Jul

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.

Interested in reading more? Check out our other blogs:

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.

 

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Easy Yet Untapped Revenue Channel for Hotels Worldwide

There are many travelers looking for hotels and places to stay on social web. Every day.

Take Twitter, for example:

 

Or this:



People are genuinely looking for help. Surprisingly though only few are getting it. According to nmodes data less than 12% of Twitter travel  requests are being answered. The rest - lost opportunities for hotels and businesses in the hospitality industry.  

 And how big is this opportunity anyway?

nmodes Twitter data shows that every 15 min somebody expresses intent of going to, or visiting New York. Most of these travelers need a place to stay there.

Every 33 min - intent of traveling to London.

Every 54 min - intent of traveling to Paris.

We started Twitter recommendation service @nmodesHelps and were encouranged by the results. 72% of those that received our travel recommendations reacted by thanking us and expressing their gratitude. This reinforced our assumption that people seek travel advice on Twitter, accept it as an instant value, and are prepared to act upon it.

The hotels that are ready to move fast to monetize this opportunity will benefit the most.

 

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