Chatbot, chatbot, and one more time – chatbot! Everybody talks about them. Chatbots use natural language recognition capabilities to discern the intent of what a user is saying so that it can respond to inquiries and requests.
IBM has recently estimated that customer service chatbots will help businesses all over the world save $8 billion per year by 2022. According to Gartner, by 2018, six billion connected things will be requesting support. It is taking on a growing number of roles, from answering questions on a smartphone to providing customer support for big business.
There are a few kinds of stuff you have to consider before you choose how to create a chatbot. Let’s take a look at four criterions:
You should know who uses your services or products or and visits your website. Analyze the statistics and reports to understand who the bot should mostly be conversing with.
Define what you need your bot to do. You should know: the time and expense of the development rely upon this a lot. You should have as much as the functional requirements to build it well.
With a complete or at least partial understanding of your present goal in chatbot development, it is possible to evaluate the project budget to ensure your business is prepared for some spending. We encourage you to turn to a development company for the most precise estimation.
There are a lot of approaches to bring a chatbot to its digital life.
We will furnish you with a review of each way to accomplish your goal below.
There is a limited set of predefined “phrases” by pressing the appropriate phrase-button. It does not care about what the users type. There is a tool that allows creating such bot and designing the dialogue flows in visualization.
Rule-based chatbots respond to simple text processing tools and it can be run quickly without any knowledge and skill of modern NLU science. Unfortunately, because the project expands with additional functions and a broader vocabulary, a bot becomes too complicated to maintain and develop.
ChatScript is one of the platforms to build up your bot.
There are some advantages and disadvantages when you choose this simplest way to create chatbot:
- You can find it in only one central location;
- There’s a decent chance you can see a best-matched chatbot for your retail business;
- You don’t need to be time-consuming and money on chatbot development.
- It’s hard to look for a chatbot that will meet 100 percent of your business demands;
- You can’t customize ready-made chatbots.
Then, we move on more complex way to create a bot.
Chatbots based on NLU-as-a-Service
At First, We Need to Know What NLU Is?
Natural language understanding (NLU) is a small branch of artificial intelligence (AI). It uses computer software to understand input made in the form of sentences in text or speech structure.
NLU enables computers to understand the text without the formalized syntax of computer languages and for computers to communicate back to humans in their own languages.
So, What Are Chatbots Based on NLU-As-A-Service?
It’s different from a rule-based bot, this service can recognize the intent even if the real user’s chat message is unlike what the developers have provisioned.
First, you should define a model which contains a set of intents the users may send while chatting with a bot and some sample statement the user may use for that. Next, you let the service learns on this model. Then, the NLU-as-a-Service will be able to analyze the actual user utterance in real time and tell you to which intent it most corresponds.
NLUaaS gives good results with interpreting user object conversational queries, but that we may still beot enough. If you need a universal tool for entities recognition, support for complicated circumstances, ability to nest conversations, etc., you will want to go with…
Let’s Find out Some Platforms with Its Pros and Cons
- Pros: Free; a good user interface; rich context handling; entity search strategies.
- Cons: Text-only entities
- Pros: Concise code; good documentation.
- Cons: Fixed-length entity children list; manual utterance training; never-ending review procedure that it doesn’t have an inability to test on Facebook; bad user interface.
Chatbots with Custom NLU Stack
If you need more adaptability in the way your NLU operates, you have to build your own stack from the open-source, available, or purchased components. Or, you can cut the intent classification altogether and build your bot logic directly around Name Entity Recognition process using.
Build Your NLU from Scratch
This is the most sophisticated and expensive option to create a bot. If you need to take the character over the head or the problem you are solving is very specific, you will need to the experts in the NLU field. They will use the performances of modern Data Science to create a custom, low-level solution for your problem.
At a glance, you can find a lot of pros and cons when you build your NLU from scratch:
- Software development teams are professionals, and can execute complex functionalities;
- Good developers use advanced technologies and development methods;
- Developers can build you a custom chatbot with a great UX and help you analyze your business demands to find the right answer;
- Companies offer regular support and constant revisions.
- It takes more time to build a bot from scratch than it does to create it with self-service platforms;
- You need your own server infrastructure (hosting).
As you can see, when you create the bot, there is a range of options from the simplest to the most sophisticated. Industry analysts say chatbots could be generating revenues of several hundred million dollars a year by the time we get to 2022. Are you ready to build your owned chatbot?
Related post: Chatbot Changes the Hospitality Industry Nowadays