Aug

Scalable Yet Personalized

How to offer businesses and organizations a solution that personalizes and scales consumer interaction process at the same time?

Personalizing the user relationship process. Today end users and consumers demand to be targeted individually and to be approached based on their actual interests. nmodes AI (Artificial Intelligence) powered solution helps organizations accurately identify user needs in real time. Our solution delivers information on each user individually thus providing the necessary level of personalization required of the successful customer service.

Scaling the user relationship process: Once the organization identifies a user and a problem that needs to be addressed, next step is reaching out to that user individually. Currently this is a manual non-scalable procedure. nmodes AI (Artificial Intelligence) solution provides automated assistance to human personnel, including substitution when deemed appropriate, thus making the entire process scalable.

Today more than 90% of all organizations and businesses rely on solutions based on keywords, even though these solutions provide low quality results not sufficient for the new generation of personalized scalable services.

nmodes solution enables sustainable delivery of high quality results, with x5 costs reduction and up to 45% increase in conversation (engagement) capacity.

 

Interested in reading more? Check out our other blogs:

Microsoft AI products

                                                 

Microsoft product strategy has always been and still remains that of ‘zero alternative’. Their ultimate policy is for their customers to have no choice but to embrace only Microsoft products. Consequently they created and are offering products and solutions in (almost) every segment of IT enterprise and consumer market, including, but certainly not limited to, their own data base, their own cloud services, operating system, office tools, programming language, and many more.

Not only do Microsoft offer wide variety of products, they tie them up together in a unified ecosystem that makes it easy for components to connect and interact. At the same time, this ecosystem is hostile to non-Microsoft products.

Microsoft strategy for the burgeoning, fast growing AI segment is similar:

Create products to address all parts of the AI market, add them to the ecosystem to ensure easy compatibility from within and difficulty of use from outside.

Currently the products on offer are:

- Microsoft AI engine, called LUIS. It is supposed to compete with other major industrial AI systems such as IBM Watson, and has similar training methodology. It offers webhook interfacing via endpoints.  

- Microsoft chatbot building platform, called, surprisingly, Microsoft Bot Platform. It addresses the popular demand for easy chatbot design and provides seamless connectivity with main user interfaces, such as web interface, SMS, mobile, and messaging platforms.

- In addition Microsoft offers their own messaging platform in Skype.

The main advantage of  using Microsoft AI products is the built-in connectivity with user interfaces.

The main disadvantage is in their ‘zero alternative’ policy - once you’ve chosen a Microsoft product you are likely will be forced to choose only Microsoft products for the duration of your project.

 

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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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