Organisations are increasingly relying on chatbots for customer service as a way to deflect inbound calls and reduce costs, but Forrester Analytics data shows that consumers aren’t thrilled with this approach.
They found consumers are reluctant to trust a chatbot to resolve their service issues, and remain skeptical that chatbots can provide a similar level of service as a human agent.
However, it seems companies are enforcing technology solutions at every opportunity in the hope of improving their CX strategy.Amid all the talk of technical advancement, we seem to forget about the human factor, that personal touch that only people can deliver to consumers no matter the channel of engagement.
Clients today have huge goals for their CX strategies, such as 50 percent of all calls to be self-serviced, 80 percent of contact to be automated, 50 v of calls to be eliminated and all of this along with, in most cases, being tasked to make significant cost savings.
Ian Jacobs, Principal Analyst at Forresters, said: “Customer service organisations have been looking for ways to cut costs for decades.Now that chatbot mania has taken over, many are jumping on the bandwagon and attempting to replace their human agents with chatbots.In theory, that makes sense – a chatbot costs less than a human over time, and most customer service organisations tend to focus more heavily on cost than on customer experience.”
It is fair to say many companies are struggling with automation, whether to automate, why automate, what to automate and when to automate.According to Forrester, when it comes to automation and customer service, brands are getting the last three questions wrong.
We regularly see in the industry news headlines such as Chatbots set to take over most cost service work or Robots are set to replace humans and costly contact centres.However, according to Forrester’s Analytics Consumer Energy Index on-line survey 2018, consumers expect chatbots to disappoint with 54 percent of US online consumers expecting interaction with customer service chatbots to negatively affect their quality of life.
The message from Forrester’s research is clear: ‘Augment, don’t replace and blend AI and humans’.
The question is which blended operations model works better for your business?
Forrester has identified four approaches to agent augmentation:
1. A chatbot for agents where the conversation is with a live agent and a chatbot
If your customer service agents search a knowledge base during their interactions with customers, why not create a natural language interface for that task?It’s a great starting point if you’re just beginning your chatbot journey.
2. A human-intermediated chatbot for increased efficiency and seamless suggestions
Here, an AI tool observes a conversation between a human agent and a customer, providing suggestions that the agent can either push out to the customer, or modify, or personalise the suggestion, or even reject it and type their own answer.
3. A front-end chatbot where the chatbot authenticates the customer and determines intent and gathers all relevant information
Chatbot hands interaction off to the agent and the agent resolves the customer’s issues.The benefit of this one is that agents are handling the meat of the interaction and have more time for upselling/cross selling and it also significantly reduces handling time.Conceptually agents are more engaged as they are adding value and not doing mundane tasks.
4. Intermingled workflows with both agents and chatbots do what each does best
The human agent can invoke a chatbot to handle a specific task, then have the chatbot hand the interaction back to the agent.Similar to the front-end chatbot, human agents are relieved of routine tasks, but in this workflow, the agent and chatbot can flex back and forth to tackle the portions of the interaction they excel at.
Using one or more of those approaches to augment customer service agents can result in significant benefits to an organisation, such as reduced handle time, increased employee engagement, and improved experience -0 while also ensuring your customers don’t lose faith with your brand after a frustrating chatbot interaction.
One thing is for sure, AI is here to stay. Brands that want to keep ahead in a competitive world will need to re-think their business models and make sure there is a place for human employees and AI.
If you’ve ever visited a website and been greeted by a human-like pop-up asking “How may I help you?”, you’re not alone.
According to Comm100, nearly 50 percent of consumers already engage in automated conversations with chatbots. And, according to Gartner, these numbers are growing.
Gartner predicts that 25 percent of customer service and support operations will integrate chatbot technology across customer service channels by 2020. The same source reports that in 2017 fewer than two percent did so, marking a huge jump in adoption of this technology in a relatively short amount of time.
With any business trend, organisations can feel pressure to adopt quickly, fearful that they will miss out on revenue and engagement opportunities if they do not use the same technologies as their competitors. However, simply deploying the latest technology does not guarantee companies will immediately begin delivering a great customer experience.
Organisations should be thoughtful in the way they strategically plan before implementing chatbots – AI-powered or not – to ensure that they are contributing to a positive customer experience, rather than just masking existing CX flaws.
How digital body language can guide when – and when not – to deploy a chatbot
A recent report by Juniper Research estimates that chatbots could help lower annual business costs by more than $8 billion by 2022. Chatbots also increase efficiency. By using AI-powered chatbots to process simple requests – account balances, due dates, etc. – agents have more time to have more personal, in-depth interactions with the customer via live chat.
These in-depth interactions also include successful sales conversions: the American Marketing Association reported that live chat increased sales by up to 20 percent.
With chatbots increasing efficiency and live chat boosting sales, bringing technology into customer interactions seems even more enticing than ever before. However, companies must consider how they will design their bot strategy so that it helps – rather than harms – Customer Experience.
I spoke with Tim de Paris, CTO at Decibel, a Digital Experience intelligence company based in Boston and London. He shared his thoughts on how chatbots can actually damage a customer’s experience if deployed ineffectively.
“To make a chatbot successful, organisations must have insight into how users are feeling about their experience,” he said.
“If a chatbot pops up asking the user if he/she needs help during an experience where they clearly don’t need help –like right when the page opens, or when he/she is already engaged in a positive experience – the chatbot interruption will only irritate the user, pushing them away rather than serving as a helpful assistant.”
According to de Paris, bots should be equipped to understand users’ digital body language: is the user engaged and ready to purchase? Or showing signs of confusion through scattered mouse behaviour? By being able to identify user pain points, brands can determine the best time to interject with a chatbot.
For example, if a customer is bouncing from page to page on a website and showing frantic mouse movements, clearly showing signs of frustration, the chatbot should step in to help, and even potentially pass the interaction off to a human agent who might be better positioned to help.
Conversely, if a customer has viewed one page for a significant amount of time and is flipping between shirt colours, he/she could be toward the end of the funnel, about to make a purchase and just contemplating last minute details. In this case, the chatbot should stay quiet, avoiding interrupting the customer’s decision.
“Only when organisations have insight into users’ digital body language with the right digital experience technology can chatbots be deployed at the most effective time,” said de Paris.
Best practices for implementing chatbots
Chatbots are immensely useful in boosting the efficiency of a company’s contact centre, but they are not a ‘one size fits all’ tool. Some bots can analyse text with natural language processing (NLP), whereas others only offer predetermined response options for users to interact with.
To successfully bring chatbots into the contact centre, companies should begin by being honest about the chatbot’s capabilities. By being up front about what a bot can and can’t do, customers will know right away what they can achieve in their interaction with a bot, and companies will understand when it’s time to transfer an interaction to a human agent.
Some chatbots are most helpful with basic questions – generating account balances, sharing business hours, etc. – but an agent should be brought in whenever the customer’s needs go beyond the bot’s capabilities. It is important that contact centres identify when a customer’s needs would be better serviced by a live agent based on a range of other criteria such as status, shopping cart value, geography, or the relative value of their query – every company will have a threshold above which they would prefer the question gets handled by a human.
Once a company deploys a chatbot, it should take advantage of all the metrics that the service provides to fine-tune the applications as needed to offer the best interaction. This includes feedback from post-chat surveys, recorded wait times, conversation lengths and customer satisfaction scores. This data can be used to identify trends as well as areas of strength and areas that need improvement.
While continuously using the metrics that the chatbot provides, businesses should be prepared to maintain the chatbots for best performance outcomes. Implementing a chatbot is not a ‘set-and-forget’ solution, but requires constant monitoring and improvement to best serve the agent and the customer, leading to a better interaction across the board.
After all, positive Customer Experience leads to more customer conversions.
Chatbots are here to stay, and if companies are going to use them, they need to know how to do so successfully and efficiently. By bringing a personal, customised experience to prospective buyers on digital channels, companies can improve the customer experience and increase revenue.
There was something strange in the neighbourhood of Denver, Colorado this summer at the Xperience19 conference, hosted by Genesys.
The theme for one memorable breakout session on how to build a chatbot was spooky comedy classic Ghostbusters, and as you might expect, plenty of fun was had along the way.
Armed with a Dialog Engine Natural Language Understanding (NLU) model, Intelligent Automation, our Genesys skills, and the Genesys® PureConnectTM application, our attendees set off to build.
As in all ‘Build-a-Bot’ workshops, a team of experts was in attendance to provide information on how to build a chatbot – but not just any chatbot!
This was a Ghostbusters chatbot, and to get into the ‘spirit’, the Genesys experts were decked out in Ghostbusters hooded jackets, thanks to Joe Ciuffo.
As more businesses embrace bot technology and understand its value, more people come to us for advice and instruction.
This session was no exception, with around 80 keen delegates in attendance. Armed with laptops open and ready to go, Marc ‘Venkman’ Sassoon took to the stage, first asking the audience if they remembered the famous 1984 movie itself (thankfully they all did, despite some being born after the year of release)
He then created a use case: in the movie, Janine, the Ghostbusters’ sarcastic secretary, struggled with customer service. Due to a ghost invasion in New York City, the team’s phone was ringing off the hook, making it tough for Janine to keep up with demand.
This Genesys Ghostbusting team had a solution – it quickly deployed a Genesys artificial intelligence (AI)-powered chatbot that uses Intelligent Automation with Dialog Engine.
Yours truly, Jonathan ‘Stantz’ McKenzie, joined Venkman onstage to showcase the power of these technologies. The process was simple – we just needed to assign it with Dialog Engine utterances, intents, entities, and slot values, before letting it get to work.
Build, test, and update the bot
Armed with this intent – to use a bot to handle customer conversations by chat or voice demand – Marc showed the attendees, in detail, how to build a chatbot using Intelligent Automation. He selected some of the 80-plus pre-built microapps Genesys offers. These include the Intelligent Automation Natural Language Menu microapp, which seamlessly integrates with Dialog Engine NLU.
The chatbot was taking shape.
For the bot, NLU does an analysis of each utterance, classifies the entity, and selects slot values from each utterance, as shown below.
Once Intelligent Automation and the Dialog Engine are configured, flow and NLU combine to deliver the chatbot.
The next step was to test it, with attendees using a conversation about ghosts spotted in the boroughs of New York.
The final task was to make changes to NLU models, update the utterances, add more entity types, and of course have some fun. Each update occurred instantly and was visible in both Intelligent Automation and the chatbot.
Give chatbots and voicebots a try
According to Accenture, well-designed bots can resolve 80 percent of customer interactions. Bots also make it easy for customers to engage with you in the ways they prefer – whether it’s calling to schedule a Ghostbuster, chat online, or self-serve in any number of ways.
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.
Providing a first-class Customer Experience is a goal every organisation pursues – it’s no longer a luxury only large brands with big pockets can afford to focus on.
Consumers now expect to experience the ‘perfect’ buyer journey with every business they deal with – no matter how big or small. Research by Kampyle illustrates this, showing that 87 percent of customers think brands should put more effort into delivering a better Customer Experience.
Thanks to recent progress in technology, this now all achievable. Conversational software – chatbots, in particular – make providing an excellent, hands-on, personal experience to customers possible. Below, we will dissect how a chatbot can assist and delight your customers at every step of their interaction with your brand.
There are four key touchpoints between your customers and your company:
Maximising delight at each of these touchpoints is primordial to providing an excellent Customer Experience.
Let’s see how chatbots help.
Your customers, for the most part, will first get in contact with your brand through your website.
Surprisingly, websites are still hard to navigate for the average user. A simple UX testing exercise can uncover the many ways in which your website visitors get lost between opening your homepage and getting to the goal you want them to reach.
Strategically placed on your website as a widget, a chatbot can offer help throughout the experience. It will be able to pop up, offer help, send accurate information, and drive the user to its goal – all by itself.
Not only does your chatbot provide users with the help they need, it also delivers results. Ubisend has reported that a website widget chatbot converted28.3 percent of its helpful conversation with users into warm leads for the sales department.
Meanwhile, some brands out there don’t own a website, such as some local restaurants that deal straight from their Facebook page. Chatbot can live on there as well – and achieve the same results.
A chatbot is not just for first touch. The power of a chatbot is its conversational nature tied to its extensive knowledge. In a nutshell, a chatbot can talk to your customers several times over a long period of time and continuously learn about them, providing them with the next best action.
Nurturing leads with a chatbot is the future of marketing. Forget about users losing sight of what they want to achieve on your website; your chatbot knows exactly who that person is when they come back and can nurture them towards your business goal
The user experience here is clearly enhanced – no more endless searching. As the user logs back onto your site, they don’t need to figure out what the next step is. A helpful digital assistant is there to help.
We all buy from the internet. It has become part of our lives.
And yet, we’re all still very apprehensive when doing so (unless we buy from the giants like Amazon). Does this product actually suit me? Will this wardrobe fit my bedroom? What if I don’t like it, can I send it back?
As business owners, we’ve been trained to try and answer all these questions preemptively through copy, FAQs, and unboxing videos.
Even so, the stress is there for some users, turning them off from buying altogether. Need I remind anyone that the average landing page converts only 2.35 percent of visitors?
This is where a chatbot can help. Strategically placed on your sales pages, a custom-built chatbot will know everything there is to know about your product or service. It will pop up to offer help, answering all the questions your customers have about what’s on the ‘other side’ of their purchase.
Though it comes last, customer care is most likely the first thing that comes to mind when thinking of enhancing the user experience.
Providing the best, most attentive, empathic customer support experience is a must-have these days. We’ve all started to expect 24/7 instant answers; we’re likely spoiled by the likes of Amazon.
The good news is that chatbots can help you with that, too.
A customer service chatbot can sit on your service desk and answer questions at incredible speed, 24/7.
Now consider that your chatbot never sleeps, never gets grumpy, never gets bored. It does its job, all day every day, helping your customers get the answers they need in an instant.
The current conditions for UK high street retailers are far from favourable.
Not only are they battling market pressure and challenges from ecommerce competitors, but also increasing rents and tough trading conditions. To ensure survival, retailers today must keep their finger on the pulse of all the latest technological advancements.
What businesses must remember is that as technology advances, so do customer shopping behaviours and expectations. We are already starting to see more and more businesses implement chatbots, artificial intelligence, and messaging apps to keep up with demand. We are in a time where consumers have never been more vocal on their wants, and this is highlighted with the results of our research.
Our data showed that in order to satisfy customer needs organisations have to offer a variety of channels in which to engage with – 81 percent of respondents demanded this. Consumers not only want choice but also, a seamless, integrated experience across all of them. Taking this one step further, many consumers are wanting Augmented Reality (AR), Virtual Reality (VR) dressing rooms and even drone delivery, to be a possibility.
Termed as “technologies of the future”, large, online and in-store retailers are already reaping the benefits of AR and VR in an attempt to make the customer journey more immersive and engaging. For instance, IKEA, has introduced Amazon’s AR view to help customers visualise how furniture will look in their home before making a purchase.
For customers who prefer ‘ease of use’, these new technologies couldn’t be more perfect as they allow consumers a chance to ‘try’ before they buy. As well as convenience, AR and VR are helping stores to stand apart from the traditional retailer. L’Oreal Paris, for example, guarantees loyal customers with an in-store virtual makeover tool that enables you to try make-up and certain looks before buying. On paper, it has never been so simple for retailers to deliver a more engaging and convenient approach to Customer Experience.
However, AR and VR are not the only new inventions transforming the CX landscape. Increasingly, we are seeing chatbots being used as a more convenient way for customers to interact with brands, specifically when they require assistance. In fact, 87 percent of businesses say self-service customer enquiries are a current priority of theirs.
Apart from the obvious benefits like saving businesses money, self-service chatbots are improving CX and satisfaction. It’s fair to say we have all had our share of agonising waits and frustrating calls with agents and can therefore, understand the appeal of having access to instant help and real-time information.
In the age of GDPR and data sensitivity, customers are very particular about who they give their personal data to. With this in mind, retailers must remember to be upfront about who or what they’re speaking to. Giving customers the option to self-serve will only succeed if you’re as transparent as possible.
Ultimately, although traditionally we associate a human touch with CX, retailers that do not adopt the latest technologies and integrate them into the customer offering jeopardise losing the loyalty of existing customers as well as potential new ones.
However, before businesses embark on this digital transformation, they must remember not to run before they can walk. Implementing chatbot technology initially can be just as effective as implementing technologies which are grabbing the headlines, such as AR and VR.
There is ever increasing interest in the role emotions play when managing Customer Experience in the contact centre.
At the same time, there is a drive to introduce technology such as chatbots to make customer service teams more efficient; removing repetitive tasks and providing ‘always on’ customer service. These potentially conflicting trends are happening at a time when the demand for customer service is growing, and organisations are fighting to differentiate themselves through their customer service offering.
A recently commissioned study by Forrester Consulting suggested that 90 percent of customer service leaders agree personalisation is core to the future of automation, and existing chatbot technology is stalling their efforts. The key challenge is to build simple yet personalised experiences for customers.
As Maya Angelou famously said: “People don’t always remember what you say or even what you do, but they always remember how you made them feel.” If your chatbot or AI solution leaves the customer feeling frustrated or angry because they have to put in more effort to get the answer to what they perceive is a routine query or task, all that is being achieved is an increased chance that the customer will look for an alternative supplier who can make this task easier.
In addition, quite often humans want to talk to humans. A study by PwC found that an average 74 percent of non-US consumers want more human interaction in the future and that 59 percent of all consumers feel companies have lost touch with the human element of Customer Experience.
Certainly, there have been strategies employed whereby chatbots are being disguised as humans which can only lead to frustration on behalf of the customer when they find they are being deceived and the bot cannot fulfil their needs for a more emotional or complex issue response. While customer views are constantly evolving, I still think Userlike got it right with their view on avoiding the ‘uncanny valley’.
Organisations need to be up front when a customer engages with them by disclosing that they are talking with a bot, and take advantage of the benefits that can be gained when effectively deploying it for more routine and simple tasks. In addition, they need to give the customer the opportunity to seamlessly switch to a human agent, without the need for the customer to repeat themselves. In short, make it easy, make it simple and, when the customer is speaking to an agent, make it personal.
No one can deny that AI is getting better and better, and chatbots will certainly have their place in our future. A well-designed customer-centric journey will allow the bots to tackle low level tasks, but companies also have to be cautious in blindly launching bots into the contact centre eco-system. When poorly executed the effect upon customers can be detrimental to their overall experience. It’s all too easy to deploy a chatbot that can get stuck in a loop, resulting not only in an increased cost to serve but also a decrease in overall customer satisfaction.
Hockenbury & Hockenbury in Describing Psychology (1997) described emotion as “a complex psychological state that involves three distinct components: a subjective experience, a physiological response, and a behavioural and expressive response”. Delivering customer service for an organisation dealing with often highly emotive subject of money, we have yet to find an AI solution that can effectively replicate the human touch our industry-leading customer service team can deliver. They can handle the simple routine tasks well, but then so can a well-designed FAQ or Help Centre. Until such a time as when chatbots can manage all three psychological states, there will always be a need for humans.
Human agents have a big advantage. They understand compassion, they can demonstrate empathy and they have their own shared experiences of everyday life which continues to become busier and more stressful for us all. In having this unique skill set, the human agent is here to stay and will own the complex matters where a human touch is needed.
The recent Genesys Xperience19 conference in Denver, Colorado, saw some of the most exciting developments in Customer Experience technology showcased to an eager global audience, and in case anyone was under any illusion about the future of CX – it involves AI.
The tech itself is dispassionate, and can appear benevolent to users as it cheerfully helps them along their customer journey. However, decades of pulp sci-fi dystopia has left AI with an image problem – no matter how helpful it may seem, some simply cannot shake the idea that bots might someday pull a Hal 9000 and see humans as inferior and deserving of subjugation…or worse!
Such fears ought to be dispersed when one discusses the details of AI technology with the real intelligence behind it – someone like Olivier Jouve, Executive Vice President of Genesys Purecloud, perhaps the planet’s most popular contact centre platform.
Olivier took on the role of PureCloud EVP in 2017, having spent over three decades honing his craft in pioneering customer sentiment technology development, including through senior positions at IBM.
His impressive resume also reveals a stint as an associate professor in computer science at Leonardo da Vinci University in Paris, and today, with a 150-strong AI team under him, Olivier is one, if not the world’s foremost authority on AI and its ability to make our journeys as customers easier.
He knows, in detail, how much his tech helps us in our lives – often without us realising it – but still the idea that AI will have a negative impact on humanity can cloud the vision and judgement of some sceptics who see it as an evil overlord-in-waiting.
Fresh from a timely Xperience19 breakout session on AI Ethics, Olivier took time out to chat with Customer Experience Magazine about his work, its reputation, and just how much AI is used for the betterment of our lives as consumers.
Speaking of the “creepiness factor” that some associate with AI, its access to personal data, and how it could be used in the wrong hands, he describes why being open and honest with customers, and letting them see the advantages with their own eyes, is the best way to go.
“We want the customer to know that we respect their data, and we need them to see what data we are using, so they are able to opt out if they so wish,” he explains.
“In the way we build our products, we do a lot of design thinking with customers to understand where the limit is. You know, what type of data they are comfortable with.
“And of course, you are being careful not to introduce any bias, which is something that’s very complex – not using any gender, or lifestyle, race…whatever, that could turn your model into something that is going to be targeting a specific minority.
“This wasn’t on the table 15 years ago when we were already scoring contact centres for next best action, cross-selling, up-selling, and so on, and using that data. Now there is much more sensitivity about how you use the data, and I think that’s actually a good thing, as it forces us to be clear from the get-go.”
Olivier highlights that those who are creeped out by an AI’s use of data, to the point where they will walk away from it, are a small minority compared to those who see the benefits and remain loyal to brands brandishing the tech.
“Companies which use AI the right way will enjoy great benefits, by being fair and respecting privacy,” he adds.
A common cause of ‘creepiness’ is the notion that a customer is unaware if they are interacting with a human or a bot on their journey with a brand, but as Olivier sagely states, that uncanny valley effect is being superseded by good old fashioned customer satisfaction when the AI does its job – and does it well.
“Me, personally, I don’t care if it’s a bot, as long as I get what I want, quickly, and with a great experience,” he continues.
“I don’t think customers care as much about the technology they use as much as the experience they have. I do think we should disclose that it’s not a human though – that should be part of the disclaimer. But at the same time, I don’t see that as something that should be discouraging people, who may think ‘oh no I’m not talking to a bot as I won’t get anything from it’.
“That’s also a danger of going to market too quickly with AI tech – some chatbots don’t provide the right experience. There are, however, things chatbots and voicebots can do very well, and I think we should double down on those.”
As Olivier points out, it’s not as if customers aren’t already used to interacting with bots on a less ‘intelligent’ level already.
“When someone asks for the balance of their bank account, they don’t care if it’s a human giving it to them, so people are already used to this sort of automation. We just have to be careful that if we go deeper with more complex things that users don’t get the feeling we are not responsive.
“People like empowerment, and chatbots can be great for that. But there are still some limitations, so we are not yet at the stage where AI is going to replace humans. We have chatbots that are very specialised and do things very well, but we need to find the right moment where you have to hand over to a human.”
Yet will there be a day when there is no human to hand over to? Will we fleshies be redundant in a future where all the work is being done by bots?
“I don’t think it will replace humans, as we are putting humans into something they are really good at, and so I see that more as a collaboration between AI and human – something we call blended AI. We can do sentiment analysis automatically, but it has limitations,” Olivier replies.
So what are these human skills that we can still feel superior to the bots on, and that customers still desire on their journeys? What’s the key difference that currently keeps humans in contact centre customer-facing roles?
“Empathy – we aren’t there yet,” says Olivier.
“Humans are great at it, and we need to think of the overall CX, the CX we want to provide. AI does things a human cannot do because you could not integrate all the different insights you have about a customer, but AI is really good at that.
“However, to take the conversation to the next step, at some point currently you have to hand over to a human. Of course, even humans need to be taught empathy in some cases!
“Perhaps in 10 to 15 years we will be able to train AI better in applying empathy, but for now that’s why we need this combination of human and technology.”
Ok, enough of what humans can do better than bots! It’s time to let Genesys genius shine, as Olivier outlines exactly why today – not years from now – AI is simply superior in most non-emotional ways to humans when it comes to steering customer journeys and earning the desired end result – superior Customer Experience.
“Our products work on finding the best agent for an incoming call or interaction, something we do very very well thanks to machine learning. The AI is understanding what the topic of the interaction is and uses historical data and a sophisticated decision tree to move the interaction forward.
“We want to optimise the customer journey, so we have a solution called predictive engagement. We can look at what a user is going to do on a website, view their navigation, and see at which moment he or she might need some help, and decide what the best outcome is for this customer.
“Once you have this interaction you can develop additional models which could be for retention or selling – more things we do very very well that a human couldn’t match.
“When you do next-best action and you have a customer calling who is ready to leave, our tech knows if you go for a specific action, there’s an 85 percent chance that this person is going to remain as a customer, for example.
“That’s from crunching a lot of data, gained through similar situations, and handling so much data – well that’s not something a human can do.”
Other than the aforementioned empathy, does Olivier feel his AI is lacking in any other areas that might be beneficial to overall CX?
“I think what AI does not do very well yet is go deeper into the conversation,” he tells me.
“We see breakthroughs when we look at things like Google Duplex, where, you can find yourself questioning if you are interacting with a real human or not, but an AI able to handle 100 percent of complex interactions? I don’t think we are there yet, though we are making a lot of progress.”
Despite my impatience as a customer to know when exactly this will be possible, Olivier sensibly refuses to give a date.
“I don’t like giving predictions, but I see how fast we are moving forward. I think Duplex was really a breakthrough – suddenly you see something and you think ‘wow’ – the voice, the type of interactions…it’s all very human.
“I don’t think it’s about developing the technology now – it’s about the right data and making it accessible. All of that is moving at an exponential speed. What’s really accelerating AI is that everything is in the cloud. Every single interaction from the employee or customer’s side – all that is feeding our AI platform. The more data we have, the more we are going to be able to power the customer journey.”
In the midst of such dazzling tech capabilities, it can easy to forget any regulation necessities to protect data and ensure AI is used for the good of humanity.
“It’s our responsibility to propose how we want to be regulated. It’s the right time to do the right thing,” adds Olivier
“Over 30 years I’ve been through a few AI ‘winters’, where AI had been at peak hype, but then died. I don’t want this one to die because some people are not responsible, so I will do whatever I can to make sure we are doing the right thing.
“There remains a fragility to this whole sphere, caused by the actions of Cambridge Analytica for example, which rocked people’s confidence in AI and data use, but I believe what we are developing at Genesys is promising, and beautiful, in a way that will not kill the hype this time around.”
Have you been the victim of chatbot incompetence recently?
It typically starts with a specific query that you need help with. You don’t have the time to listen to the contact centre’s hold music, so you turn to the company’s much vaunted chatbot.
It seems fairly straightforward. You type in your question and press enter. The chatbot comes back with a list of completely unrelated content links, and asks if any of these solve your problem. ‘No’ you say.
You retype your question, hoping this time it’s a little clearer. Again the chatbot cheerfully responds with a new list of possible ‘helpful’ articles and FAQ links, and mentions that it is busy learning and is grateful for your help. It will get more accurate the more people engage with it.
Oh, so your diabolical customer experience is all for a good cause – to train their chatbot! The cheek of it.
After a third attempt, you notice a link that may be relevant. You click on the link and are taken to a three-page document providing generic product information. The assumption is that you will take the time to read this document and then work out the answer yourself. With raised blood pressure, you click on the ‘Connect me to an agent’ link and hope that possibly they may have the knowledge needed to solve your query. Sometimes they do, sometimes they don’t. That’s how it goes with so many omnichannel customer journeys these days.
I must confess I expected more. I envisaged that by now we could engage with chatbots that are capable of diagnosing my specific issue, and then offering me a relevant response that results in a relevant action. In other words, a chatbot that is not simply an over-hyped digital assistant that can execute basic instructions or offer me links to possible content matches. I had in mind an digital advisor that could operate at the level of an expert – one whose intelligence is defined as much by the relevance of the questions it asks as the answers it finally offers.
If you talk to most AI companies, their chatbots already perform like digital experts. They will refer to their amazing natural language understanding and incredibly intelligent algorithms that are powered by ‘machine learning’, ‘deep learning’, and ‘neural nets’. They will give you the sense that all you need to do is point their technology in the direction of your knowledge base and the digital advisor will magically onboard all your product, policy and procedural expertise. Then, with just a little bit of guidance, you can soon have trained your chatbot into a digital Einstein that can change your customer service offering forever.
When you ask them to show you a working example, they will probably show you one of their canned demo’s – built off a scenario where the source data is in rich supply, the use case is clearly defined, and the user script can be carefully followed. As a result, their chatbot’s conversation will feel so intelligent, so human-like, that you will feel you simply have to have one.
Just don’t ask them mid-way to type in something unscripted and to upset their crafted storyline! I am certain that you will be quickly informed that they have not managed to train this chatbot to cover all contexts, and that this is simply used for illustration purposes.
The real reason is that it is all really a digital mirage. It looks so achievable until you shift your eyes down to your current position, and suddenly the mirage vanishes. There are a number of reasons for this:
Companies seldom have the quality of data needed to accurately train a customer facing chatbot
Most companies operate in a world of legacy systems, limited integrations, poor quality data, and poorly documented internal policies and procedures – the very things that cognitive systems depend on to build their engagement accuracy.
A customer support chatbot is powered more by prescriptive than predictive logic
To understand the difference, ask Siri or Alexa for an answer based on available information, and they can usually give it to you. For example, if you ask: “What is the weather looking like tomorrow in London?”, you will be amazed how accurate the answer is. That is because the information exists, and thousands of people have already asked the same or a similar question. The patterns are thus established and the correct answer can be predicted.
However, try ask a question that requires more context before answering. Say: “What is the best home loan for me?”. You will probably notice that the response will be to offer you possible links to companies offering loans. It won’t begin by understanding your needs. This is because a financial need analysis is driven off a diagnostic set of prescribed logic. There is no answer yet – the problem still needs to be understood.
In regulated environments, you need to be able to prove your chatbot asked the right questions and offered the right advice
Where a chatbot is powered by predictive logic – the logic you need to train and that keeps ‘learning’ based on multiple engagements – you will find it will struggle in a regulated environment. This is because the logic is designed to change and adapt, based on user engagement. It is also hard to prove how a decision was reached, as each recommendation is made in what is often referred to as a ‘black box’. This is hugely problematic when you are offering customers advisory support in a regulated environment, such as banking and finance.
Context matters, and the way most companies capture prescriptive logic lacks context
Prescriptive logic is typically captured using documents (knowledge bases) or decision trees (process flows). It’s how we have trained employees brains for decades and it’s how we are trying to train our chatbots. So just like giving staff exercises to learn how to apply the formula to different situations, we get teams to train the chatbot, telling them when they are right and when they are wrong. The problem is that documents and decision trees are not able to capture all the possible scenarios. They can only describe a few. And as a result, the more variables you need to consider in order to offer a customer accurate, relevant advice, the harder it becomes to achieve.
The good news is that there are now digital platforms available that allow you to achieve the holy grail – a chatbot capable of asking me context relevant questions that then lead to relevant answers and actions. These platforms have been built off data-powered, prescriptive logic that can ensure your customers are offered a consistent, compliant and context-relevant digital engagement, one that leads to a successful customer service outcome every time.
These platforms have acknowledged that not all logic should be predicted, and that for customer support chatbots the foundation of the logic has to prescribed. The trick is ensuring it is also contextual, and these platforms have now managed to do this in a way that can be maintained effectively.
The dawn of chatbots capable of offering customers consistent, compliant and yet highly context-relevant customer engagements is upon us. And it’s about time, too.
Each year, analysts predict trends that will determine the course of the advertising, media, and digital industry in the near future.
Year after year, we see the same predictions about the importance of video content, new approaches to SEO optimisation, growth of mobile internet penetration, and related advertising tools. However, it seems that a lot is going to change in 2019. So let’s take a closer look at the new revolutionary solutions and approaches that are going to shake the market this year.
1. Personalised marketing
Personalisation is a key trend in many business areas. The idea of delivering a personal message to the client, taking into account the characteristics of his or her behaviour, personality, and sociography is not new. However, such an approach becomes a reality thanks to the introduction of artificial intelligence (AI) technology. Even if a person uses hidemyass, it will be still possible to track his online actions.
The love of marketers for digital is largely due to the possibilities of fine-tuning the targeting for advertising, but now more advanced personality recognition mechanisms are being tested. Thus, Amazon uses AI-based solutions that combine user data from various sources, such as transaction archives, trending sales, competitor information, CRM data, and information from social accounts. At the latter point, the machine predicts the desires and capabilities of the user. As a result, a company is able to formulate and prepare a 100 percent personalised offer, which will hardly be refused.
2. Voice services
There are some technologies that burst into our lives suddenly. Voice assistants are one of them. At first, users limited themselves to comic dialogues with smartphones; with time, they began using voice assistants for their intended purpose. Siri, Google Now, Alice, Amazon Alexa, Cortana, and others teach users to use the voice dialogues with the software. Markets are saturated with Voice Search Tools, Amazon Echo, Google Home, and others.
According to NPD Group, by the end of 2019, sales of ‘smart speakers’ will grow by 50 percent, and the market volume will reach $2.7 billion. This technology is in the trend of marketing integration with services and applications for delivering food, calling a cab, searching for the right locations, and other things. Just like vpn services were popular a few years ago, voice assistants are on the peak now.
3. Communication automation & chatbots
According to Gartner, 85 percent of user interactions with companies will occur without human participation by 2020. Nowadays, many companies use chatbots in social networks and instant messengers to simply communicate with their audience. In the future, scripts will become more complex, and the bot will be able to imitate a live seller or manager, saving companies’ resources.
4. Augmented reality (AR)
According to the estimates of the Harvard Business Review, global investments in the development of the AR sector will exceed $ 60 billion by 2020. The research centre MarketsandMarkets states that market growth will exceed 75 percent over the next five years. In 2022, it can reach an estimate of $120 billion.
The largest technology brands have seized upon this promising technology because it is extremely interesting to the end user and does not force it to acquire new products. Everything works on your favourite smartphone. AR is used in education, medicine, and, of course, marketing solutions, especially in a retail segment. The investment volumes are impressive, and we will see a lot of interesting consumer variations using augmented reality in the coming year.
Standards for deploying fifth-generation mobile networks are still in development, but individual elements are being tested by operators around the world. 5G networks will create new opportunities for users, such as the Internet of Things (IoT), as well as broadband media services and real-time communication in areas of natural disasters or mass events.
According to many experts, we are now entering the era of digital technology, which will mostly depend on the introduction and development of artificial intelligence (machine learning) and all the consequences associated with it. The incredible development of the digital environment over the past ten years (social media, improved search technologies, the AppStore, and PlayMarket, cybersecurity, streaming video, etc) will not slow down, but go to a new level.
In 2019, marketers will need to prepare for constant experimentation with new technologies. Only a continuous stream of testing new ideas will allow you to be on the success wave.
Ask the leadership of any reasonably-sized company what technology they’re looking to implement and they’ll almost invariably mention artificial intelligence (AI).
In theory, that’s great, because AI has the potential to fundamentally change the way a businesses operates and creates a great Customer Experience. The longer the business uses an AI application, the better the experience should get. Given enough time, the system can collect enough data on each individual customer to provide meaningful, hyper-personalised experiences.
Implemented badly, however, AI can be a total disaster. Rather than feeling like the business they’re dealing with cares about them, they’re left with the impression that customer service has been handed over to a bunch of dimwitted machines.
Let’s talk about chatbots
The easiest way to illustrate how varied the AI experience can be is to look at chatbots. They’re the kind of front-facing AI that more companies are using and which an increasingly large body of customers are familiar with. Trouble is, most companies are terrible at implementing chatbots.
Apart from a few forward-thinking exceptions, companies tend to put a chatbot on their website in the hope that that it will learn from each interaction it has with a customer and that its answers will become more nuanced over time. They also operate in the belief that customers will tell the chatbot when it’s wrong, helping to train it further (hands up if you’ve ever done this willingly).
That would be great…if the chatbot was actually equipped to do so. However, for the most part, chatbots are simply going through the company’s existing knowledge bases and serving you with a document (or, in the worst cases, multiple documents) to try and help. It’s essentially a slightly smarter form of search.
As anyone who’s tried to use the search function on a corporate website will tell you – that’s not particularly helpful, especially when you’ve got a specific query. Let’s say that I want to know if I can insure my sunglasses. I don’t want to have to scour through insurance agency documents to try and figure out the answer. I just want the answer.
Contextual, hyper-personalised, relevant
As long as chatbots rely on a flawed architecture that depends on the existence of relevant documents containing the needed information, they won’t be able to provide that answer.
If you’re going to use AI to improve CX, you need to take a different approach. If you want to operate in the digital era and want to drive logic through data then you need to start it in data. That means looking beyond your existing documentation and CX architecture and integrating insight into customer behaviour across digital and offline channels.
This approach will, ultimately, allow you to offer customer support that is hyper-personalised, relevant, and compliant.
A chatbot built on this kind of framework understands what you’re asking and can answer specific questions according to what you actually need. While that’s just one small part of CX, anyone who’s cursed a company for failing to provide useful information, will know how important it is.
The aim of AI
That said, this approach shouldn’t be limited to chatbots. Consistency – in style, tone, and content – is one of the most important factors in successful CX.
It’s therefore imperative that any organisation turning to AI to improve CX apply a data-first architecture across every customer-facing channel. So, whether I make a query using a chatbot, the search function on a website, or a call centre, I should get the same – relevant – answer.
However, if this is going to happen, businesses need to stop trying to bolt AI onto their existing architectures and take an approach that allows it to reach its full potential.