Article | August 17, 2020
A few years ago, the team at Gartner came up with a useful framework designed to help IT asset managers compare different Software Asset Management tools based on “six critical activities” of SAM. Known as the DINROS framework, it outlined the following activities: Discovery: the act of interrogating TCP/IP networks to identify network-attached physical and virtualized platforms upon which software executes Inventory: the process of capturing platform configuration information and extracting a complete list of all its software. Normalize: the consolidation of discovered inventory datasets to remove duplicated or conflicting information
Article | August 17, 2020
Chatbots have come a long way in the past few years. The improvements in technology have enabled developers to expand on bot capabilities far beyond just functioning as a FAQ. Today, the automation of chatbots can process orders, perform financial transactions, make bookings, and much more. (Check out other intelligent functions here.)
However, as intelligent as bots can be, no chatbot can handle and resolve all your customer queries. It simply cannot answer the infinite number of questions a human may throw at it. The technology is simply not there yet, and it may never truly get there. But perhaps more importantly, brands shouldn’t want a bot to manage every customer query.
A bot working independently of human involvement won’t always deliver the best results for customer or agent. It’s the combination of chatbots and human agents that takes customer service to new heights. What you need is a smart and efficient way of translating your organization’s unique customer service philosophy into appropriate action so that every question is met with an answer in the best way possible – whether that be by bot, human agent, or a blend of both.
To deliver this, you have to pay attention to the who, what, when, and where of customer engagement. You need to know who your highest-value customers are so you can always route them to a human agent, for example. You need to know what they need help with so a simple question can be managed by a bot. And the list goes on.
Here’s why humans need chatbots, and chatbots need humans – and how you can achieve this perfect balance to deliver support that will exceed customer expectations and generate substantial ROI.
Why humans need chatbots
There’s no doubt that supplementing customer-facing roles with automation can yield fantastic results. The launch of McDonald’s self-serve kiosks is a great example of this. By giving customers the option of ordering their meal through a kiosk, or through a cashier, McDonald’s demonstrates the success you can achieve by combining automation with human. Here are just some of the benefits it brought to the customer and employee experience:
1. Automating large portions of simple queries so workers have more time to focus on other, more complex tasks
2. Reducing monotonous, repetitive queries to improve employee experience
3. Catering to customer preferences – choose quick automated service or deeper human engagement
4. Reducing queue times, in turn improving customer experience
5. Lessening the opportunity for human error
6. Generating ROI by reducing staff numbers
These results almost identically mirror the benefits that intelligent chatbots can provide customer service teams. By implementing a bot, a large portion of frontline support can be automatically managed by the bot which:
1. Gives agents more time to handle complex questions
2. Reduces the monotony of answering repetitive questions
3. Allows customer to choose between chatting to a bot or an agent
4. Reduces wait time and queue length (through bot’s ability to handle infinite simultaneous conversations), in turn improving customer satisfaction through quicker resolution
5. Eliminates human error in data entry
6. Generates substantial ROI through lower service costs
See how closely those benefits match?
Recommended reading: Chatbot ROI Calculator
Why chatbots need humans
The relationship between bots and humans isn’t a one-way street. While agents need bots to provide more effective and efficient support, bots need agents to provide the personal, ‘human’ touch that many situations call for. In our latest 2020 Live Chat Benchmark Report, we found that chatbots handle 68.9% of their chats from start to finish – although an impressive stat, it still shows that many queries require an agent’s touch.
Recommended reading – 2020 Live Chat Benchmark Report
There are always going to be situations that call for human assistance: canceling a subscription, reporting a lost or stolen credit card, or registering a serious complaint. Or maybe the topic is sensitive, and your customer would feel more comfortable explaining their situation to an agent. Similarly, some (though increasingly less: stat?) people are still wary or reluctant to communicate with bots and prefer to only speak with a live agent. To cater to these customer preferences, it’s vital that these customers can be routed past or transferred from your chatbot to human agent without effort and without having to repeat themselves.
It’s important to note however, that transferring from bot to agent isn’t always just in the interest of the customer – it can often benefit the customer service team too. This is because not all queries are equal. For example, if a customer reaches out asking about a bank’s opening times, this can be easily managed by a bot. However, when the same customer asks about a loan, this high-value interaction may dictate that – according to your unique customer service view – a human agent takes over immediately to ensure the customer receives the best experience and you close the deal as quickly and effortlessly as possible. If your chatbot can’t do this, turn it off and find a chatbot that can (we can help with that).
How to create the perfect chatbot – human (agent) balance
To begin creating the right balance between chatbot and human, you need a bot that’s widely accessible to today’s digital-first consumers; your bot needs to be where they are, wherever they are. Comm100’s AI Chatbot can serve customers on web, in-app, Facebook, Twitter, WeChat, WhatsApp for Business, and SMS. You also don’t need to build separate chatbots for each channel. Simply select the channels you want your bot to be available on (hint: all of them!) and you’re off.
Although your customers will know they are speaking to a bot (and you should make this clear to them to set expectations), you need a bot that understands natural human language. Comm100’s AI Chatbot harnesses the world’s most advanced NLP engine so that it can understand your customers’ goals and provide the answers they’re looking for. Better still, add a large range of off-the-shelf integrations to this, and the Comm100 bot can begin performing actions on behalf of your customers – from tracking an order and paying a bill, to booking a flight.
By resolving a large portion of your frontline customer service questions, your agents will have more time to focus on higher-value queries and customers that matter most to your bottom line.
Recommending reading: Comm100 Chatbot Resolves 91% of Assigned Live Chats for Tangerine
As we’ve discussed earlier, there will be times when you or a customer would rather connect with an agent than a bot. It’s crucial that your bot offers this flexibility.
Firstly, your bot should be able to give the customer the option to speak to an agent at any time. Eighty-six percent of consumers believe they should always have the option to transfer to a live agent when dealing with a chatbot. You can easily set this option up within the Comm100 AI Chatbot.
Next, you need a bot that can automatically identify the conversations that you want an agent to manage. This requires training your bot on the topics – ‘intents’, in bot lingo – that your customers will bring up. If there are specific intents that are of high value to you, you can tag them so when a customer mentions it, the bot recognizes it and automatically transfers the chat to the appropriate agent or department. The bot can also be trained to notify an agent or escalate the conversation when asked a question it can’t answer or if a visitor is clearly frustrated. As a failsafe, your agents should also be able to monitor bot conversations and take them over in these situations.
Chatbots will never replace whole customer service teams, and nor should they. The ‘human touch’ is still essential to customer support, and we are a long way off until this changes. However, if implemented intelligently, bots can resolve a great portion of customer queries without any human involvement, allowing team sizes to reduce, or remain the same in the face of increased support volume.
Take Tangerine, an Australian telecom company, for example. They experienced rapid growth, which in turn produced a surge in chat requests. By implementing Comm100’s AI Chatbot, up to 91% of assigned live chats were resolved by the bot without any agent involvement. As a result, Tangerine could manage the increase in chat volume without hiring and training more agents. And when high-value customers reached out, their agents were free to provide them with the best experience.
Article | August 17, 2020
In the area of computer intelligence where we have robotics, machine learning, artificial intelligence, etc. There is a new game-changing concept that is so profound that industries are already finding a use for it and it is paying off. This new concept is called Intelligent Automation (IA).
Discussions with heads of global organizations as well as research, and experience of experts show that IA is establishing itself as a future key driver of competitive relevance and enterprise efficiency. This is why IA experts are convinced the concept can provide solutions to several urgent issues in the world right now such as improving our planet, education, and life-saving measures. The impact of IA is becoming more prevalent, and that saw the concept selected by Gartner as the number one tech trend in 2020.
What is IA?
The concept of Intelligent Automation otherwise known as Hyper automation leverages the new-gen software-based automation, which blends technologies and methodologies to implement business processes on automation for knowledge workers. This is achieved through IA imitating the skills used by knowledge workers to execute their work. All these are done to attain a business outcome via purposeful redesigning of automation carried out with little or no human oversight. The end game is cost reduction, which improves process speed, optimization of decision outcomes, improved process resilience, and improved quality and compliance. In the end, businesses and organizations will see an increase in revenues and enhanced employee and customer satisfaction.
Who are the knowledge workers?
Who are the knowledge workers that IA is purposefully designed for? For starters, knowledge workers' main currency is the knowledge they possess. We have examples like pharmacists, designers, programmers, architects, lawyers, physicians, engineers, public accountants, scientists. Any worker that has to “think for a living” is considered a knowledge worker. This type of worker is mainly domiciled in the service industries. A knowledge worker is information-based compared to manual labor that is material-based and mainly domiciled in the manufacturing industries.
Where does IA feature here? We already know the importance of industrial automation to the manufacturing process. We can consider IA the “white collar” version of industrial automation. IA can be used to supplement the job of a knowledge worker such as call center agents, financial controllers, etc.
Let us break down what IA does specifically for a knowledge worker. Imagine IA as a digital worker created to imitate the activities of a knowledge worker to deliver the same outcome as a human would. It mimics all the human business processes, which is a succession of tasks by reproducing the human capabilities of reading, speaking, learning, hearing, seeing, acting, and reacting to produce the same business processes as a knowledge worker.
The synergy between IA and humans
IA creates a synergy by merging the software-based workforce with the human workforce. On the task spectrum, IA shoulders a load of executing tedious, low value, and monotonous tasks like processing and digitizing paper invoices, reconciling data, etc. IA equips a worker with what we can call superhuman abilities like the ability to generate insights from millions of analysed data done in just a few minutes. That is on a human level is virtually impossible to do.
The uniqueness of IA
How is it that a concept so recent that its name was only created in 2017 by IEEE has witnessed a rapid expansion and is expected to have a lasting impact on us? We believe the answer lies in its unique features, which are listed below:
The IA pools together new technologies, most of which are recently developed in the last decade.
The application of several IA functionalities is universal. They are applicable across several business functions like finance, sales, etc., and industries such as retail, banking, among others.
IA programs are scalable. Once developed, scaling can be carried out immediately and infinitely at no added cost.
Its availability is unmatched; IA can deliver 24/7.
IA is economically viable and reliable. It gives the same results based on settings repeatedly at a reasonable cost. In less than a year, the program will normally generate payback on the initial investment.
AI and IA, two sides of the same coin?
Here comes the inevitable question. Is Intelligent Automation (IA) any different from Artificial Intelligence (AI)? Are they not just two sides of the same coin? Well, in the world of computer intelligence, laying down the differences between robotics, AI, IA, among others is a very complicated process.
The line between is so blurry that they can sometimes overlap due to the continuous evolution, emergence, and convergence of these concepts. However, that is not to say there are no areas where there are clear demarcations.
For the purpose of clarity, a few key anchor points are drawn using the analysis of the survey of the opinions of more than 200 IA experts as well as our experience in IA. These are the main anchor points:
AI and IA –Since IA has to do with the automation of knowledge work that is the area where AI and IA interrelate. That means IA comprises all use cases of AI in all industries excluding industries like fundamental research, arts, gaming, or any other that is not information-based.
For robotics –physical robotics utilized in the manufacturing industries are not classified as part of IA. It only covers software-based robots.
Lastly, under workflow, business process management, and cloud; only programs or platforms that exhibit a form of intelligence fall under the class of IA. Programs that have limited capabilities to process end-to-end tasks and offer little insight into business processes are not included.
The unfolding potential of IA
The adoption rate of this phenomenon is already significant to the extent that a recent survey of world business leaders shows that 86% of them believe they must implement IA in the next five years to stay competitive. According to a survey by Gartner, 42% of CEOs have embarked on the digital transformation process already with 56% reporting gains from the application.
Due to the uniqueness of IA, in the next five year, experts believe that it is very likely to reach a sophistication and adoption level that took more than 200 year for industrial automation to achieve. A Deloitte survey already indicated that the adoption rate for IA is more than 50% and the rate is predicted to jump to over 70% in two years. If it continues at this rate, we could see a near-global adoption level achieved in the next five years. Despite being a new concept, IA is progressing very rapidly in terms of capabilities.
Article | August 17, 2020