiences for customers in real time, the initial investment does not automatically guarantee quality personalisation and a return on investment. For that, a solid foundation must be put in place by understanding where customer experience can be improved, deciding on a clear strategy for implementation and removing data from silos.
Then, AI has the power to offer personalised experiences which offer true value to customers and meet, or even exceed, their expectations.
The Barbican’s newest exhibition, AI: More than Human, is an artistic exploration of the possibilities thatmodern technology presents, examining the diverse potential of artificial intelligence (AI).
A particularly striking installation is MakrShakr, a robotic bartender which can mix cocktails for customers via an online pre-order system.While undoubtedly a fun gimmick, the introduction of AI into a traditional service role raises important questions about the future of our restaurants, cafés, and bars.
The food and drink industry is no stranger to new technologies, and the latest developments are an evolution of sector staples like the sushi belt and fast food self-service machines.However, the gradual move towards AI presents unique new challenges.Principally, to what extent can automation really reflect Customer Experience value generated by humans in what is an intrinsically personal sector?While few would argue that real employees can ever fully be replaced, increased automation should come with a few health warnings.
Choosing the right persona
Finding the right persona for an AI system is the first step to ensuring customers actually enjoy using it.It’s important to have a welcoming interface, but this can be quickly undermined if the technology doesn’t work as it should.Successful AI personalities like Alexa and Siri are approachable and lighthearted when the situation dictates, but they’re primarily programmed to be as helpful as possible so people can find what they want quickly.
In the service sector, making the interface fun and playful is especially important, but there also needs to be a level of emotional intelligence present for when things go wrong.Investment in self and situational awareness so that customers feel their needs (and frustrations) are understood goes a long way.For voice services this means ensuring bots recognise emotion and intonation when customers speak.And where the technology isn’t voice based, a simple on screen message – for example an apology in the case of slow service – makes technology feel as attentive as humans would be in that situation.
This isn’t to suggest AI can ever replicate the value ofexisting employees, who will always be the major drivers of high quality CX.Instead, AI should complement staff, freeing them up from administrative or procedural tasks and allowing them more time to engage qualitatively with customers and build brand loyalty and retention.
Upselling is a major part of successful service businesses – everything from ‘do you want fries with that?’ to making sure diners have dessert and coffee at the end of a meal.For AI, this represents both a challenge and an opportunity.Making these transactions appear conversational and informed is key; just think of the persuasiveness of a genuine recommendation from a well-read employee at Waterstones compared with the ‘frequently bought with…’ pop-ups seen online.
Like finding the right persona, successful upselling relies on engaging customers, showing awareness, and demonstrating genuine knowledge.Recommendations should be presented as being bespoke to specific customers, not just based on the habits of other people.
Keep it fresh
Multiple conversations with the same person do not feel like the same experience over and over again – and interactions with automated services should be just as refreshing.Where an AI uses voice, this might mean mixing up the repertoire and programming varied responses to common questions.For others, different aspects can be kept fresh.In the case of our robot barman, making sure the menu is regularly updated to encourage people to come back for more will engender regular customers.
At the time of writing, the Barbican’s robot barman has already temporarily closed because of technical issues – proving that the museum exhibition is a long way from the reality of frontline customer service. It is inevitable that automation will become more widespread, we just need to make sure that the consumer, not the technology, remains king.
Happy customers are returning customers; they are the ones that will leave positive reviews on social media websites and lift a company’s reputation.
There is no doubt about it, King Customer is ruling the roost and needs to be courted. But the game has changed – it’s 2019 and customers are aware of their privacy rights, newly reinforced by GDPR, and also of the ever-growing danger posed by hackers. Let’s look at the most important factors that have changed the face of customer service forever.
Word on the virtual-street
Brands need to understand that today’s consumers can exert an unprecedented amount of power by voicing their opinions and (dis)satisfaction via numerous communication channels. From email and phone to social media and live chat, customer engagement is at an all-time high.
However, the challenges have also increased. Complaints and less favourable reviews on sites like Trustpilot and Facebook can spell big trouble for businesses. An unhappy customer who might once have complained to the customer service desk can now tell the whole of the internet about it, and as H&M, Pret a Manger, and SnapChat can testify, negative publicity can have a terrible effect on reputation, which frequently translates to a downward direction for revenue and share prices.
Another sure-fire way to tarnish brand reputation is by mismanaging customers’ personal information. In 2017, 2.5 quintillion bytes of data were created each day, and the pace is only accelerating –by 2025 worldwide data is expected to grow 61% to 175 zettabytes. The potential of ‘big data’ to improve our lives by identifying health trends, predicting our lifespans, and selling us things we want to buy, is undeniable.
Considering this, it is no surprise that businesses are holding more data on their customers before. In fact, customer information is the backbone of a range of business processes. However, we need to consider why we are keeping all this data – for marketing? Or just in case? While these might have been acceptable reasons previously, under the rules of the GDPR, they no longer are.
Regulating the service
Regulations offer a great deal of protection to brands as they eliminate any ‘grey areas’. 2018 was a big year for privacy with the EU GDPR coming into force and over in the United States, the California Consumer Privacy Act (CCPA ) was signed to come into effect in 2020. There is a lot to think about for companies taking card payments already, especially in terms of Payment Card Industry Data Security Standard (PCI DSS) compliance. For those in the financial services sector, additional regulations by the Financial Conduct Authority (FCA) come into play.
The 2018 Cyber Security Breaches Survey found that 43% of UK businesses were a victim of a cybersecurity breach in the last 12 months, while Action Fraud reported that victims of cyber fraud lost £34.6 millionbetween April and September 2018, an increase of 24 percent compared with the previous six months. Therefore, it is of utmost importance for a business to ramp up their cyber security to be a match for the modern fraudster. Otherwise, their customers might fall victim to so-called ‘carding’ – the theft and resale of credit and debit cards.
One report revealed that Russian hackers were offering a six-week programme for $945, teaching aspiring cybercriminals how to find legitimate credit card data for sale and hacking into PayPal accounts. The damage caused to consumers by having their credit card details stolen was £4.6bn from British internet users and£130bn worldwide, and we don’t need to guess what that does for the customer satisfaction rating.
But it doesn’t have to get this far – businesses can take a variety of steps to avoid a data breach, including self-contained native apps to increase purchase safety, endpoint protection, anti-spyware, and antivirus software. Artificial intelligence is also bad news for cybercriminals and is getting more sophisticated all the time. AI solutions can monitor and respond to cyber-attacks in real time, enabling IT security professionals to detect threats they couldn’t before and improving their overall effectiveness.
Smiling was yesterday
While smiling is still part of the job, it is no longer enough to keep demanding customers happy. Instead, 21st Century customer service is about taking responsibility for the safety of valuable customer data. Businesses have to acknowledge that today’s customers are aware of their privacy rights, therefore data collection and storage transparency are vital to build and maintain brand loyalty. Thanks to regulations, clear responsibilities are assigned to companies, enabling them to meet customer’s demand for privacy and protection.
The UK’s first till-free grocery store has opened its doors in London, but owner Sainsbury’s has been warned to not to underestimate the “power of human interaction”.
In a month that saw the proposed merger between Sainsbury’s and Asda shelved thanks to a bruising report by the Competition & Markets Authority warning it would lead to customers facing price hikes, the new store is part of a drive towards increased convenience for shoppers.
Customers at the Holborn Circus outlet must download the supermarket’s Smartshop app, meaning they can quickly purchase items via Google Pay and Apple Pay, without the need to queue at a till – which have been removed. Only a helpdesk manned by a single member of staff is available for those who still wish to pay using cash or credit/debit cards.
The new system is being tested at the store for three months, with the downsides for certain customers being that the sale of alcohol and tobacco has been halted due to the current inability to manage age verification.
Clodagh Moriarty, Sainsbury’s Group Chief Digital Officer said: “This is an experiment rather than a new format for us – it hasn’t been done in the UK before and we’re really excited to understand how our customers respond to the app experience. We’ll be with our customers and colleagues all the way over the coming months, iterating continuously based on their feedback before we decide if, how and where we make this experience more widely available.”
However, retail expert Manu Tyagi, from Infosys Consulting, highlighted a potentially difficult balancing act now faced by retailers such as Sainsbury’s as technology continues to dramatically change the way we shop.
“The launch of Sainsbury’s till-less store in London reflects a wider trend in the retail industry, which sees many retailers replacing existing employees with AI,” he said.
“The last year alone has seen some fascinating developments such as the Amazon Go concept of ‘checkout-less’ supermarkets where, thanks to a host of sensor and deep-learning technologies, shoppers can browse, fill their baskets, and leave without queueing up to pay.
“These advances are unarguably impressive, and spell a bright future for the retail industry. However, we’re finding that some consumers still prefer the reassurance of human interaction – and this need should not be ignored or underestimated.
“For instance, in 2015 Morrisons reintroduced human-staffed checkouts for small shopping baskets – a move away from the wave of automated, self-service tills that have swept the nation. It turns out that people quite enjoy their everyday interactions with the smiling, familiar checkout operator; advice from a knowledgeable shop assistant; or just bumping into a friend in the local supermarket queue.”
One of the biggest challenges for retailers is not getting customers, but keeping them.
Loyal brand ambassadors are the backbone of growth and building long-term interest in a brand is no easy feat. Customers want personalised experiences and services that makes them feel as they are the most important client. A 2018 Bond Brand Loyalty Report found that 87 percent of people say they are open to having various details of their activity monitored in exchange for more personalised rewards and brand experiences. Consumers value convenience, time saved and flexibility and they will shop around for companies that can give them just that.
In addition, customers want white glove treatment should a problem arise. Even one negative experience can have a customer switch their allegiance to another firm. Efficient customer support has become a true competitive differentiator and businesses have an opportunity to stay ahead of the pack if they act quickly.
A recent Vanson Bourne survey, found that less than 50 percent of customers considered their recent retail interaction to be excellent or very good, which gives retailers a new goal to meet.
With Customer Experience becoming a key success factor, businesses are turning to new technology solutions to help them quickly scale, improve responsiveness, and increase conversion rates. AI-powered chatbots, for example, are helping retailers be more responsive to requests and can even offer customers a concierge-like experience by providing personalised suggestions based on browse history, previous purchases, etc. While these chatbots are delivering highly intelligent self-service, they are also working behind the scenes for the customer service teams, gathering pertinent information about the customer and the question or issue to help the agents provide quick and personalised support from the get-go.
So how is AI helping retailers build brand loyalty today? Here’s just a few ways:
1. Offering 24/7 Service
Most online shoppers aren’t browsing during normal business hours. And when they have a question, waiting until the next business day to respond could mean losing the sale altogether. Chatbots are helping retailers be available to their shoppers 24/7 – answering the most frequently asked questions with ease and helping to ensure customers are getting what they need while they are ready to buy.
2. Creating a concierge experience
As AI continues to evolve, it’s starting to move away from handling simple questions and into acting as a customer’s personal assistant while shopping – providing personalised recommendations, reminding shoppers of sales to help them save, providing content to help with decision making, and much more. This level of proactivity means retailers don’t have to wait for the customer to engage, but can start building relationships by delivering information at the right time and in the right way.
3. Freeing up human agents
With all this talk about AI, what about the human agent? Individuals still play a pivotal role in the overall Customer Experience – but they are being leveraged in a different, more strategic way. Most customers hate waiting on hold – especially when they are having a problem. AI removes the need for customers with simple questions to clog up the human agent queue and allows customer service teams to spend more time with the customers that need them most. They are not only in a better position to resolve issues faster, but can spend the time to turn a sour experience into a positive one.
So are customers really onboard chatting with bots? The Vanson Bourne study also uncovered that more than half of customers agree that AI is changing Customer Experience for the better. I suspect this number will grow as the technology evolves and becomes more mainstream.
Today’s AI is all about delivering customers an intelligent and frictionless experience throughout their entire journey. When customers feel valued, they continue to come back again and again.
To create the best possible Customer Experience, organisations must think strategically about implementing the tools that will support their omnichannel strategy.
As adoption of live chat increases, simply implementing the technology won’t be enough to set a business apart in terms of the CX they offer.
According to Walker, today’s consumers expect consistent and user-friendly experiences from any brand they interact with, which is why CX is set to surpass price as a key differentiator for consumers by 2020. A business’s CX offerings are no longer just measured against their competitors, but against what consumers know is possible with CX. To meet these high expectations, organisations must be mindful of how to properly service their customers while making good use of their agents’ time. It will also be important to prioritise quality over quantity and ensure customers are serviced on the digital channels they prefer.
Customers are making the shift to mobile in waves and, as this occurs, businesses must ensure they provide seamless and consistent CX regardless of device or channel. The fourth annual live chat benchmark reporthighlighting the future of live chat and its impact on CX from Comm100, a global provider of omnichannel CX solutions, found that last year, chat queries sent from a mobile device increased to nearly 52 percent, representing an almost eight percent increase from 2017.
As customers continue to pivot their primary device usage away from desktop to mobile, mobile chat optimisation is becoming a critical strategy for all brands, but particularly for those in the consumer services and recreation industries.
Focusing on improving CX metrics alone may not be the best decision for brands. Brands that scored 90 percent or higher for customer satisfaction had an average wait time of 46 seconds, while customers that reported the lowest satisfaction ratings had an average wait time of 25 seconds. While many organisations strive for short wait times and quick conversations, these metrics do not necessarily indicate more efficient agents and increased customer satisfaction. It is easy to sacrifice the quality of the Customer Experience for efficiency, but organisations who emphasise quality of service over arbitrary targets will have an easier time meeting overall business goals.
The report indicates that companies and agents are close to achieving the right balance between speed and quality. On average, chat duration saw a decrease of four percent, with chats lasting an average of 11 minutes and 53 seconds. This continued the trend of shorter chat times, following the nearly 15 percent drop in 2017.
Just as with wait time, companies with a 90 percent or higher customer satisfaction rating had an average chat duration of 12 minutes and 26 seconds – 13 percent longer than organisations with lower satisfaction scores. Having meaningful, personalised experiences that address customer needs is more important than only attempting to lower metrics like wait time or chat duration.
For longer chat durations that take up your agents’ time, AI can step in to help balance out the workload. To ensure resources are used efficiently, organisations can route chats through AI-powered chatbots to offset chat volume and free up their agents for more complex queries. Chatbots with Natural Language Processing (NLP) and machine learning capabilities are now involved in over half of all chat interactions. They’re proven to be able to handle nearly 27 percent of those interactions without an agent, almost a seven percent increase from 2017.
Another tool that agents can rely on for optimising their workload is co-browsing. When an agent can view and interact with a customer’s web browser in real-time, it allows them to troubleshoot issues more efficiently, making co-browsing one of the quickest and most well-received ways for agents to solve customer problems.
The report found that co-browsing sessions have an average satisfaction rating of 89 percent – six percent higher than the overall 2018 customer satisfaction rating of 83 percent. Customers may complain that canned messages are robotic or impersonal, but when used correctly it can help decrease an agent’s workload without sacrificing quality, which is why the use of canned messages has increased nearly 70 percent in one year.
The benchmark report’s findings indicate that consumers are readily embracing live chat, so long as the focus remains on improving their experience. To stand out from the competition and exceed customer expectations, brands need to focus on strategically implementing their omnichannel customer experience solutions in a way that prioritises personalised, consistent service without putting a strain on their resources.
Over the last few years, we have seen a shift to more conscious consumerism that values experiences over things.
This can be attributed back to several trends, such as the rise of mindfulness, the minimalist trend in homeware, as well as the tidying trends like KonMari and Swedish Death cleaning. These, combined with the very real effects of climate change, are changing the way consumers think about their purchases. This was spotted as a trend back in 2017 by Euromonitor and does not seem to be reversing any time soon.
What this change means for businesses
Remarkably, few large companies are truly adapting to this change in mindset. Even then, a lot of it is paying lip service to the consumer experience rather than embedding Customer Experience at the core of the organisation.
Persil’s “Dirt is good” campaign is a great example of how messaging has moved away from the product to focus on the experience the product enables. In fact, Unilever seems to be one of the few large organisations truly keeping in touch with the zeitgeist as their recent Sustainable Living Plan has shown. And it’s working – their sustainable living brands grew 46 percent faster than the rest of the business and delivered 70 percent of its turnover growth.
Even with these great examples, and the rise of the B Corporations with ethics at the core, traditional industries are constantly being disrupted by more nimble startups that put Customer Experience at their core. This is a bit of a no-brainer, really. Traditional companies can float happily on repeat customers whilst startups literally live or die on the experiences they provide. I recently wrote about this at length, but I very much doubt that this illusion some companies have that technology will save them is anything more than that.
How AI can increase productivity
First, it’s important to realise that what we term ‘AI’ is a bit of a red herring. True AI, in the science fiction, self-aware state, is yet to emerge. What we are using quite effectively in business settings are things like machine learning, natural language processing, and speech recognition systems. I talk about this at length here.
The best thing organisations can do to improve productivity is to take a long hard look at internal systems that enable staff to work more efficiently. What is the use of enabling chat function on your website if your customer support person still needs to log into three different systems to get a simple answer?
With machine learning, we have the option to automate a lot of the menial tasks that create busy work and detract attention from what should really matter to a company – providing the best customer service you can, ideally a human one.
The importance of visibility for improving the customer journey
With this in mind, I would caution against solely relying on analytics as quantitative data tends to say a lot more about the biases and existing knowledge of those who form the questions and therefore only works when you already know the parameters that you are looking to confirm or validate.
We find that the best tools for truly forming a picture of the end-to-end Customer Experience is by mapping it with contextual research. Further value can then be added by overlaying this with the Employee Experience as well as mapping the third parties involved. This is known as service blueprinting. These maps can be used very effectively to set up analytics journeys and provide further validation for the issues arising in the customer journey.
Why businesses should strive for the perfect mix of automation and human contact
Over the last 10 plus years, we have been dedicated to letting customers self-serve. In fact, I have helped create a multitude of self-service help sites. They can be very useful. What has now started emerging in research sessions is that people really hate them…and chat bots (more on this in a minute).
One of the interesting things we found back in 2012 when running usability tests on the newest iteration of BT Business Self Service (which has since been updated several times) was that participants were far more likely to try using the self-service flows when they could clearly see the contact number.
This has stood the test of time. When you allow people to try but also provide an intervention point that enables them to talk to a real person, they are more likely to have a go first. eBay, with their impenetrable help flows, could do with taking this lesson on board, but they are by far not the only offenders in this category.Royal Mail’s business help doesn’t look too great either for allowing human contact.
People have also started bring up chat bots as these often mis-interpret the text and provide rubbish answers whilst masquerading as real people. There was a fascinating article on this fairly recently, but in essence, what we keep seeing is that when people know they are chatting to a bot, they tend to be far more forgiving than when they feel another human is treating them like a fool.
I firmly believe we have reached peak Customer Experience automation and to further automate customers out of direct contact with the company is foolish. I recently wrote about this in a housing association context but it is very applicable to all other industries – have you ever been to a Tesco Express to be faced with a row of self-checkouts and no till staff? This happens to me all the time and I believe it’s not a great experience. There is merit in mixing the automation whilst still always providing human contact.
Technological sophistication is all pointing towards one thing: reducing the requirement for human labour, and input.
But in the fintech industry, humans aren’t simply a necessity – they’re irreplaceable.
Human developers are obviously needed to write, and maintain code, as well as learning and understanding the products that make the code necessary. Say these processes eventually become automated, the need for humans would remain.
That’s partially due to the fact ‘build it and they will come’ isn’t applicable to the disruptive world of fintech. Even the most boisterous, compelling and revolutionary products need effective branding, marketing and PR teams that will help the product succeed. Particularly as we see the rise in the importance of search ranking, SEO professionals will also have an integral part to play in making their fintech visible in an increasingly competitive space.
Moreover, while technology drives many fintechs services, certain businesses need real-time human emotion to overcome unique challenges. For some enterprises, such as online mortgage brokers, they’re task is to convince consumers to change from traditional brokers to their banks or existing lenders. This is just one case in which human empathy can’t be replaced or simulated by technology.
Understandably, customers can be hesitant to trust small, relatively unknown startups with their personal and financial data, especially if it involves one of the biggest financial decisions you can make.
Recent research conducted by TopLine Comms found a whopping 83 percent of respondents were ‘unsure’ of fintech companies and how they work. Insightfully, 27 percent ascribed a lack of understanding as the reason why they were unsure.
Consumers will only trust Fintech firms once they understand and address customers’ concerns, one pertinent method to educate and earn potential consumers trust is through marketing, communication and branding.
But that’s solely acquiring customers. Consumer experience is what turns a cynical user into a fully-fledged customer, and a hybrid approach that encumbers an equilibrium of tech and human interaction is likely to be the key to the best customers experiences in fintech.
For example, fintechs in the banking industry can use technological innovation to alleviate their human advisers from the arduous and time-consuming parts of the job. Now finance experts can spend more time sourcing the best advice for their customers and building a relationship that raises trust between both parties.
These relationships build advocacy, which can in turn convert others – offline and online word of mouth is undoubtedly one of the best methods of attracting new customers. Ninety-three percent of respondents in a recent survey by Podium said online reviews affected their decision to make a purchase or not.
Many people intend to use an online fintech, as opposed to a traditional service because they won’t have to deal with a human when liaising with the former. Often, customers aren’t too keen to talk about their own sensitive financial situations issues and said customers in these circumstances may prefer to not speak to an actual human. Entirely digital experiences, that use complex technological features such as Artificial intelligence in the form of Natural Language Processing (NLP) and machine learning, could be the best solution for them.
The truth is that both kinds of consumers exist – and so a hybrid model that uses tech and human interaction flexibly, to improve the Customer Experience may be the most pragmatic approach.
As fintech moves into the mainstream, consumer attitudes may also move in a certain direction. The more sophisticated and accessible technology becomes, the more likely it is that people may start to feel less anxious about trusting new businesses with their data. The arrival and widespread adoption of Open Banking could be the catalyst to start this paradigm shift.
But in the meantime, fintechs must combat the dichotomous challenges of converting sceptical customers and making themselves stand out in a crowded marketplace. In both cases, humans will remain an integral and irreplaceable element to any fruitful fintech.
Giovanni Toschi is the Founder of AI firms Jatana and BotSupply. The Copenhagen-based entrepreneur took time out to talk to CXM about how we are firmly in the middle of the Golden Age of Customer Experience, and where the industry can go from here…
Your role must give you a great perspective on what businesses need and what customers expect from them. How do you see the overall role of Customer Experience today?
Customer Experience is probably going through its Golden Age right now. The awareness of businesses all around the world has grown a lot and they really do care about CX and overall customer satisfaction. No business is unique, everyone has competitors; it’s the relationship with your customers that makes the difference between successful companies and the rest of the businesses that fall behind.
What do companies often do wrong when it comes to CX?
There is more than one thing. First of all, many are faking it. They try to seem like they really care for the customer while they actually care for the cash. Yes, everyone is in the business for the money, but that does not mean you should treat your customers as a number or data. They’re not, and they know when you do that.
Second, they do not devote themselves too much. Low effort to satisfy a customer in most cases end up with bad results for the company.
Customer support plays a big role in Customer Experience. How do you see it?
Interaction between the company and the customer is half of it, honestly. The ways you connect with your customers, including providing customer support, is a key differentiator today.
Automation is a hot topic. Do you think it improves the efficiency of a company and Customer Experience in general?
Absolutely yes, if used correctly. Customers today want everything almost instantly. Twenty-four hours to reply is no longer enough – you have to act fast. But they also want you to show effort and focus on them as an individual. That’s where automation kicks in. It provides instant replies to frequently asked questions, and agents can focus on the more complex topics and connect to the customer on a personal level.
When it comes to automation, it is often associated with the fear of AI replacing humans and taking over their jobs. Do you think this is true and how do you see the future with ‘robots’ as our coworkers?
The same fear was present with the industrial revolution, yet we did not lose jobs, we just created new ones. Machines can replace humans in many positions, but that only means new positions will open. Humans will always have their advantages over robots.
What exactly is Jatana?
We are on a mission to bring Artificial Intelligence to customer support teams of any size. Using Jatana, any company can set up AI automation in their contact centre in a matter of hours. Our solution allows support agents to focus on the issues that matter while leaving repetitive tasks to the AI.
What inspired you and your team to create this tool?
Since 2016, at BotSupply, we have been helping companies like Carlsberg and Mercedes leverage conversational AI to provide better Customer Experience. In the process, we kept on getting requests to develop a solution that could do the same for email support. We put together an initial MVP and after closing the first customer we decided to spin-off the product into a stand-alone company and that’s how Jatana was born.
Could you give us an example of a company that successfully included your tool (or any other automated service) into their business?
We have been operational for a few months only but our customer base includes companies from Scandinavia, as well as other parts of Europe and Asia. A good example is Stocard, a fast-growing German company that developed an app to keep all your loyalty cards in one place.
What is your message to the readers of Customer Experience Magazine?
If you’re reading this magazine that already means that you do care about Customer Experience. That’s great – stay on the right track, follow what’s trending, and don’t let competitors leave you behind. Try to be one step ahead, as that’s how you win the race.