Article | July 1, 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 14, 2020
You just got a new drone and you want it to be super smart! Maybe it should detect whether workers are properly wearing their helmets or how big the cracks on a factory rooftop are. In this blog post, we’ll look at the basic methods of object detection (Exhaustive Search, R-CNN, Fast R-CNN and Faster R-CNN) and try to understand the technical details of each model. The best part? We’ll do all of this without any formula, allowing readers with all levels of experience to follow along! Finally, we will follow this post with a second one, where we will take a deeper dive into Single Shot Detector (SSD) networks and see how this can be deployed… on a drone.
Article | January 4, 2021
2020 has been an unprecedented year where we have seen more downs than ups. COVID-19 has impacted every aspect of our lives. But when it comes to digitisation and Artificial Intelligence, we have seen some impactful developments and achievements. As we approach the end of 2020, it is worth to look back at these AI stories to highlight the truths and discuss what it means for AI future direction.
The Great Truth:
Artificial intelligence played a crucial role in the detection and fight against COVID-19.
Indeed, we have seen the emergence of the use of AI at hospitals to evaluate chest CT scans. With the use of deep learning and image recognition, COVID patients were diagnosed thus enabling the medical team to follow the necessary protocols. Another application was the triage of COVID-19. Once a patient has been diagnosed with COVID, AI has been used to predict the likely severity of the illness so the medical staff can prioritize resources and treatments.
COVID has highlighted the need to deploy intelligent autonomous agents. As a result, we have seen both robots used at hospitals to diagnose COVID-19 patients and drones deployed to monitor if the public is adhering to social distancing rules.
Another major AI contribution in the fight against COVID-19 is in the area of vaccine and drug discovery. Moderna’s vaccine that has been approved by US Food and Drugs Administration has used machine learning to optimise mRNA sequencing.
The above is a proof that AI can make great contribution to mankind if it is used for “good”.
The Glowing Truths:
Some impressive AI results have been achieved. However, to leap forward a holistic and sustainable approach is needed.
2020 has seen some great AI achievements and leaps forward. The first example is Deepmind’s AlphaFold. The model scored highest at the Critical Assessment of Structure Prediction competition. The algorithm takes genetic information as inputs and outputs a three-dimensional structure. The model has impressively addressed a 50-year-old challenge of figuring out want shapes proteins fold into known as the “protein folding problem”.
While Deepmind’s AlphaFold is a great achievement, it is noted by some scientists that it is unclear how the model will work with more real-world complex proteins. Thus, more work is needed in this area.
The second example is OpenAI’s GPT3. The model is a very large network composed of 96 layers and 175 billion parameters. The model has shown impressive results for several tasks such as NLP questions & answering and generating code.
However, it is noted that the model does not have any kind of reasoning and does not understand what it is generating. Furthermore, its large size makes it very expensive. It is also unsustainable carbon footprint wise; its training is equivalent to driving a car to the moon and back.
While both AlphaFold and GPT3 models are both impressive achievements, there are some philosophical challenges/ questions that need to be addressed/ answered. The first question is about games/ simulated worlds vs. real world examples. Most often algorithms/models succeed in simulated world but fail in real world as the environment is more complex. How can we close the gap? How can we make the AI models succeed with complex tasks? I guess the first step is to apply AI to a real-world example with varied complexity levels.
The second question is about the structure and the size of AI models. Do models have to be big? Can we come up with a new generation of algorithms/ models that are smaller is size and have more efficient computations? Well to answer this question we have to take a pause on deeplearning and explore new venues.
The Gross Truths:
Ethics and bias remain the main drawbacks of Artificial Intelligence.
Over the last year, we had several prominent examples of AI ethics and bias issues. The first example relates to facial recognition: after several calls against mass surveillance, racial profiling and bias, and in light of Black Lives Matter movement starting in the United States, several tech companies such as Microsoft banned the police from using its facial recognition technology.
The second example relates to the use of an algorithm to predict exam results during COVID-19 period: after accusations and protests that the controversial algorithm was biased against students from poorer backgrounds, the United Kingdom government was forced to ditch the algorithm.
In the absence of regulations and tightened frameworks, ethics and bias will continue to be the main concerns surrounding the use of artificial intelligence.
Looking into the future, AI adoption will continue to accelerate, and we will probably see more breakthroughs achieved by only if we start looking at the subject in a holistic and sustainable view. Focusing models on real world problems and reducing the models carbon footprint will be a major step forward. We need to move away from thinking that “more” is always “more”. Sometimes “more” is “less”.
Article | August 11, 2020
The revolution of the public cloud has irrevocably changed the role of the CISO in the modern enterprise. The cloud is the biggest enabler in a generation and is a massive opportunity for enterprises to start innovating at speed and scale. But only if they bring their security with them on the journey! But the pace of change in the new world can feel unsettling, so it is not uncommon for CISOs and security teams to want to stick to older models and to double down on familiar ways of working.