Feb

Integrated Real-Time Data Boosts Content Delivery

How to make content more relevant and appealing to the content consumer?

This is a problem that has been on the mind of content creators for some time now. In our age of information abundance it is not easy to stand out and make your voice heard. The competition for the consumer’s attention is escalating, and with the number of information sources ever increasing, it will only get tougher.

Traditionally, a content delivery does not change across the target audience. A commercial, or a blog, looks and is experienced in the same way by all viewers and readers. We are entrenched in this paradigm, and can hardly imagine it being otherwise.

It turns out, the advancement of new technologies capable of capturing individual intents in real time brings up new opportunities in creating personalized experiences within the framework of content delivery.  

This is how content can become more relevant - by becoming more personalized.

In a rudimentary form, we are already familiar with this approach as seen in online advertising. Some web and social resources aim at personalizing their promotional campaigns based on whatever drops of behavioural patterns and interests they can squeeze out of our web searches.  The problem, of course, is that the technologies used to power these campaigns understand human behaviour poorly and results, therefore, more often than not leave a great deal to be desired. To put it mildly.

nmodes has been working on semantic processing of intent for several years. We now can capture intent from unstructured data (human conversations) with accuracy of 99%. (Interestingly, many businesses do not require this level of accuracy, being satisfied with 90%-92%, but we know how to deliver it anyway).

We recently started to experiment with personalizing content by using available consumer intent.

We used Twitter because of its real-time appeal.

We started by publishing a story, dividing it into several episodes:

 

And we kept the constant stream of data flowing, concentrating on intent to dine in Paris:

We then merged the content of the story with consumer intent to dine in Paris as captured by our semantic software. Like this:

This merging approach shows promising results - the engagement rate jumped above 90%.

Overall we are only at the beginning of a tremendous journey. We know that other companies are beginning to experiment, and the opportunities from introducing artificial intelligence related technologies into content delivery are plentiful.

There is a long road ahead, and we've made a one small step.  But it is a step in a very exciting direction.

 

Interested in reading more? Check out our other blogs:

Building 24x7x365 Customer Support and Online Sales... FOR FREE (Almost)

                                                             

We’ve all seen the numbers and they tell us that customers are more likely to make a purchase if they’re able to speak to a representative at the time of purchase. Study after study shows that if you can prevent even the smallest percentage of customer defection revenues and profitability can literally skyrocket as much as 80%. Just as important, the faster is your service the better is customer experience.

The same can be said for customer support. More than 70% of customers say that responsive customer support providing fast, courteous, relevant and contextual answers to their inquiries are the most important factors in determining the quality of customer service and the likelihood of that customer doing business with the company in the future.

As our world becomes even more “on-demand” and global, providing around the clock sales and customer support is quickly becoming a key differentiator. Customer’s desire to do business with companies on their own schedule and terms are driving financial growth and customer loyalty across all sectors and industries. Companies that neglect this “always on” requirement not only lose out, but need to find ways to be competitive.

Unfortunately, only the largest companies have the financial resources to deliver 24x7 customer support and sales operations. Still many of the largest companies can’t justify the expense of building out and staffing a 24 hour contact center. While outsourcing to a BPO is always an option, statistics show a diminishing return for outsource customer and sales support operations.

As customers continue to drive up the use of chat and social communications for customer support and sales, along with the incredible growth in Artificial Intelligence technology, smart companies on the forefront of customer service now have the ability to offer around the clock service for a large portion of their customers.

Think about this: While the average phone support call has previously been measured at almost 6 minutes, the average chat session lasts just 42 seconds, indicating that the vast majority of customer support issues are simple and only require limited information in order to leave a customer informed and satisfied with the interaction.

Today Artificial Intelligence can deliver a personalized, informed, and contextually relevant response to just about any question related to most customer inquiries. Add on the fact that AI actually “learns” as it interacts with people and information and the value to the customer and the vendor actually increases over time.  Wouldn’t we all like to have immediate service with zero wait times and fast, courteous response that immediately addresses our needs? I know I would.

Implementing Artificial Intelligence for customer service comes down to an application cost that, when amortized over the number of chat or social sessions it can handle, reduces customer support costs to as little as 10% of traditional contact center and agent expenses.

The one objection to relying on Artificial Intelligence in the contact center is the customer experience. There’s enough bad press out there about Chatbots and broken, robotic responses that are sometimes irrelevant that some customer support professionals are wary of any form or automation. My response to that is, while those were valid concerns; just take a look at Siri today vs. 2 years ago. The quality of responses has dramatically improved, as has the customer perception and usefulness.

What are your thoughts about Artificial Intelligence in the contact center? We’d love to hear from you.

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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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