3 Reasons Why Knowing Intent is Essential for Your Business

sasha uritsky

Jun 18, 2014

What is intent? It is the reason behind the sentences we say. Behind posts and messages, as they appear on social networks. For instance, the intent of the tweet ‘I am going to buy a new car soon, my old car is entirely broken’ is buying a new car. The intent of this one however ‘ Need to buy me a car, got things to do lol’ could be anything from killing time by posting randomly to impressing friends, but not buying a car.

During the time when most customer activities online happened on search engines (e.g. Google) understanding of intent was predominantly the task of these search engines. So when I type ‘typical menu of Chinese restaurant’ and the search engine displays the list of local Chinese restaurants clearly in this case it did not understand my intent.

Nowadays, when an ever growing part of the consumer related activities is happening on social networks the task of understanding the customer intent becomes responsibility of a business.

Here are three reasons why this task is essential:

1. Marketing is personalized. Email blasts are a thing from the past. Today to stay completive your business should be able to target individually. And that means knowing what each of your potential customers needs in real time. The best way to know this is to understand customer intent. The numerous analytical and measurement tools available today exist only because until recently we didn’t know how to capture customer intent properly.

2. Knowing intent allows efficient and timely service across your company’s departments: those interested in the product belong to marketing department, purchase intent goes to sales, unhappy customers go to customer service, and so on.

3. Knowing intent offers long-term sustainability to your business because it reduces the noise. Unlike the previous generations, when the problem was a lack of information, today’s problem is the abundance of information. Business can function efficiently and be sustainable only when a competent model of finding the right information is in place. Understanding of intent is the best model available

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MAKING AI MAINSTREAM

We are experiencing a strong demand for conversational AI solutions. It is coming from every corner of the B2C market. It is growing by the day.

Conversational AI is becoming increasingly popular among the consumer facing business community. It is easy to see why - AI offers sales and customer service scalability and therefore is critical for the long-term success of a business.

Conversational AI solutions such as chatbots, voice bots, and virtual assistants provide much needed speed and efficiency, in an age where the rapid advancement of technology makes them virtually the only sustainable customer service solution.

Bu there is a catch - AI is complicated. Mainstream businesses do not have in house AI expertise. And it is not part of their business model to develop such expertise.

Today’s market offer several good conversational AI solutions, such as IBM Watson or Google DialogFlow. However, getting a business value out of them requires the very AI expertise that mainstream companies do not possess.

So what can be done?

Any AI solution should follow these three steps in order for the mainstream business community to fully benefit from it:

Conversational AI should come as a service,
The service should be available in natural language,
The service should be fully personalized.

In the next several posts we will explore how the AI industry, including nmodes, is moving towards achieving these goals.

What Is AI Engine and Do I Need It?

Chatbots and assistant programs designed to support conversations with human users rely on natural language processing (NLP). This is a field of scientific research that aims at making computers understand the meaning of sentences in natural language. The algorithms developed by NLP researchers helped power first generation of virtual assistants such as Siri or Cortana. Now the same algorithms are made available to the developer community to help companies build their own specialized virtual assistants. Industry products that offer NLP capabilities based on these algorithms are often called AI engines.

The most powerful and advanced AI engines currently available on the market are (in no particular order): IBM Watson, Google DialogFlow, Microsoft LUIS, Amazon Lex.

All these engines use intents and entities as primary pnguistic identifies to convey the meaning of incoming sentences. All of them offer conversation flow capability. In other words, intents and entities help to understand what the incoming sentence is about. Once the incoming sentence is correctly identified you can use the engine to provide a reply. You can repeat these two steps a large number of times, thus creating a conversation, or dialog.

In terms of language processing ability and simplicity of user experience IBM Watson and Google DialogFlow are currently above the pack. Microsoft LUIS is okay too; still, keeping in mind that Microsoft are aggressively territorial and like when users stay within their ecosystem, it is most efficient to use LUIS together with other Microsoft products such as MS Bot Framework.

Using AI engine conversation flow to create dialogs makes building conversations a simple, almost intuitive, task, with no coding involved. On the flip side, using AI engine conversation flow limits your natural tendency to make conversations natural. The alternative, delegating the conversation flow to the business layer of your chatbot, adds richness and flexibility to your dialog but makes the process more comppcated as it now requires coding. Cannot sell a cow and drink the milk at the same time, can you?

Amazon Lex lacks the semantic sophistication of their competitors. One can say (somewhat metaphorically) that IBM Watson was created by linguists and computer scientists while Amazon Lex was created by sales people. As a product it is well packaged and initially looks pleasing on the eye, but once you start digging deeper you notice the limitations. Also, Amazon traditionally excelled in voice recognition component (Amazon Alexa) and not necessarily in actual language processing.

The space of conversational AI is fluid and changes happen rapidly. The existing products are evolving continuously and a new generation of AI engines is in the process of being developed.