Jan

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:

  1. Conversational AI should come as a service,
  2. The service should be available in natural language,
  3. 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.
Interested in reading more? Check out our other blogs:

Social selling. Difference between Facebook and Twitter

                                                         

There are obviously some key differences between Facebook and Twitter that make them appealing to different people as well as businesses. If possible, businesses should try to leverage both networks in their marketing and sales efforts.

But marketing approaches for each network differ.  Consequently social selling approaches differ as well. Here are some major differences of the two networks that impact sales strategy:

- Twitter lets all the accounts commingle, Facebook makes a definite distinction between business and personal. This can be an issue because a business page cannot proactively connect with individuals with personal profiles. Individuals have to first like a business page and still the business can’t reach out to them directly unless they message first. This is not the case with Twitter, as anyone can follow pretty much anyone.

- Facebook preferred way to market products and promote online sales can be compared to a showroom. The prospects can see the product and purchase it through some other channel, however engagement (with prospects) is limited to friends and followers. Hence growing the number of friends and followers becomes a critical task on Facebook.  Twitter does not offer promotional capabilities but engagement activity is not limited to followers. The engagement on Twitter is therefore more straightforward and can lead to direct sales.

- Facebook user data is typically open to friends or followers. Twitter data is typically open to the entire world.

- Twitter is fast (minutes). Facebook is slower (hours and days).

- Twitter is more about building a brand identity. Facebook is more about business relationships.

To summarize, a direct timely engagement could be a good strategy on Twitter. In a typical scenario a user tweets that she needs a taxi or asks where to dine tonight. A taxi company or a relevant restaurant engages in a conversation and secures a customer. It is an efficient approach with immediate ROI.

On Facebook a good strategy is to grow and educate a community of followers. Facebook is excellent for promotional campaigns. This is a longer-term strategy with effects not visible until after several months.

 

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Why Keywords Do Not Cut It on Social Search

Most of the online search is keywords-based. Same in social domain, a vast number of analytical tools, networking platforms and mobile apps use keyword-based technologies as well.

There is a difference, of course, between traditional internet search and social search. The former finds websites. The latter finds conversations, messages, posts. Keyword-based internet search is doing a decent job for us for over 20 years. Keyword-based social search is not doing a decent job at all.

Consider a basic example: finding on Twitter who is interested in buying jeans. We can start by typing ‘jeans’ but that brings up too much noise. Maybe ‘need jeans’? Less noise but then we  people who use expressions like ‘looking for jeans’ or ‘want jeans’ or shopping for jeans’. Not to mention those who use ‘denim’, or brand names. So we have to run multiple searches or create a complex search string using logical AND and OR and hope it works. Neither option is simple, or convenient, and certainly not efficient.

The above example highlights the major flaw with keyword search - it does not capture the meaning of social conversations, and therefore cannot be a reliable source of information about conversations.

It does not provide too much of correct information. And it does provide lots of incorrect information. But the biggest problem is that it has extremely limited potential for improvement.  

So as long as we stick with keyword-based social search the results are destined to be limited.

Why, then, we stick with keyword-based search in social search? Simply because there is no good alternative. Until recently, that is.  

The advanced semantic technologies capable of capturing the meaning, or intent, of conversations are now offering an exciting alternative.

I will discuss these technologies on my next blog.

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