Social Marketing is Simple

sasha uritsky

Sep 06, 2015

In its very essence social marketing is based on one simple foundation - give first, take later.

This concept of giving to the community is hardly possible to overestimate. It defines the way social networks operate and goes even deeper, to the basic principles of social interactions among humans.

In fact it is a much healthier foundation for business than traditional one, based on advertising.

Yet it runs contrary to what many entrepreneurs and business people perceive as a proper marketing approach.

Traditional marketing, such as billboards, radio ads, posters, banners, emails blasts, etc is based on two principles, a) the statistical law of big numbers, aiming to reach out to as large audience as possible while knowing that only a small percent would become interested, b) message of self-promotion and self-advertisment.

Social marketing negates both of these principles.

Social marketing is personal, it operates individually, and in a personalised way. Which makes perfect sense from a common perspective. Would you rather be bombarded by the generic ads that in most cases have nothing to do with your interests and desires, or approached on a one-on-one basis with a chance to discuss your specific needs?

Social marketing is directed towards promoting the interests of others, not yours (or your business). Again it makes sense as we are a social species, we live in societies and rely on communication. The most successful communication strategy is the one that takes care of the needs of your communication partner.

And so, opposing the traditional marketing approach, social marketing is based on the idea of giving to the community. Which makes it more efficient than traditional marketing, if measured against the effort applied. In other words, taken 100 random prospects, we are more likely to convert them into customers if using social marketing than traditional marketing.

But is it scalable?

(to be continued)

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.

Artificial Intelligence of Chatbots: What Do You Need to Know.

While Chatbots have been around for a little while now, their presence is more noticeable thanks to Facebook and Microsoft’s recent advancements.

Initially customers complained about the robot-like experience and the limited functionality of first generation bots and rarely found them useful. The customers were skeptical about how valuable in practice chatbots actually are, which has left recent AI vendors like nmodes with the task to combat the leftover stigma from the poor customer experiences and shortcomings of these initial offerings.

Chatbots, like an IVR?

We’re all used to calling into a contact center and punching numbers into a menu to be routed to the correct agent or service to address our needs. Interactive Voice Response solutions (IVRs) drive this interaction and are basically If/then routing trees that “listen” to the digit entered and “transfer” the user to the appropriate next step. While tremendous advancements in technology have brought voice recognition capabilities, those first generation IVRs were all about automated actions based on prompts. Enter your account number, press 1 to speak to an agent, etc…

The first generation Chatbots are just like an IVR. They can respond to prompts to progress through a predetermined process or display some canned information like pricing, a contact number, route to an agent, etc., but that was about the extent of it. Still 1^stgeneration Chatbots came with 4^thgeneration expectations. While these basic functions have tremendous value to a business, the customer expectation is very different when dealing with a phone call vs. a chat session. Consumers have experienced IVR routing for decades whereas chat is still relatively new and is perceived as a conversation with a person, rather than interacting with a machine. Add on the fact that many vendors and consumers mislabeled Chatbots as Artificial Intelligence in the beginning and the expectation of a dynamic, responsive customer experience is even greater.

So it’s no surprise that customers were less than impressed with “Artificial Intelligence” that could only display simple answers and basic information. We were expecting Hal from 2001: A Space Odyssey or KIT from Knight Rider, and we got a pixelated PONG instead.

Let’s talk…

Now, Artificial Intelligence has evolved to be integrated into Chatbots to deliver a more powerful user experience. While these new versions of Chatbots coming out are powered by Artificial Intelligence, AI powered chat also exists independent of bots in some instances. Confusing? Yeah, I was too.

The beauty behind true Artificial Intelligence is its ability to recognize the context of a conversation and respond with relevant, contextual information dynamically. A customer can now “speak” to technology the same way they would hold a conversation and the AI has the ability to “read” the customer’s intent to provide information quickly and efficiently. No more are you limited to a set of canned responses. The AI can reach in to a wider array of relevant information to craft unique responses based on any number of criteria. While in most cases AI is still limited to a few topics per use case, the technology is growing quickly, making almost daily improvements in functionality and customer experience.

What is even cooler is that the longer the AI is deployed, the more it “learns” and improves the speed and quality of responses. So while the scope of AI interactions is limited at first, the maturity curve is quick, delivering an ever-improving customer experience without having to invest in additional people, processes, or technology. It really is like a “growing up” of technology, right before your eyes.