The rise of automation in customer service has become more prominent within many industries. But along with the positives and time saving tasks it can provide, it can also prove to be a deterrent for its users.

How many times have you needed to contact a company and struggled to find the correct department or even just a central phone number? Did you try to go though a chatbot to quickly find an answer to your question only to be directed to the FAQs, articles, or a customer forum? If you’ve answered yes, you’ve probably undergone the same frustration that, no doubt, many users before would also have felt.

As a customer service professional, a company that makes it so difficult to access their contact information seems to be an alien idea. We are taught and encouraged to make the customer experience as smooth as possible, yet some brands are struggling with the simplest of things. It is our instinct to want to help when contact is made, whatever channel it comes through. So, why when we are living in a world of instant access to information, do we have such bad customer communication bots?

One of the qualities that the customer service sector prides themselves on is communication. Complicated barriers, such as chat bots that do not work, or badly designed websites, make this skill redundant. How will a customer believe that you will communicate effectively and solve their query or issue if they cannot find a way to talk to you? Unnecessary frustration by systems which do not answer the customer’s queries can cost a company dearly.

Loyalty takes a while to earn and can be lost in a heartbeat especially as it is now easier than ever to share an unsatisfactory experience with our many channels of social media. Let us remember that accessing reviews can prove to be the downfall or success of a brand. If you read a scathing assessment, you are more likely to steer clear, but if you read mostly goods things, you’ll give them a try.

Personally, I find the automation side of the customer experience to be not quite fit for purpose. I have experienced both good and bad in bigger and lesser-known companies. For me, it does not have the personal, individual touch that a human has, and it does not solve issues with the same accuracy, empathy, and resourcefulness that a trained member of a business can. Perhaps I am biased being directly in the customer service sector but, when I am a customer myself, I expect to have my issues resolved in a timely manner through a team that I have easily accessed.

Automation and chat bots work best when they can provide customers with their desired information, but I see so many examples where this is not the case. In identifying the areas now in which there are some difficulties, the customer experience can be updated for companies where customer communication is key to their success.

About the Author

Tabitha Langley is a technical customer service specialist working in the security industry. She has a background in manufacturing environments within supply chain, operations and technical support. Tabitha can be found on LinkedIn.

The best chatbot initiatives start with good planning. Magnus Geverts at Calabrio shares his top tips for an automated assistant strategy for improved employee and customer satisfaction.

When combined with the latest Workforce Engagement Management (WEM) solutions, chatbots have the power to improve workforce flexibility, employee satisfaction and the customer experience all in one go. Reports indicate their influence is set to continue especially among the younger generation. Most recently, Calabrio surveyed over 250 contact centre agents and discovered that more agents aged 30-44 years think chatbots will have a greater impact on their job than they did in 2017 (22% versus 10% respectively). They are also more likely to believe chatbots will have a greater degree of impact than agents aged 45-49 (10%).

However, like all technological advances, effective chatbot implementations depend on a thoughtful approach that blends the needs of the organisation with those of the customer.

Step-by-step guide to scaling chatbots successfully

Here is a six-point plan to getting started:

1. Outline clear roles and responsibilities – as chatbots rise up the digitalisation agenda, it is important to establish a dedicated team of experts.  Gartner has identified “three pillars of chatbot responsibilities: business domain, conversation management and technical implementation.” Focus on each of these areas to collectively “drive effective business oversight and decision-making, optimise interactions and customer value, and enforce application integration and data management best practices.”  

2. Involve the right people from the beginning – who are the people who will make or break the chatbot project?  They might be those responsible for deploying the technology or the leaders of the customer service department.  More often than not, they are the budget holders. Next, set realistic expectations and measurable goals – it’s crucial to define expectations as clearly and tangibly as possible so that everyone understands what constitutes good and bad results. 

3. Establish the common questions customers ask – starting with an FAQ project for customer facing chatbots by taking a look at your website’s FAQs. If they are already written in the customer’s words and prioritise the most common queries, that’s a good sign. It shows that the contact centre has analysed customer needs and created well-informed responses. If FAQs are regularly updated, a chatbot begins on solid foundations. To extend the value of automation further and be even more accurate with trend mapping/grouping use a modern speech analytics engine to identify common questions.

4. Translate agent training and evaluations over to chatbots – when coaching agents to interact with customers, a key lesson is to use plain English and remove jargon. The same principle applies to chatbot scripts. To ensure that customers don’t have to wade through technical information and complex phrases, pass these scripts through a readability assessment. A simple, online test that uses a Flesch–Kincaid readability score will do the trick. However, even with well-crafted scripts and innovative solutions, chatbot success cannot be guaranteed. There remains plenty to consider from a people, process and technology perspective. Just as the contact centre uses quality assurance for agents, monitor chatbot performance over time too. Tracking metrics such as customer satisfaction, deflection rates and user numbers can provide valuable insights into whether the pre-defined scripts are landing well with customers. Analytics systems that monitor and predict sentiment, predictive QM scores, goal completion rates and spot trends take this to the next level to further improve the customer experience.

5. Don’t apply chatbots to emotional, complex queries – while customers expect ‘quicker response times’ (93%), they also desire ‘human agent availability over bots’ (68%). It is when bridging the digital and human worlds that chatbots really come into their own. Although technology is evolving rapidly, currently chatbots work best when managing routine and transactional queries. They’re not so good when things get complex and emotive.  One approach is to analyse the top reasons for customer contact and consider which can be resolved with a rigid response and could be dealt with by chatbots. This is often a customer’s preferred method of resolution for simple questions.  For the more complex questions left on the list, create process flows and identify further chatbot features that can deliver the required responses possibly combined with a human agent.

6. Use automated assistants for workforce management and agent wellbeing – automated assistants can do much more than interact with customers. Take a workforce management (WFM) virtual assistant as an example. It informs employees when they can work overtime to earn extra cash or take voluntary time off. Planners also benefit as the bot sorts through absence requests, saving time for them to concentrate on more tricky forecasting and scheduling tasks.

Then, there’s intraday automation. By monitoring service levels, these bots offer   advisors opportunities to change their breaks, helping to better meet incoming demand. The result is increased operational efficiency, empowered employees and improved work-life balance.

To read Calabrio’s latest Health of the Contact Centre 2021 – Agent Wellbeing & the Great Resignation Report and for more ideas and inspiration, visit www.calabrio.com.

About the Author

Magnus Geverts is VP Product Marketing & Management at Calabrio.

Calabrio is the customer experience intelligence company that empowers organisations to enrich human interactions. Through AI-driven analytics, Calabrio uncovers customer behavior and sentiment and derives compelling insights from the contact centre. Organisations choose Calabrio for its ability to understand customer needs and the overall experience it provides, from implementation to ongoing support. Find more at calabrio.com and follow @Calabrio on Twitter.

Poor customer service and perseverance in long waiting loops – what we have begrudgingly accepted as the norm can be changed with the help of modern AI software.

Cognigy, an internationally successful pioneer in customer service automation, offers one of the leading Conversational AI platforms (think voice- and chatbots). Companies such as Bosch, Daimler, Henkel, Lufthansa and, more recently, BioNTech are automating their customer communication in a smart way.

We spoke with Sebastian Glock, Technology Evangelist of Cognigy, who explains why bots have an image problem and how companies can actually save on service costs while making customers happier.

As customers, we always need individual support from companies, whether it’s questions about products, contracts, changes or problems. Chatbots rarely help. Do you see it that way?

Glock: Definitely! 90% of FAQ bots are unfortunately useless – even those on the websites of big brands. As customers, chatbots often make us feel blocked from getting the information we are looking for. The result? We call the service center more annoyed. For most of us, FAQ chatbots do not solve our problems, do not save any costs in the call or contact center and weakens rather than strengthens customer loyalty.

Why are chatbots used in the first place?

Glock: With thoughtful applications and smart implementation, you can improve customer service by being reachable to customers at any time, in many languages, and by solving problems. And you reduce costs. According to an IBM study, companies worldwide spend more than $1.3 trillion to handle 256 billion calls from their customers each year. In other words, each service call costs an average of $30. That is huge! Companies are looking for solutions to deliver the same quality in customer service, only at a lower price. Affordable service is also in the interests of customers. But a chatbot that deflects customers instead of helping them doesn’t do anything better – on the contrary. Many companies are therefore looking for new, more effective solutions. Not only in marketing and sales, but also in service. They want and need to digitize and automate more with meaningful self-service solutions.

What should such a solution look like?

Glock: As customers, there are four hurdles that customer service has to overcome. First, we expect problems to be solved quickly, second competently, third at any time, and fourth possible on all channels. Many simple chatbots are based on rudimentary software that can only provide ready-made answers based on precisely asked questions. FAQs or rules with keywords are stored. They do not provide real added value to information on the website or in the app.

A smart solution is only available through the combination of AI-based speech comprehension and the connection of bots to back-end systems. The smart, connected bot can then access an enormous knowledge of the customer’s products, services or personal information from the customer database in real time and is not limited to a question-and-answer catalog.

The smart bot or virtual assistant also has writing access in the backend, i.e. it can change an address or order quantity, create an appointment, cancel a booking, place a callback, etc. Our platform, Cognigy.AI, is already widely used for this purpose. You could also say that earlier bots could provide some general information, later more individual information, and today the bot can self-sufficiently initiate complex business processes. Just like a real employee in the call or contact center.

How much can bots automate for customer service? Will they eventually replace people in contact centers?

Glock: There will always be a need for people to manage the communication and processes in customer service. Bots are good helpers and they are becoming more and more capable. This greatly increases the effectiveness of customer service and also the customer experience for the user. For one thing, the response and business logic behind the bots has to be dictated, hence the rise of the Conversational Designer. And they are always needed for tricky cases or problems where decisions have to be made. Take complaints, for example: a customer is dissatisfied and wants to express his displeasure. As a rule, a person is better suited, as he can, for example, reduce prices or ship new goods out of goodwill. These are individual decisions that a bot cannot make. However, the bot can accept, process and pass on concerns to the right agent in the contact center outside of business hours, who will take care of them the next morning. Customers then have a good feeling, and the agent can work more effectively.

The support of important B2B customers should also be possible face to face with a personal contact person. However, if the business customer simply wants to change an order, query a delivery date or receive a specific product info, the virtual agent can also help – immediately and around the clock.

Keyword AI: What is technologically behind a smart bot? 

Glock: A smart bot can remember things and conduct dialogues like a human being. The technology behind it is called Conversational AI. At its heart is Natural Language Understanding (NLU), i.e. AI-based “understanding” of human language. Understanding means that inputs do not have to be exact but are recognized in a wide range. An example is an address change: A customer could formulate this differently: “I moved to San Francisco”, “change address”, “I have a new address”. There are dozens of ways to say the same thing. AI can assign all utterances to a customer’s intention without all conceivable formulations being programmed or stored. In the case of the statement Move + San Francisco, the bot immediately “remembers” the city as input. For this, the bot is trained in advance.

Once such foundations have been laid, AI can be quickly scaled to other languages and markets. This is immensely important for our mostly international customers. But AI alone is not enough for this. You also need to be able to easily and clearly manage and customize the bot responses in any language, design structured dialogs, connect back-end systems, and implement bots in channels. This has been complicated up to now and can only be implemented with a lot of IT know-how and programming work. With low-code platforms like ours, employees today are able to build their own virtual service agents, continuously improve them, and scale into other markets and channels. This is an elementary step that has only been made possible by technological leaps of the last two years.

Are there specific industries or companies that can use bots particularly well in customer service?

Glock: Every company with a large volume of dialogue across telecommunications, logistics, travel, banks, insurance companies, healthcare and government – all industries are recognizing the added value. There are dozens of useful applications. For example, let’s say I want to submit an insurance claim report on Sunday at midnight or change or query something in the car on the way to the airport.

There are also cases where providers suddenly have to deal with a particularly large volume of dialogue, peak times when many people need urgent information. For example, in the case of home insurance, this happens when there is severe weather. Currently, many people have questions about the COVID-19 vaccination. Handling these peak times can be easily and efficiently cushioned with a Conversational AI platform. A great example is BioNTech, which has implemented virtual agents in multiple languages to meet the current flood of requests from interested parties, customers, doctors, pharmacists, media representatives and suppliers through all channels.

Or take institutions such as the Employment Agency, which engages thousands of people in telephone dialogue every day – on very well-workable topics such as requests, changes and general information needs. Here, bots could greatly increase the quality of customer service. Customers would no longer have to wait long in queues or wait for the clerk to call back, but could resolve their concerns directly with the bot.

How do virtual agents and human agents work hand in hand?

Glock: Often the bot is upstream in order to pass on the right agent directly. Or it acts completely in the background: it listens to a conversation and provides the person in customer service, for example, data on customer history or details about the product specification that is currently being discussed. It makes people smarter and improves their response quality. The customer receives more qualified answers faster. Such so-called agent-assist solutions are very much in demand.

A better-informed service representative is also valuable for up- and cross-selling. He/she can inform the customer of suitable offers in conversation, if reasonable and appropriate. Forrester’s analysts estimate that brands spent about $8 billion more on customer service employees last year than in the previous year, because customer service has to make increasingly qualified statements about increasingly complex products. Assist solutions will therefore continue to boom, as they also help less deeply trained and thus cheaper employees to gain more knowledge.

Are virtual assistants also in demand internally for corporations?

Glock: Clearly. There are also large requests volumes internally. We work with many corporate HR department customers who optimize their internal contact center for employees with our bots. Employees have a lot of questions, currently around Coronavirus, benefits and working from home, but also about processes and regulations. This can all be illustrated in a spoken dialogue via a virtual contact person, also via voice bot.

Are interactions with bots via spoken language the future?

Glock: Definitely! The technology has made a huge leap in voice over the last two years. Language interaction is very easy and natural for us. It’s much easier than filling out form fields on your smartphone and the like. You have your hands-free, you don’t have to look at it or click anywhere. It is a useful form of interaction for many business cases. But the context has to fit.

Such customer service bots must not be lumped together with assistants such as Alexa, Siri or Google Home. They make voice an operating interface, acceptable in broad sections of the population. But the applications are different. An airline’s chatbot can answer everything about air travel, rebook tickets and accept food preferences. Does he need to know the weather at the resort? No! You wouldn’t expect that from a contact center employee.

One last tip?

Glock: Digitization and automation are also changing customer service decisively. Companies, especially those with a large volume of dialogue, should now address the innovative solutions on the market and gain experience in the use of smart chat and voice bots. With a scalable, intuitive platform as a basis, they can start with manageable projects, build up internal expertise and then extend the solution globally in dozens of languages, to other applications and additional channels. Today there are excellent platforms and you need to determine which one is the best fit for your organization.

Thank you for the conversation!

About Cognigy

Cognigy is a global leader in Conversational AI to support customer service automation. Its low-code platform, Cognigy.AI, enables enterprises to automate contact centers for customer and employee communications using intelligent voice- and chatbots. With precise, reliable intent recognition, human-like dialogs and seamless integration into backend systems, Cognigy.AI creates superior user experiences and helps companies reduce support costs. Cognigy.AI is available in SaaS and on-premise environments and supports conversations in any language and on any channel including phone, webchat, SMS and mobile apps. Cognigy’s worldwide client portfolio includes Daimler, Bosch, Henkel, Lufthansa, Salzburg AG and many more. Learn more at cognigy.com.

Sometimes, it’s easy to forget how much the world of business has evolved, even in areas we don’t often think of as having improved. For example, the next time you’re waiting to hear back from a customer service rep, recall the days before the internet, when automated responses were king.

Indeed, in the 20th century, customer service calls usually went something like:

“Thank you for calling Acme corporation. Your call is important to us. For client services, press 1. For sales, press 2…”

This system – which has existed since the 1970s and is (sadly) still in use today – was never meant to provide great customer service. As many suspected, the entire point of having such a laborious and time-consuming phone tree was to achieve call avoidance, thereby saving the company money on customer service agents.

Is it any wonder, then, that so many big companies have such awful customer service reputations?

New technology and innovative solutions have allowed customer service departments across industries to improve dramatically. Yet when it comes to actually providing top-quality customer support, many companies are unable (or unwilling) to distinguish between call avoidance and effective practices. The metrics for success have been muddled for so long – after all, it’s tough to tell the difference between a customer who gives up in frustration, and one who’s solved the problem on their own.

When it comes to customer service in 2020, companies must implement the best methods for their product or service. Identifying the right strategy can take time, yet it’s essential to success. Here are a few key ways to decide where and when to make those needed changes.

1. Know Your Customer Profile

Too often, startups and other emerging companies are content to add a chatbot to their home page and call it their customer service department. Even when there’s a vague plan for an expanded department later on, or a troubleshooting email address buried somewhere in the ‘About’ page, companies still end up dragging their feet on implementing a real customer service solution.

This can be the difference between a company’s success and failure. When customers don’t understand, enjoy, or appreciate the product or service, they never become the brand evangelists start-ups need to survive.

One of the more common misconceptions that causes this issue are assumptions about a typical customer’s profile – often, with regards to age. Millennials are a lucrative target market, yet only 37% prefer to interact with chatbots over humans when it comes to customer interaction.

That doesn’t mean newer applications don’t have their place as part of your customer service effort. Even if your target market skews older, having the option to communicate with an AI is still an asset, even if most will opt to speak to a human. At the very least, an omnichannel approach to customer service shows your dedication is serious, whether the communication is via phone, email, face-to-face, or with AI.

2. Be Clear, Not Complicated

There’s a limit to what you can offer with each approach to customer service. If your product is tricky to reboot, for example, you may find that your customer service team has some trouble explaining this over the phone. In this case, you’d want to have pictures and videos readily available online to explain whatever process is causing confusion, as well as making sure your team knows where to direct customers if they get the question.

The same rules apply the other way around. It’s simply not enough to have an FAQ and an email address – at least, not when you’re building your customer base. Every company has budgetary limits, however a good customer service team actually generates revenue by helping to retain customers and grow brand awareness.

Increasing customer retention rates by just 5% raises profits 25%, according to research from Bain & Company. Companies with fantastic, clear, and uncomplicated customer service are the ones who achieve this growth – especially when your competitors are already implementing their own solutions for retention.

3. Never Let A Customer Down

The importance of customer retention simply cannot be overstated. The statistics on this essential corporate rule are often as shocking as they are sobering. For example, did you know it takes the average SaaS company 18 months to recoup the cost of initial customer acquisition?

With numbers like that, every customer is essential. It’s not just a financial need, either – companies that lose sales over customer confusion, miscommunication, or other easily fixable problems simply aren’t living up to their industry potential.

Keeping every customer you can means meeting their needs, whatever they are. This can require some out-of-the-box thinking. Your entire business may function in English, but a customer may still prefer to speak or write in Spanish or another language when they connect with your customer service team.

Keeping track of the vital data during every customer interaction provides the insights you need to thrive. This starts with making sure your customers are able to create a ticket in the first place. By offering a multi-channel approach, you’re creating a level of openness customers now demand.

The days of call avoidance are long over. By embracing openness and encouraging interaction, you can ensure your business thrives.

No ‘dial 0 for assistance’ required.

About the Author

Jay Reeder is founder and CEO of VoiceNation, a live answering company and an award-winning provider of customer service. Jay is a serial entrepreneur with more than 23 years of experience in the telecommunications industry starting his first company in 1994 and has been recognized by PC Magazine, Clutch, and others as an industry leader.

Volumes of business communication are growing constantly. A huge part of this is repetitive and time-consuming, but it is necessary for the enterprise to achieve success. That’s why intelligent automation is a priority for growing businesses.

Business communication market trends are demonstrating sustainable expansion. Business e-mail traffic is increasing by 6% annually and consumer e-mail by 3%. Time-honored e-mail is growing slower than other flavors of communication, but it is definitely far from being considered obsolete or insignificant in the near future.

The mobile messaging market is also growing by 7% year-over-year. Today’s mobile-centric culture has a significant impact on the overall market for business communication with expectations of rapid responses and real-time communication. While being superseded by instant messaging and social media, daily e-mail still steadily grows and will reach more than 300 billion e-mails per day in 2020.

Diversity of communication tools drives companies to develop omni-channel flexibility to stay in touch with customers and employees. To reach people and to deliver responses, the routing of information between channels is needed. Fetching and updating information in corporate information systems like CRM or ERP is often required to complete the business processes. Most of these operations can be automated and are imperative to survive rapid growth.

Nowadays modern companies are looking toward automation of business communication. Surveys, like one conducted by Forbes, conclude that a huge part of that communication is repetitive. According to our analysis for a typical company, there are 15% of requests that are unique cases and cannot be automated, up to 5% of requests are regular cases, but also should be done manually by people, and over 80% of cases can be fully automated.

The traditional approach to automation requires explicit analysis and lots of work to formulate rules and/or train Machine Learning to cover all the cases. Despite being a huge unit of work itself, it also may have data security implications, because your company’s private business communication is shared with the third-party who is implementing the automation solution.

Therefore, 5 years ago we decided at Dynamic AI to fully automate repetitive work in customer care in real-time. As it turned out, it is possible to build an AI automation system that can learn in real time by observing actions made by human operators. Patented Genetic Coding technology allowed us to achieve 95%+ precision, which is more than a human being achieves. The Dynamic AI system has unique precision reasoning module, steered and controlled by the customer care department.

This real-time learning approach keeps the data path free from third-parties, strengthening security, and allows feeding of business communication conversations directly into the system fully under control of the company. By providing integrations with common communication channels, such systems can capture and react to messages side by side with human operators. The AI is setup to constantly observe conversation flows and actions made by human being operators and to determine automation patterns. It starts doing automation when it considers it is process knowledge confident.

Automation can also work very efficiently with insights like sentiment analysis for brand management. You can see the big picture of business communication in the company as well as specific hints on conversations processed by the operators.

Choosing an automation approach, one lowers costs because a huge number of requests gets automated, vacating human operators from routine work and enabling them to concentrate on complex unique cases. As your system learns from human operators, it can grow and evolve with the company by itself, autonomously and without assistance, absorbing new cases and changing behavior to existing ones.

There is no doubt that in the near future more and more business processes will get automated. Taking into account the rapid growth of volumes of business communication, it is considered to be one of the top processes for automation. Advances in Machine Learning and Natural Language Understanding created a background for systems like Dynamic AI to emerge.

Being one of the most important fields of business communication, customer care requires a high degree of accuracy. Every modern company needs to manage its customers as well as having business communications that will benefit from AI process automation solutions. Early deployment of automation for business communication will definitely give your company a competitive advantage.

 About the Author

Florian Erlach is President of Dynamic AI56, which has been one of the leading AI companies in the US for customer care automation of fortune 500 companies since 2014. The Genetic Coding approach  is patented @the USPTO and its founding fathers are Dr. Roman Levchenko & Prof. Ievgen Sliusar.

Dynamic AI’s primary goal is to lower costs spent on employees doing customer care as well as back office tasks in your company. We offer an automation solution having less cost than repetitive human work, but still reflecting your company in the communication and being available 24/7.

Please feel free to see more use cases or contact us.

Utilita, one of Britain’s leading suppliers of Smart Pay As You Go Energy, has recruited a digital ‘workforce’ to relieve their human counterparts of laborious, time-consuming tasks and make the business more efficient.

The company is deploying AI (Artificial Intelligence)-enabled digital workers alongside its operational teams to optimise customer experience, speed up processes and free up staff for high value, rewarding activities.

Within the first 12 months, Utilita has automated 24 processes across the business using Thoughtonomy’s award-winning SaaS-based Intelligent Automation platform. They include switching customers from credit agreements to pre-pay schemes, ordering new pre-payment top-up cards for customers and handling the transfer.

Previously the Contact Centre team carried out the processes manually, checking various data systems, validating information and sending a series of emails to the customer as well as the card processing company detailing the payment request.

Cloud-based digital workers now execute all the required steps 24 hours a day, seven days a week, reducing the average time it takes to bring customers on board and ensuring they can access critical services at all times.

Welcoming in new customers efficiently is vital in supporting growth in the highly competitive energy marketplace. In February 2019, Utilita ‘onboarded’ 31,000 from failed Scottish provider, Our Power, followed, in September 2019, by another 35,000 from Manchester-based Eversmart. In both instances digital workers were used to support the verification of details and the set-up of new accounts across a range of CRM and billing systems, which was critical in completing the acquisition effectively.

Another benefit of the new strategy is the focus on automating a range of Field Services processes. Digital workers are now scheduling appointments for field engineers to perform installs and meter checks. As a result, the team has eliminated wasted site visits (where commercial customers were not yet connected to Utilita’s energy supply), leading to significantly higher rates of engineer utilisation.

Taking automation to the next level, Utilita is now training AI-enabled digital workers to read, categorise and forward more than 2,000 customer queries each week to the correct customer service team. Groundbreaking use of natural language processing (NLP) will ensure that queries as varied as change of tenancy details through to rescheduling engineer visits can be actioned sooner.  It means customer service teams can dedicate their time to resolving queries rather than being tied up in logging communications for regulatory reporting purposes and attaching documents to workflows.

The long-term Intelligent Automation programme forms a critical part of Utilita’s overall IT Transformation strategy that moves technology from a support service to becoming the central focus of the company’s efforts to create sustainable differentiation within the marketplace.

Ian Burgess, Director of IT at Utilita, said: “We’re already seeing the positive impact that digital workers can deliver to both our customers and our own team, but we’re really only starting to scratch the surface when it comes to the potential benefits of Intelligent Automation.

“Over the next few years, we will look to incorporate Artificial Intelligence, such as Natural Language Understanding and Sentiment Analysis technologies, so that our digital workers are able to fulfil more sophisticated tasks.

“We are putting Intelligent Automation at the very centre of our wider IT transformation programme, making it integral to the design of key initiatives such as enabling and promoting customer self-service through mobile apps and online platforms. This was one of the most critical factors in our decision to work with Thoughtonomy. Their cloud-based platform provides us with the flexibility and functionality we need as we embark on this exciting journey.”

The automation team is now setting its sights on more complex internal processes in order to help drive benefits to staff directly. For example, routine employment reference requests no longer take up time for the HR team. The next step will be introducing a new automated ‘onboarding’ process in which virtual workers will set up email addresses, payment details and access to systems for new employees, allowing the HR team to focus on the human aspects of welcoming new colleagues into the business.

Terry Walby, CEO of Thoughtonomy

Terry Walby, CEO of Thoughtonomy, said: “Technology innovation is absolutely essential to differentiation and growth in a fiercely competitive energy market.

“Utilita has recognised the potential for Intelligent Automation to deliver on digital transformation goals, both now and in the future. They are already reaping the benefits in terms of business efficiency but, as they scale up their automation programme and incorporate AI, they are now perfectly placed to drive business performance and agility.

“This is when Intelligent Automation really starts to become a game-changer, opening up new revenue streams and enabling organisations to pursue new opportunities which simply would not be feasible or affordable with a traditional resourcing model.”

Thoughtonomy enables organisations to enhance the productivity of their workforce through the intelligent automation and digitization of knowledge work. It uses AI and robotic process automation software to emulate how people work, allowing healthcare providers to add flexible resources to their team without disruption and delivering rapid ROI.

 About Utilita

Utilita is one of Britain’s leading suppliers of Smart Pay As You Go Energy. The company was established in 2003 with a vision of helping those households who were being badly served and overcharged by the Big Six – primarily the prepay market. Using its smart meter technology, Utilita puts customers in control of their energy usage and spend and estimates it has saved Britain’s hard-pressed households more than £500m since 2010.

The company serves approximately 800,000 customers, managing around 1.3million meters. It employs 1,400 people.

About Thoughtonomy (Blue Prism Cloud)

Thoughtonomy, a Blue Prism company, delivers an artificial intelligence (AI) driven intelligent automation platform that enables organizations and the people they employ to do more and achieve more. A leading provider of intelligent, cloud-based automation, the company’s award-winning Software as a Service (SaaS) platform gives companies access to a pool of cloud based intelligent digital workers that can perform the repetitive, time-intensive tasks that slow people down. By integrating this digital workforce with their human teams, companies can accelerate growth and achieve a step change in efficiency.  More than 200 customers use the platform in 29 countries spread across four continents. In 2019,  Blue Prism acquired Thoughtonomy, to add the SaaS offering into its wider connected-RPA portfolio and will be renaming the platform as Blue Prism Cloud from January 2020. Visit www.thoughtonomy.com to learn more.

WhatsApp, Facebook Messenger, and other social chat applications have opened a brand-new avenue to get in touch with customers. And customers love to use them because they don’t have to change apps; they get customer service right in the apps they use the most.

If given a choice between searching for answers and talking to an agent to get answers, customers will always choose the least-effort path. This is because these platforms provide instant gratification which doesn’t take long for users to get addicted to.

Given the number of users using these platforms, they cover markets for almost every industry. It is clear that the stakes are high. However, it is also clear that companies are not doing enough to handle the massive influx of customer feedback.

According to a report, American companies lost about $1.6 Trillion USD to customers switching due to poor customer service.

So, how do companies handle the volume of complaints across multiple channels without wasting resources or losing efficiency?

The solution involves automation that takes advantage of the existing customer service framework and delivers intelligence to agents allowing them to improve their service levels at scale.

How Artificial Intelligence is contributing to Customer Service

When thinking of AI, chatbots are the most natural application that everyone can recall rather fondly. But modern AI has gone far beyond plain rule-based chatbots.

Leveraging AI in marketing helps the team understand customer feedback better and gather crucial insights to enhance the quality of their marketing efforts. Several AI applications in customer service help reduce the time to respond to each query and also improve the overall customer experience through the process of resolution.

In other words, there is a lot more to AI in customer service than just chatbots.

1. AI-based Shopping Systems

AI has overhauled the way companies sell and customers shop. Artificial intelligence in eCommerce has opened up endless avenues of possibilities for engaging customers.

Product recommendations in e-commerce were once based on categories similar to what the user bought. Now, an AI-based system can recommend products based on the customer’s order history, similar products, and the buying habits of other customers of the product.

Amazon, Netflix, and Spotify are already recommending your favorites based on your own preferences as well as preferences of those who have similar viewing habits as you.

A tool called Shelf AI combines voice search bots like Amazon Alexa and Google Home with AI and Machine Learning (ML) to deliver superior shopping experiences. It learns each individual shopper’s behavior through order data and uses contextual knowledge and semantic precision to deliver better suggestions.

Companies like Botgento and Octane AI build interactive Facebook bots to provide a more organized shopping process for sellers such as Shopify store owners. These bots allow customers direct access to the online store from within the Facebook messenger. They can navigate their order list, explore favorites and recommendations, and even manage their wishlists.

2. Automated pre-processing of customer queries

How do you improve the productivity of customer service agents without costly approaches such as increasing their hours or hiring more agents?

The answer is to pre-process the queries across all channels and attach additional contextual information to customer tickets.

A basic ticketing system allows centralized, intuitive access to tickets, reduces the turnaround time and improves the efficiency of the agent workflows.

Modern ticketing tools also add analytics and reporting capability which gives a big picture of the types of tickets, nature of tickets, sources of tickets, and so on. It gives the customer service team a high-level overview of problems faced by customers. They can then work on creating training content for teams to solve such repetitive queries quickly and decisively.

With AI in the mix, the system can automatically identify the low-priority and low-effort tickets. They can send automated content or knowledge-base articles as part of their content marketing strategy to the customer adding a self-service capability. Tickets that cannot be solved through self-service can be passed on to agents with additional contextual information for a quick resolution.

Tools like Fusion CX can also enable bot-based resolution for low-effort tasks such as password change or plan upgrade/downgrade.

Modern applications of Machine Learning in customer service

Machine Learning is a subset of AI which takes the automation in customer service tools beyond simplistic rule-based associations. ML capabilities allow the tool to analyze and learn from existing data and identify patterns to draw accurate insights and provide suggestions.

ML services work behind the scenes and empower agents to provide better customer services. Their effect is not as explicitly apparent as AI-based applications mostly because these ML services are subsets of the AI functionality.

Organizations of scale can provide personalized services to users by learning their behavioral patterns. By unlocking the wealth of insights hidden in customer data, ML services allow AI applications to provide more relevant suggestions thus enhancing customer’s delight and improving chances at retention.

3. Fraud detection services for users of financial services

American Express (AmEx) uses ML to analyze millions of transactions to find fraudulent transactions. Their core service compares native user patterns with known patterns of fraudulent transactions and derives insights about the specifics of the fraud. This fraud data is then fed to an AI application as rules which powers fraud detection.

Customers are relieved almost instantly because they have an automated system that can detect fraud on their cards. This scores massive customer loyalty points for AmEx and sets them up as a modern banking services provider that truly cares about its customers.

What’s great about these services is that they are not one-time, data-dependent applications. They can learn from ongoing patterns and adapt their insights as threats evolve.

4. Customized insurance discounts for users 

Progressive, a group of insurance companies, runs a program called Snapshot for its auto insurance customers. The program takes input from a mobile app or a plug-in device to monitor the user’s monitoring habits.

It analyzes the user’s driving habits over a period of 6 months and compares these habits to safe driving protocols. It gives users a driving which it uses to provide discounts based on the user’s safe driving habits. The safer they drive, the higher discounts they get.

5. Navigation suggestions inside a large campus

Disney’s theme parks provide rich, entertaining experiences for the entire family. To help enhance this experience, guests are given wristbands called MagicBand. These bands hold information about payments, tickets, and even act as room keys.

As users navigate through the park premises, the band provides intelligent suggestions on the better route through the park. During summer and on business holidays, the crowd patterns are picked up via various touch points throughout the park. The ML system analyzes the crowd movement patterns and provides insights for crowded rides and routes.

This allows users to save time and avoid inconvenience in heavily crowded areas.

Final Words

Modern customers are proactive, smart shoppers. They don’t want to waste their hard-earned money on brands that don’t provide exceptional services. They read online reviews before buying from a brand for the first time.

Customer experience is quickly becoming a competitive differentiator for all contemporary business. Most of the customer’s purchase journey happens on the brand’s online assets, which are opportunities for the brand to curate these experiences.

AI and ML provide innovative solutions in customer service. AI and ML applications are helping companies compete better in a hypercompetitive market.

Learn how a recent innovation called Conversational Process Automation is truly making it possible to understand customer intent, and do so cost-effectively.

We’re now living in an on-demand economy in which consumers insist on convenience, speed, and personalization. From ordering dinner with the push of a button to listening to a song by merely speaking its title, customer expectations are soaring higher and higher – and modern consumers aren’t inclined to wait for the companies they interact with to catch up.

For the customer service industry, this presents a substantial challenge. Not only are expectations of service rising, but demand within the contact center is as well. An increasing number of communication channels available through technology means a rapidly growing incoming ticket volume for customer service departments – one in which the cost of customer interactions is also ballooning.

With all of this increased demand, expectation, and cost falling upon the shoulders of customer service as an outcome of evolving technology, businesses are turning to technology for solutions of their own.

One of the key challenges that this tech will need to face is cost-effectively determining what the customer is hoping to accomplish when they reach out to the contact center. Whether by email, chat, web form, social media or otherwise, AI is already being adopted by many companies across the industry to support sustainable processes, but a recent innovation called Conversational Process Automation is truly making it possible to understand customer intent, and do so cost-effectively.

The Challenge

In order to serve the customer, the service department must first determine what the customer needs. The process to extract this information, when using traditional methods, is costly and can be inaccurate. It’s not financially feasible for business owners to hire and train enough human agents to answer and address each incoming customer call, so many have turned to other methods to try to remedy their situation.

For example, many companies use phone trees or web forms to narrow the customer’s intent. These types of tools help to reduce the possible scope of the need, but they are not an optimal solution. Many unique customer queries will not fit into a prescribed category, leaving the customer frustrated and the agent no better prepared to assist them.

It’s not only the dialogue where customer service reps need help. Our customers tell us that only 30 percent of customer service interactions in their contact centers involve actual “conversations.” When a consumer reaches out with an issue, chances are they aren’t just looking to chat about it, but for a transaction that provides them with a full solution to their issue.

Solving their issue requires the agent to take some type of action, often in one or more backend systems, which adds even more time and cost to the interaction. Customer service departments need to find a way to intelligently connect the conversations that determine customer need with the backend customer service processes that fulfill that need.

Conversational Process Automation provides that solution.

Unlike traditional customer service tools that work by deflecting a customer’s question, Conversational Process Automation (CPA) leverages machine learning to understand the request. For the most repetitive, time-consuming tickets, CPA can reach a point where it is able to determine the customer’s need, then resolve that need using all of the necessary systems without any agent involvement. This provides such a distinct advantage for both the customer and the agent because it doesn’t just delay a costly interaction, it can phase it out entirely.

With these monotonous tasks taken off of their plates, agents are able to tackle more complex issues with customers. While the CPA is tending to thousands of refund requests (or account lookups, or cancellations, etc.), agents can take more time on calls that require a more personal touch.

CPA is able to entirely take over these transactions because it employs open APIs that can interface with front-end CRM systems, as well as with backend software where ticket resolution actually occurs. Traditionally, customer service reps needed to switch between multiple programs in order to successfully resolve a ticket. They might have initially recorded information in their CRM system, yet when they needed to issue a refund or change a password, they were forced to pivot to another piece of software, and then back again. All of these extra steps can be avoided by streamlining the entire process with AI, with the results rapidly becoming evident in the bottom line.

The benefits of a collaborative relationship between the agent and the practical AI they’re using aren’t merely operational; it is also a great boost for morale. As they’ve taken on more challenging projects with more free time to be proactive, customer service reps are experiencing higher job satisfaction. Agents have self-reported higher employee satisfaction (ESAT) scores when using AI tools, which puts them at a lower risk of burnout, which in turn helps business owners manage turnover, decreasing recruiting and training expenses within the department.

With customer expectations and ticket volume on the rise, companies can’t afford to waste precious resources trying to extract customer intent. Businesses will increasingly turn to AI tools that utilize CPA to resolve the most repetitive and costly types of tickets they experience end-to-end.

As customer service becomes more accurate and timely, customer expectations of service will continue to rise, requiring companies to further step up their customer service operations. AI that gets smarter and learns over time is a crucial and necessary tool that facilitates their ability to do just that.

About the Author

Mikhail Naumov is the Co-founder & President of DigitalGenius, a venture-backed artificial intelligence company, transforming the customer service industry. In his role, Mikhail is focused on bringing practical applications of deep learning and artificial intelligence to customer service operations of growing companies and well-established enterprises.

As a frequent speaker on the topics of emerging technology, artificial intelligence & entrepreneurship he is a leading voice in the Human+AI movement, which focuses on the seamless interaction of human & machine intelligence in business applications and everyday life. Author of Amazon-Bestseller, “AI Is My Friend: A Practical Guide for Contact Centers” Recognized as Forbes’ 30 Under 30 for Enterprise Technology, Mikhail is passionate about bringing emerging technologies to life, to make business and everyday life more productive and enjoyable.