How today's AI bots are akin to IVR-and can advance it.
Then chat and email channels arrived in customer service centers (or, by that point, contact centers) around the year 2000. A somewhat revolutionary thing that was now possible at this stage was referred to as auto responses, which involved a machine responding automatically with preset responses based on word spotting, and also preset responses that agents could choose while in the throes of dialogue with their customers.
Was this AI? Well, not really. But it was really the beginning of the application of knowledgebase responses to customer service inquiries, even if that knowledgebase was really just a small set of rules. Regardless, the rules knew to send this response. However, those rule sets could not learn; they could only be expanded upon by humans intent on studying their customer service organization events and then applying new rules.
Fast forward to the now, and we see the rise of artificial intelligence, or more accurately machine learning. Imagine a computer-driven system that, instead of executing programmatic activities to the letter of its programmer, was given the power of complex algorithms that can allow a computer system to learn from and make predictions about responses from vast amounts of data. Feed the computer system more data, and it continues to adapt its knowledge based on analysis of trends in the data. We see evidence of these machine learning networks everywhere these days--an obvious example being search engines trying to predict what you are wanting to search for and providing relevant responses despite spelling or grammatical errors or that tiling that decides what to put into your trash email folder.
How does this relate to customer service? If you've ever used chat to communicate with a business seeking customer service, there's a good chance you were chatting with a machine, a chatbot, and there's a good chance you didn't even know it. The same goes for an email trail; you can't be sure it was a human agent who typed that accurate and helpful response.
Imagine a customer service organization that could offer not only self- service in these digital channels, but also include the ability for these digital channels to actually be helpful without agent intervention. Chatbots are doing just that. They communicate in written form, some latency is expected in this semi synchronous communications medium, and there is no detection of a mechanical voice to be heard. Their responses are preprogrammed to communicate in a culturally relevant style. They're able to communicate with knowledgebases that contain vast amounts of data to draw upon for solutions and answers. And lastly they (or their AI knowledgebases behind the scenes) have the ability to learn and adapt, and with every interaction comes more data the machine learning algorithms may exploit to formulate more accurate and helpful responses and solutions to customer service inquiries.
Does this mean that we no longer need agents? No.
Nothing can replace human interaction in customer service, not yet at least. Any customer service organization would be wise to back these self-service engines up with human agents. In the same way that an agent sometimes needs to consult an expert, so too will machines need to consult humans--especially if that machine doesn't have the data to feed its algorithm to compute a meaningful response.
Also consider machine learning being aided by and also aiding human agents (i.e. machines learning from the solutions that the human agents apply) and providing helpful predicted responses that agents could choose from when speculating on what response to provide to that customer for that issue.
Circling back to the IVR discussion: Remember those natural language IVRs that were so reviled? Well, those speech engines were driven primarily by a programmatic set of grammars. If you didn't have the grammar written down for the speech engine to match phonemes to, it wouldn't recognize what was being said. This is why speech applications required long bouts of tuning (listening to utterances and expanding the grammars to accommodate).
Machine learning can help here too. Driven mostly by search engines like Siri, and graduating into personal assistants like Alexa, these applications are learning to cope with the dynamic characteristics of human speech communications every day by listening and analyzing. IVR may also exploit those now much smarter speech recognition engines, for what is the likes of Siri and Alexa but an IVR that you don't have to dial a number to reach?
I expect the trend toward broader use and application of AI-based technologies in customer experience to grow in the coming years. The growth will be based on the value these technologies deliver in helping organizations learn and adapt to customer needs at scale. This is especially true as organizations compete more on the customer experiences they create than on traditional levers they can pull, such as price and product benefits. --Pamela McGlone, vice president of customer experience transformation at AIorica The typical customer engagement approach, which relies on sending pre-scheduled campaign content to mass audiences, is not enough for today's highly demanding customer's who expect to be treated as a segment-of-one and contacted at the right moment. It's about treating the customer as an individual. Customer-specific and contextually relevant engagement is a powerful tool driving customer value that bears huge revenue potential while cutting operational costs. --Niilo Fredrikson, executive vice president of intelligent data at Comptel AI provides a promising way to quickly analyze data and provide the right message for them in the moment. For example Under Armour is one of the many companies working with IBM's Watson and it combines the user data from its Record app with third-party information on fitness, nutrition and other personalized information that the user is interested in. --Anthony Pappas, president of brand marketing and customer experience at DMI We are starting to see that chatbots and conversational digital interactions are moving beyond the curiosity phase and into normal operations. Most consumers, 58 percent according to our research, would prefer their customer service interaction to be through a text or messaging app. The expectation is that simple to moderate requests should be satisfied without needing to contact a customer service rep. There are many benefits that come from this new interaction model, including freeing up contact center agents to spend more time on higher-value, more complex, activities resulting in better service, and a dynamic shift in how the contact center is staffed and managed going forward. --Joe Gagnon, chief customer strategy officer at Aspect Software The move toward AI-enabled CRM affects how decisions are made within organizations and how to sell. Instead of focusing efforts around selling to a single decision maker in a company, AI can analyze CRM data to equip businesses with the tools to personalize messages to multiple decision makers in real time. This adds overall value to customer's by enhancing efficiency, and driving revenue through better business processes and available insights. "When it comes to commerce, AI through Salesforce Einstein will give organizations greater insight into customers--for example, leveraging big data to identify when customers are most likely to buy. Even better, AI will enable businesses to forecast purchasing trends and inform their ability to target commerce promotions, streamline subscriptions, and more. Ultimately, Einstein will help Salesforce users extend their personalization efforts and predict market demands within a single platform that will enable a better experience for their customers. --Shawn Belling, vice president of product development and support at CloudCraze "Software developers and end user organizations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process. --David Schubmel of research company IDC "Over the next few years we'll have AIs that translate what we say, with nuance in place, to any other language; AIs that can, with punctuation, do speech-to-text for us, speeding up our correspondence and decreasing dramatically our chances of looking stupid and ending our careers. It is somewhat comforting to know we will also have AIs that could be used to significantly offset, and perhaps eliminate, this risk so that instead of appearing insanely foolish we actually look smarter. --Rob Enderle, president and analyst at the Enderle Group The ability of AI systems to transform vast amounts of complex, ambiguous information into insight has the potential to do amazing things: discover insights to treat disease, predict the weather, or manage the global economy. --Guru Banavar, chief science officer of cognitive computing and VP of IBM Research One topic that was covered ad nauseam in 2016 was AI. While it's important to be cautious about all of the AI hype (especially when it comes to use cases that sound like science fiction), the reality is that this technology is going to evolve even faster from here on out. --Sean Zinsmeister, senior director of product marketing at Infer "Across Microsoft we've invested billions, over multiple decades, into cutting-edge AI research to help every person and organization achieve more. Office 365. Bing Predicts and Skype Translator are just a few examples of how we have put these investments to work for our customer's. We are also bringing those investments to you through Dynamics 365. Designed to help improve manufacturing and supply chain execution, make field service operations more efficient, sell more effectively and ultimately deliver exceptional customer experiences, built-in intelligence capabilities are infused throughout Dynamics 365 apps including: sentiment and intent analysis, preemptive service, relationship insights, lead and opportunity scoring, product recommendations and up-sell/cross-sell, and many more. --Oct. 11 AI blog by Microsoft "In 2017, we'll see more capabilities when it comes to artificial intelligence and customer service like Alexa triggering a call from contact center based on a question about online order status, thermostats submitting a trouble ticket after noticing a problem with the heater, or Siri searching through a cable company's FAQ to answer to a commonly asked question about internet service troubleshooting. However, one thing will always remain true--human interactions will still be critical when dealing with complex situations or to provide the empathy that is needed in customer service. --Mayur Anadkat, vice president of product marketing at Five9 "We can use machine learning to drive a lot of the rote work out of the contact center and support teams...to improve efficiency of the contact center and lead to better customer outcomes. --Sam Boonin, vice president of product strategy at Zendesk" "The answer to maintaining good customer service with multiplying channels is integrating AI with traditional, conversational communications to augment the experience, not completely change it. No matter how AI technology evolves, human interaction will always be a key element far a positive customer experience with the contact center. --Rajeev Shrivastava, chief strategy officer at inCantact "Chatbots and more intelligently leveraged IVR applications are getting better at adopting and integrating AI to enable greater efficiency gains and better utilization of frontline employees or digital interactions. The necessity of typing numbers on your phone to a guided path, or even having the IVR repeat everything back to you will soon go away. And, where it isn't a complete substitution, more and more AI will be used to assist employees serving customers, better known as augmented intelligence. --Justin Thompson, vice president of strategy for MaritzCX
BY LARRY BROWN
Larry Brown is head of product management for Telax (www.telax).
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|Date:||Jan 1, 2017|
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