Artificial Intelligence Solutions Bundle
Social Engagement and Sales Accelerate When Using Intent-Driven Data
nmodes delivers consumer intent from social web to businesses. In real-time.
That is, if your company is selling cars we will connect you with potential customers out there that express intent to buy a car.
nmodes has partnered with a medium-sized travel company to help grow their social web sales channel. Our approach is to deliver consumer intent relevant to the company (people planning vacations, going on trips, flying to various world destinations, etc) and develop engagement strategies maximizing the impact of this consumer information.
Here are the results based on 4 months of data:
• The most efficient way to achieve short-term sales turned out to be individually targeted promo campaigns. For example, our travel partner created an attractive vacation destination package, and nmodes helped to spread the word on social media to those intended going on vacation.
A typical conversation start leading to promo offering. nmodes intent-based solution made it especially easy to target only relevant end users:
• The response rate varies geographically.
Canada - 20%
USA - 64%.
• The conversion rate is consistent across all locations and is slightly above 4%. When concentrating on vacation packages we were targeting 20-50 prospects daily, resulting in 2-4 sales per week, averaged $15,000 /mo or $200,000 /year.
• The potential for this particular market segment (all-inclusive vacations) in the US is at least x10 higher.
• The engagement was based on the combination of intent-based data and location data.
An intent-based sample for European destination package, travelers from USA:
While working with companies from various verticals we proved that intent-based data paired with location data offers a powerful opportunity to drive sales aggressively and accelerate business growth.
nmodes is best equipped to ensure that your business can benefit from this newly available power.
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.