Whatever your industry, you have a chance to enter the chatbot era.
And make part of the tasks that you and your team have fully automated by creating an effective chatbot.
Technology research agency Gartner Inc says that by 2020,
85% of transactions within companies will take place through AI technologies, including chatbots, from customer contact using AI.
It also anticipates a trend towards developing more specialized chatbots as solutions tailored to specific workplaces and tasks within companies such as people and make them an essential part of the corporate structure.
We are already seeing this consolidation in customer service, sales and marketing through to logistics and inventory management, as well as proliferation in areas such as cybersecurity and financial management.
The effectiveness of a chatbot is that it can double productivity and automate routine work for customers, users, and management alike.
But the biggest challenge facing chatbots today is data entry.
How can data be used that enables the bot to deal with all questions and requests directed to it?
The following will provide you with the following to avoid (or address) the common pitfalls you face in spreading Chatbot technologies.
Creating a chatbot needs contextual data:
Companies want to create a chatbot that addresses the largest possible number of questions and scenarios as quickly and accurately as possible from the first day.
However, AI is an educational technology, not a gear in a machine that once installed it will work!
Thus the better the chatbot interacts with other applications, the more it can
The faster he learns and collects data, the more prepared he will be when customers ask him various questions and thus he will be more effective and smarter in responding.
Humans interact with the bot in the same way that they would prefer to chat with a friend, for example,
There are no specific rules or phrases that will be repeated every time they speak to the bot.
There are lots of words with similar meanings, slang phrases and expressions.
Every request is different, even when the answer is the same, but the wording of the question asked to the bot is different.
Where can a bot collect data that has a clear context to help it learn?
Simply, the chatbot learns whenever it communicates with other platforms.
This is what provides him with so much data that he can place them in a context in which to understand each
What is being offered to customers in the future.
It requires institutions to link the AI through which the bot works with the company’s data sources and cloud data from different platforms and provide a broad data base that enables it to predict the answer that the customer is looking for.
Working as an integral part of the overall system architecture and databases within companies, provides a robot with access to data in its clear context that it can understand and create a clear path to solve problems.
Teaching chatbots begins in the testing phase:
Expecting your chatbot to be fully functional from day one puts great pressure on the testing phase.
Integration with several external data sources with a company’s own in the pilot phase can be very costly.
But because of its importance, companies are pursuing a new strategy to do so, which is the integration and linking of chatbots, which is in the process of experimenting with special applications, other companies through a cloud systems integration system called iPaas to speed up the process of learning it from an already stable system.
And it already has databases of dealing with clients.
Ideally, the test should use the same direct data sources as
The chatbot is expected to work on it eventually, but clients don’t want to be exposed to the learning phase and organizations don’t want to risk their business information being compromised in any way.