Companies embrace chat functions to reduce dependency on humans. Person-to-person chat conversations as we know them will become obsolete. Within a decade, chatbots will become capable enough to replace the everyday dude at a customer support desk and will provide with more efficient, personalized responses.
Although the first instance of chatbots can be traced back to 1966, huge amounts of data and sophisticated artificial intelligence competencies have mainstream commercial chatbots a reality.
Chatbots will complement your live staff. The agents will have a change in the job description from customer support agent to intelligent problem solvers.
That is bots will take over “micro-moment exchanges” to relieve agents from doing repetitive tasks like greeting back the customer and asking his name, phone, id, etc. With time and more data, the bots will become smarter and build competence to provide assistance like human agents could.
Natural Language Processing (NLP)
NLP capability helps automated bots understand human messages more easily by mobile app and lets customer service agents and bots work within the same customer conversation.
A business messaging platform evaluates which customer messages can a bot answer. Otherwise, the message is passed over to a human agent in the same chat interface.
Build on existing data
Build chatbot app on the stream of customer data as artificial intelligence systems and chatbots they power are only as smart as the data they are built upon. Such chatbots tend to have more advanced conversation abilities than chatbots built from scratch.
The ability of chatbots is personalization at scale, a world where every company has a chat function built around artificial intelligence to quickly address customer concerns personalized to the data available about that customer.
The key to support is to manage customers’ expectations. The support provider—whether a bot or human—if could replicate a human agent in solving problems and managing the expectations, users don’t seem to care. Don’t worry, nobody is going to talk your chatbot out about the holiday it last took when his printer refuses to work.
Chat is a relaxing way to communicate
People prefer to chat over other means of communication including while seeking support from a business. 56% of users expect their reliance on messaging to increase over the next two years at the cost of the phone and other modes of communication. 65% of millennials and Gen X prefer to chat one-to-one and in groups, a joint survey by Nielsen and Facebook indicates.
Every country has different point of view when it comes to messaging, everybody loves the convenience it provides, except for UAE which finds it economical.
Train your bots
The next generation of chatbots might tap into the power of AI. Via a set of application programming interfaces (APIs) programmers could train deep learning models for image recognition and natural language processing with the help of pre-trained image classifiers, or their own image recognition software.
This would make chatbots read barcodes, or identify photos to better help customers with their needs. Image recognition is the same technology that enables reverse-image search on Google and Bing and make a Google Goggles a reality.
AI didn't go so well with Microsoft's Twitter bot Tay. It became a racist, anti-Semitic, chauvinist, xenophobic hatemonger the very day.
Although the adoption of chatbots in customer support has increased, there are still some technological blockades that must be overcome before the technology takes over customer support and replaces humans.
The very thing that makes AI possibility—data is also its biggest problem. Data is never in short supply. However, bad data inputs would lead to notorious bots like Tay. Tay, Microsoft's notorious xenophobic chatbot was trained by the vast pool of Tweets, a majority of them were spitting hate.
Unsurprisingly, this is how Tay turned out to be. The whole Microsoft debacle highlighted the need for niftier systems that can tell the difference between good and bad inputs.
The speed of processing data
A major limitation AI is suffering from is the speed at which existing computing power can process data, which expands time to train a bot. AI has too much potential. NLP, at large, is still at a very nascent stage.
To make it faster, we need to be faster with mining data. Algorithms are powerful but computing devices to process them come short.
1. Don't over extend your chatbots
As long as it works and is inconspicuous, humans prefer software to human. The key is to assure that whatever way your patrons need to communicate and whichever channel they want to employ, be there for them. Using the newest tech is futile if your customers have to go extra to get what they need.
2. Be appealing
Odds are you've already cultured an online following, and individuals will reply as long as you arrest their attention. Be fun yet subtle when building your online presence. Measure customers’ point of interest, and be sure to reply when they extend to get the ‘talk’ going.
3. Avatars add emoticons
Customers hate chatting with irrational robots, including chatbots. Assigning an avatar to your chatbot gives it a human appearance and impression which makes patrons conversing with them more empathetic. They show more tolerance when the incorrect answer is sent, and less expected to get upset and call in for a human agent.
4. Always give your customers an out
There's nothing more annoying than when you want to talk to a human for customer support and yet made to deal with a chatbot. A button that a buyer can press and start conversation with a human agent in the chatbot text window.
In the end, execution matter
If integrated acceptably, chatbots help business, customer satisfaction, and reduce human agent's workload.
If integrated poorly, a chatbot can lead you to a PR disaster–remember Microsoft’s Tay. Chatbots and AI are becoming better and more sophisticated, integrating your own chatbot today will make sure your business is at the forefront of innovative technology.