Customers, in essense, are the ultimate, most holistic Internet of Things (IoT) sensor array that any enterprise could possibly access. They only call when something needs attention, and they tell you exactly what needs to be fixed. The beauty of this sensor array is that it's already in place, and all you'll need to leverage it is conversational artificial intelligence (AI) powered by natural language processing (NLP).
The use cases discussed below describe the primary benefit of automation. The secondary benefit of implementing NLP solutions around these business processes is the ability to understand the product or service issues that drive customer defection.
Reduced Reliance on IVR Systems
Interactive voice response systems (IVR) are the primary source of customer effort in a contact center engagement today. Most organizations impose this effort on their customers in order to save money on labor. While they may be successful in reducing labor costs, the savings are often offset by increases in corporate spending on marketing required to replace lost customers.
Conversational AI, optimized to provide customer support in a rapid and precise manner, can eliminate the cumbersome interfaces of IVR systems. In addition, conversational AI solutions can provide automated support for many customer needs that are impossible to implement using an IVR system.
Conversational AI has the benefit of converting three to five minutes worth of IVR interaction into a 30- to 60-second conversation. This is the type of impact a conversational AI interface can have on a customer conversation.
Emotion Detection
Emotion detection, implemented as part of the real-time analytics within a conversational AI solution, can be applied to bot conversations as well as to conversations with contact center agents. Conversational AI solutions can provide enterprises with valuable information regarding customer happiness in real time, enabling the ability to escalate an interaction to a contact center agent or manager when negative emotions become apparent. This type of solution has the added benefit of eliminating the need for customer satisfaction or Net Promoter Score surveys.
Conversational AI doesn't stop working when interactions escalate to contact center agents. With conversational AI comes the opportunity to deliver robust context, via a detailed screen pop summarizing the customer journey, that will help the contact center agent understand the reason for the outreach and resolve the issue quickly. Further, conversational AI solutions can continue "listening" to interactions and, via screen pop, provide recommendations on how to resolve the customer's issue, necessary information for doing so, and even offers to expand the customer relationship.
From an IoT perspective, enterprises can begin to understand which product or service defects drive the most emotionally charged contact center interactions.
Advanced Skills-Based Routing
Traditional IVRs offer limited routing decisions based on the phone number dialed, the customer phone number, and the last navigation point of an IVR session. These are all hardcoded elements impervious to the dynamics of human interaction. In some financial institutions, for example, the cumbersome nature of IVR interactions combined with low-accuracy IVR routing functions result in transfer rates among contact center agents as high as 50%.
In addition, note that contact centers that heavily rely on call transfers pay a premium that can be as high as 20% of the contact center labor cost. Further, customers who are transferred from one agent to another have to exert more effort in getting their issues resolved. In other words, these contact centers pay more in labor to dissatisfy more of their customers -- a situation that in turn leads to high churn rates.
Conversational AI solutions, on the other hand, offer robust skills-based routing capabilities that kick into play when a customer interaction transfers from bot to human. They make routing calculations based on the entire content and context of the interaction and any metadata associated with the customer's journey. This approach leads to a far more accurate method of routing customers to contact center agents who are best-suited to solve their problems.
By using this routing technique customers can segment the differences between dissatisfaction driven by high transfer rates and dissatisfaction with a given product.
NLP and Analytics
The automation techniques discussed above are specifically designed to solve problems for customers. These interactions naturally contain a great deal of data that can be used to identify defects in products and services. The key here is that you can't get this additional insight unless you use NLP.
The similarities between customer interactions and IoT telemetry are profound. Enterprise contact centers process content and transactions that are a treasure trove of data useable by conversational AI solutions and AI-based analytics. If you're responsible for enterprise contact center operations or technologies, then conversational AI no doubt will be part of your life in the very near future, if it isn't already.
Learn more about AI's role in the contact center and for improved customer experience at Enterprise Connect 2018, March 12 to 15, in Orlando, Fla. Register now using the code NOJITTER to save an additional $200 off the Advance Rate or get a free Expo Plus pass.
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