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

Oct 14, 2016

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.

Interested in reading more? Check out our other blogs:

Beware the lure of crowdsourced data

Crowdsourced data can often be inconsistent, messy or downright wrong

We all like something for nothing, that’s why open source software is so popular. (It’s also why the Pirate Bay exists). But sometimes things that seem too good to be true are just that.

Repustate is in the text analytics game which means we needs lots and lots of data to model certain characteristics of written text. We need common words, grammar constructs, human-annotated corpora of text etc. to make our various language models work as quickly and as well as they do.

We recently embarked on the next phase of our text analytics adventure: semantic analysis. Semantic analysis the process of taking arbitrary text and assigning meaning to the individual, relevant components. For example, being able to identify “apple” as a fruit in the sentence “I went apple picking yesterday” but to identify “Apple’ the company when saying “I can’t wait for the new Apple product announcement” (note: even though I used title case for the latter example, casing should not matter)

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.