One Fast Way To Increase Your Online Sales

sasha uritsky

Jan 22, 2018

Chatbots and conversational AI solutions offer easy and reliable sales channel.

Chatbots are helpful because they not only engage with your customers, they also retain them. This means that unlike other forms of marketing, chatbots keep your customers entertained for longer. For example, when a user has a customer care inquiry, the chatbot can answer it accurately and take the conversation a step further by offering personalized shopping advice based on the customer's purchase history and preferences.

Chatbots make communication with your brand proactive, thus enhancing your brand's reputation. By increasing interactions, they make a positive effect on your sales numbers.

Chatbots allow your business to reach out to a wider audience. They can gather and analyze customer feedback and data. Send relevant notifications. Make communicating with your brand more fun.

Chatbots help with top of funnel conversions. Offering your online customers a highly personalized experience of your site with the addition of a chatbot can help guide consumers down the sales funnel, and ultimately influence their buying decisions.

To summarize, chatbots opens new opportunities for you to increase sales. Opportunities that are easy to use and are not costly. Better not to miss them!

Interested in reading more? Check out our other blogs:

AI unmasked: Why long-term success of your business depends on conversational AI

For a business to grow successfully, it needs to scale its sales, customer service, marketing.

The only sustainable way to do this is to introduce an automated sales and customer experience service.

Conversational AI is the single available method to automate customer experience without reducing the quality of service. It comes in a variety of forms such as a chatbot, voice bot, virtual assistant, cognitive agent. They all share the scaling ability and the ability to deliver human-level quality of conversations.

what is conversational ainmodes.com

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.