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 | August 3, 2020
The M&A process is complex and nuanced. Technology compatibility, IT infrastructure longevity and overall IT security are all crucial to a successful end result. Below you’ll find the important milestones of each phase of the M&A journey and key considerations to help ensure a secure, streamlined and timely IT implementation for your organization.Consider IT transformation and determine how technology can help the company remain relevant to customers and more advanced than competitors.
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 | December 8, 2020
This article explores why AI is the highest ROI opportunity.
How revolutionary the technology is as Artificial Intelligence (AI) progresses with each passing day, its implementations are massive and almost every magazine and report has covered it. However, explaining its particular facets and how AI and its subsets of Machine Learning and Deep Learning can transform the way we view life has become imperative.
Both businesses are or will be implementing AI in one way or another, the future of the corporate world. Investing in the right AI will deliver a really high ROI if you know what you’re doing.
Entrepreneurs have nothing to do without their senses and the need for testing. Fortunately, the early stage of AI presents financial investors with a more advanced and profitable strategy and provides valuable strategic perspectives when building a new company.
And small companies now generate reliable data from basic market fluctuations in stock to corporate announcements, and this is just the beginning. It is very difficult to select the important one when transmitting data. What do you intend to invest as a long-term investor? Any finance analysts have worked out how to deal with crucial data over time. At that point, they have stable tools that complement their portfolio of investments.
AI can assess how early-stage start-ups perform for investors and capture the start-up opportunity for success by forecasting sales growth, market size, business expertise over all variables. It will evaluate the details in order to see if the statistics will actually make improvements. This means that investment-worthy startups can afford to start raising funds.
Most investors use AI to make important investment choices. By integrating algorithms, data mining, and language processing, AI can create relationships and models to render proposals based on investor bends. As AI continuously absorbs new data, it will grow as it analyses new information and eventually becomes reliable and far-reaching.
MotherBrain, the Machine Learning system developed by EQT Ventures to classify potential start-ups, applies its algorithm to historical data so as to potentially distinguish investment applicants. The platform uses details such as financial statistics, site rankings, device placement, and social media features that most businesses can physically test and evaluate. Surprisingly, if the invention of Motherbrain has already been accessed as it comes to seed and angel finance for businesses.
Small investors, including angel investors, can also leverage the expertise and services accessible exclusively to historically significant firms. Another real downside for venture capitalists and angel investors is seeking pleasant investment targets before. This is a consistently strong and travel-intensive challenge. However, Machine Learning and Predictive Analytics are starting to shift the strategy.
For other users, there are goods, such as Allegro, an intellectual algorithmic investment focused on AI that is absolutely free from human prejudice. This is a great investing product, but when the market is low on the promise, turn to an equity fund and a debt fund when the market is strong, meaning that the investments are safe.
Indeed, finance managers who are compliant with the implications of the industry are often in a tough position where erroneous data exist or where market flaws occur. These defects may be rumors, financial theft, or innocent relationship slip-ups. Owing to the fact that financial markets are in touch with a constant flow of data, the vulnerability or interruption in the flow appears to be worse than the bad news.
So, what is restricting the implementation of AI in conventional businesses following the pattern of hedge funds? The most important problems arise from massive financial and human capital investment.
Probably the most commonly known inhibitor is the scarcity of available talent pools. As another field, there is a pool of detainees with expertise and experience in the field. The same happens to data scientists and AI practitioners, who usually expect a summary of observations into critical company behavior and goals. Pesa reports that more than 10,000 AI vacancies are available in the United States alone.
Apart from a lack of expertise, investment companies need to respond to the priorities and desires of this unique pool of potential that binds the world of academics, academics, and Ph.D. students. Many of these people do not partake in conventional investing jobs and are motivated by capital and financial security. This talent pool is very common and involves their preference. Instead, investment firms need to build a situation where talent demands are fulfilled, placing a high premium on having a positive impression, taking a shot, and seeking game-changing growth.
When it comes to worrying about money, investors are still trusting in the person behind the idea. This leaves a lot of space for personal inclination and emotional misconduct. Emotional investment is at stake. AI is balancing this out. AI helps investors to rely more easily on research and statistics. We can not empty our current senses, however, we can use AI to circumvent our existence.
The growth estimated for AI is huge and shows that it will rise by billions in the coming years. AI may be much larger, analysts say that this sector would rise 30 percent annually. AI has tremendous potential because it can go from engineering to tech in any field and can affect everything in between.
Applications of AI are massive and it would not be possible to show only a few facets of it. But if you are an entrepreneur and you have a particular interest in getting to know the industry, then you can look at and evaluate it properly. Opportunities are massive, and technology is just going to grow because the world economies have been struck by a pandemic, one area that has exploded is technology.