Our Success Rates Have Changed, Please Listen Carefully

Transcription

1 Our Success Rates Have Changed, Please Listen Carefully Leveraging IVR, Speech Recognition, and Artificial Intelligence to Optimize the Call Center

2 IVR systems were originally intended to effectively solve common, well defined problems that completed the customer interaction without the need of escalation to a live agent. Although intended to improve customer satisfaction, a survey taken by Amplicate in found that 88% of people hate IVRs. Not dislike, but hate. Why do so many people hate IVRs? In our research, we have found that IVRs were not implemented to solve customer problems. They were deployed to solve the problems of the customer service department to save costs by automating the call, and, if that failed, get it to an agent. Eighty percent of IVR implementations were done with little planning and developed haphazardly, usually driven by vendor promises of fast ROI. Making IVR Work for Your Organization The issue is not whether an IVR can deliver a good return on investment 2 but how to maximize that return. Here are the three most critical aspects of achieving the ROI for your IVR from a white paper published in 2009 by TellMe 2. Traditional interactive voice response (IVR) systems have not delivered on their promise. New advances in speech recognition technology and artificial intelligence have led to intelligent IVRs that demonstrate higher success rates and return on investment (ROI). 1) Select. Not every single transaction can be done effectively with an IVR or even automated. Some calls require a human agent listening and adjusting the rules to fit the situation or handling an exception that was not considered when the process was created (10 15% of interactions). Some interactions can be automated, but not via telephone as they require more information that is easily addressable via an automated interface (20 25% of interactions). The other 60 70% of interactions can successfully be handled by an IVR as long as the interaction is well understood, the process is documented, and it is suitable for telephone resolution. 2) Optimize. Once appropriate interactions are selected and programmed, their accuracy begins to drop. This occurs because the needs and desires of users and the way they interact with any process are constantly changing. For example, language changes often sometimes guided by common events such as a new television show or current event. Communication channels go in and out of favor. While texting may be all the rage today, users may shift back to communities and forums at a later time. The demographics of the customer base may also change, altering interaction styles as well. All these changes require the IVR processes, prompts and answers, and business rules to be constantly in flux to optimize the interactions. Analyses of customer feedback related to the way the IVR performs or fails to perform will affect it as well. 3) Measure. This is the trickiest part especially for call center managers. The inclination is to treat an IVR like another live agent: measure how many calls it received, how many calls it handled, how long each lasted, and the cost of each call. Call centers use this to calculate savings between how much it would have cost to handle the same calls via an agent versus the cheaper IVR channel. Too often, IVR performance is measured simply on efficiency (shorter calls and cheaper interactions for resolving issues) instead of customer centered effectiveness (such as customer satisfaction, customer effort, and level of frustration). The inclusion of the latter in particular yields a truer return on investment Our Success Rates Have Changed, Please Listen Carefully ~ 2 ~

3 Finding the Savings Once you have properly handled these three issues, you are in a position to start searching for ways to leverage your investment and achieve better returns. An effective IVR can become better by using two technologies: speech recognition and artificial intelligence. Figure A depicts the average added savings when speech recognition and artificial intelligence are added to a traditional IVR (which itself saves money versus a traditional live agent solution). Although the savings in our research averages 20%, we have seen it range from no savings to almost 80% in extreme cases. Speech IVRs are a poorly understood technology. While they require more work and planning than a traditional DTMF (touch tone) IVR, the rewards far exceed the added investment. A case study of OC Tanner found: Our initial statistics show a success rate of 62 percent on weekdays, increasing to 82 percent on weekends. Figure A Additional Savings from Adding IVR and IVR SR+AI to a Voice Deployment Our old DTMF solution delivered only 25 percent with a marginal customer experience. 3 Indeed, not only does effective speech recognition increase success rate (which is a far more effective metric to monitor than the number of calls received or number of minutes on the IVR), but it is also essential to automate more complex and higher time toresolution customer interactions effectively. Implementing speech has its nuances: we have seen failed implementations following the standard route: converting DTMF entries to guided speech recognition entries. There is more to using speech recognition in IVRs than perpetuating the model of guided menus. To leverage the benefits of speech recognition, customer interactions should be constructed to enable actual conversations beyond a preset menu and to leverage artificial intelligence. Enter Artificial Intelligence IVRs with artificial intelligence are demonstrating real value in customer service operations by raising success rates and providing much more intelligent, natural language interactions. Artificial intelligence goes beyond the concept of understanding language patterns to focus on the conversation itself. It is no longer about knowing what words or sentences are used, but what the context of those words is, how they relate to the conversation, and how to use that information to complete the next step in a transaction. IVRs with artificial intelligence perform very similarly to the way humans act during a conversation: they know how to use content and context to discover intent and complete the transaction ab3 4cc6 b5ca 58bccec08061/OCTanner.pdf Our Success Rates Have Changed, Please Listen Carefully ~ 3 ~

4 Growing Success Rates Let s look at an example. In a conversation about appointment scheduling, a smart IVR understands and keeps in context times of the day, days of the week and present versus future times. For example, saying in the afternoon indicates that an appointment should be between noon and five pm. This clear distinction helps shorten interactions and makes it far easier to solve issues and provide needed information. Organizations can stop building dialogs and scripts for every single potential interaction that a virtual agent is to have with a customer and instead focus on helping the IVR system understand the context of each term within recurring transactions. Understanding what a word or concept uttered by the customer means in the context of the conversation has risen dramatically, yielding far shorter and more effective interactions. According to a study published in Harvard Business Review, what customers really want (but rarely get) is just a satisfactory solution to their service issue." 4 Customers are now more eager to have interactions with these virtual agents and more satisfied with the outcomes. Integrating artificial intelligence with speech recognition further enhances performance. Our research has found that together they benefit organizations with added savings (more calls handled) and added success rates (more calls completed). Traditionally once the customer s Figure B Additional Success Rates Results from Deploying IVR SR+AI speech was translated into actionable terms, IVRs had a very short time to search for the proper data or knowledge to answer the question that was recognized. Using artificial intelligence to conduct searches in context and focus on more precise results has helped speech recognition move from structured, guided searches to more effective, free flowing conversations. We see encouraging results already surpassing what conversational engines that attempted to do this previously did. The difference is the use of artificial intelligence to better understand the content, the context, and the intent of the transaction. The results are transactions that are more focused on providing resolution to specific problems. Figure B shows average increase in success rates that result from using speech recognition and artificial intelligence, which translates into potential cost savings from a traditional IVR and live operators. Although we show average rates in this chart, we have seen success rates ranging from eight percent to nearly 100% in certain cases. 4 trying to delight your customers/ar/1 Our Success Rates Have Changed, Please Listen Carefully ~ 4 ~

5 One example of the higher performance enabled by this improved technology is an appointment scheduling application for a large pest control company 5. Customers can call to reschedule their appointments. The higher intelligence and speech recognition capability of SmartAction's IVR agents enables them to complete more calls by responding effectively to phrases like "how about later in the morning?" or "I need something on Thursday after 5 in May." SmartAction s smart call agents are successfully handling around half of the available calls while the traditional IVR used was only able to handle 5% of those calls. There are many other areas where these added success rates and savings are possible. Each organization must look for the best place to deploy them. It is certain that the time for speech recognition and artificial intelligence as a method to improve the effectiveness of an IVR deployment is today. It is definitely the right time to start planning for it. Further Reading To get more information on this matter, read the following online contact: 1. Why People Hate IVR, Amplicate, 2. Getting the Most From Your IVR, Microsoft om_your_ivr.pdf 3. O.C. Tanner Case Study, Microsoft, 8ab3 4cc6 b5ca 58bccec08061/OCTanner.pdf 4. Stop Trying to Delight Your Customers, Harvard Business Review, to delight your customers/ar/1 5. Terminix Case Study, SmartAction Newsletter, pages/ published august thinkjar llc all rights reserved no reproduction or use without license licensed exclusively to SmartAction 5 pages/ Our Success Rates Have Changed, Please Listen Carefully ~ 5 ~