Jan

Social selling for businesses

Social selling is one of the hottest buzzwords in the technology market. The popularity of social networks made the customer interaction and buyers hunting easier than before. More and more consumers are using social media to find deals, research products and make recommendations.

From the seller’s perspective the efficient use of social media is based on the mastery of following two major steps:

1. Finding the relevant audience,

2. Engaging with that audience.

The first step should be automated. This is exactly where the promise of Big Data, or Smart Data, as they now begin to call it, is supposed to come into fruition. Finding relevant information in the ocean of social data is the poster example of how Smart data can help businesses in the new world defined by computerized systems and networks. The companies should be able to use programs and solutions that accurately and efficiently deliver relevant data. If the company is spending time to sift through the ever increasing informational stream without automating the process, it is wasting precious time thus compromising its business growth and eventually losing competitive edge.

 The second step however is inherently manual. it is not a good idea to automate the engagement process. Social networks are designed to build trust, and trust cannot be won automatically. So it requires time and effort and knowledge. It also requires patience - trust cannot be built in minutes.

It is important that businesses looking to add social media into their arsenal of revenue channels, and we believe that all businesses should do just that, grasp this two-steps process. A clear understanding of the nature and requirements for each of the steps helps to plan strategically, manage the resources properly and avoid costly mistakes.

 

                               

Interested in reading more? Check out our other blogs:

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.

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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. 

 
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