Intent-driven Data Critical for Sales Growth

sasha uritsky

Dec 10, 2015

One of the most central causes of missed growth opportunities and overspending is a failure on the part of businesses to create strategies that are tailored to the intent of the consumer. Recognizing and harnessing visitor intent brings increased engagement with relevant messages and calls to action.

Once a business identifies purchase intenders it can create content that aligns with their needs and desires in order to increase the likelihood of conversion. Consequently it can pick up on pre-sale signals from visitors in the research phase and drive lead-nurturing initiatives accordingly. The ability to identify this spectrum of visitor intent is key to creating relevant engagement campaigns that drive sales.

nmodes has been at the forefront of delivering consumer intent to businesses.

We sort the intents based on conversation topics, called ‘streams’.

Here is a stream of people looking for a hotel:

A stream of people who are getting married:

A stream of people thinking of going on a cruise:

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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.

When Big Data is not so big anymore

We are inundated with information. There is so much information around us they coined a special term - Big Data. To emphasize the sheer size of it.

It is, of course, a problem - to deal with a large amount of data. Various solutions have been created to address it efficiently.

At nmodes we developed a semantic technology that accurately filters relevant conversations. We applied it to social networks, particularly Twitter. Twitter is a poster child of Big Data. They have 500 million conversations every day. A staggering number. And yet, we found that for many topics, when they are narrowed down and accurately filtered, there are not that many relevant conversations after all.

No more than 5 people are looking for CRM solutions on an average day on Twitter. Even less - two per day on average - are asking for new web hosting providers explicitly, although many more are complaining about their existing providers (which might or might not suggest they are ready to switch or looking for a new option).

We often have businesses coming to us asking to find relevant conversations and expecting a large number of results. This is what Big Data is supposed to deliver, they assume. Such expectation is likely a product of our ‘keyword search dependency’. Indeed, when we run a keyword search on Twitter, or search engines, or anywhere we get a long list of results. The fact that most of them (up to 98% in many cases) are irrelevant is often lost in the visual illusion of having this long, seemingly endless, list in front of our eyes.

With the quality solutions that accurately deliver only relevant results we experience, for the first time, a situation when there are no longer big lists of random results. Only several relevant ones.

This is so much more efficient. It saves time, increases productivity, clarifies the picture, and makes Big Data manageable.

Time for businesses to embrace the new approach.