We live in a new world of customer expectations, where data and analytics rule the day and companies that are able to more effectively turn customer data into actionable intelligence will have a decided competitive advantage. Customers understand they are sharing information with every digital interaction – and they are willing to exchange that data for a better and more personalized experience.
We are also in a very self-service-oriented world, where customers increasingly prefer to do their own research or problem solving, using live customer service as a secondary option, only if needed. In fact, while the use of self-service channels is growing, a large majority of customers (71 percent) say they still want to be able to connect with a live agent when needed.
The imperative for businesses is twofold. First, they must provide the tools to enable customers to find answers on their own by providing well-designed web-based tools along with an advanced, intelligent IVR system. That includes Visual IVR, which effectively combines the methodology of IVR with digital channels using a visual UI.
Secondly, they must ensure that not only can customers easily reach live representatives when needed, but that the make the transition from self-service to live agent seamless. The age of self-service has not only given rise to a new breed of customer resources, but also incredible amounts of customer data that can and should be used to create an exceptional customer experience.
When customers find themselves having to contact customer service, they expect a high level of service. In fact, the customers are becoming less tolerant of sub-par service, and exhibit a growing tendency to find alternate vendors after receiving poor customer service. A recent survey noted 54 percent of people have stopped doing business with a company due to poor customer service, an increase of 5 percent over the previous year. That figure is even higher (61 percent) when it comes to Millennials, which now make up the single largest segment of the U.S. workforce. Likewise, customers tend to reward a good experience, with 68 percent of all people reporting they have done more business with companies that deliver good customer service. Again, that number is much higher with Millennials (78 percent).
Knowing these facts, businesses must place high importance on ensuring continuity between self-service and live customer service delivery. That means not only collecting customer data when they use IVR portals, other self-service tools, or even prior live agent interactions, but integrating that data in customer records and analytics engines. There are countless sources of data that can be used to generate intent data: online browsing and searches, recent orders and purchases, reservations and bookings, previous IVR activity, and service subscriptions, just to name a few. When properly used, this data can help both AI engines and live agents know why a customer is calling, chatting, or texting when – or even before – they do.
For instance, a cable customer may be experiencing a service outage. An AI engine can identify the caller and verify service status as soon as the customer calls into customer support. If there is a known outage, the AI engine can let the customer know the provider is aware of an outage and inform them of expected restoration time frames. For an even more customer-focused approach, as soon as an outage becomes known, systems can be designed to proactively notify impacted customers through to let them know the issue is being addressed.
Perhaps a customer has gone started using an IVR system and can’t find the information she is looking for, and then asks to speak to a live agent. The history from that IVR engagement can be immediately appended to the customer record, so the agent not only know who the customer is (if she is calling from a number on record, or if she was already asked for account information), but has a good idea of what she is trying to resolve from the IVR system. With intelligent voice-based IVR systems, in fact, agents can know exactly what the issue is, nor just a general idea. With this information, agents are able to address the customer personally and immediately get to the problem, circumventing a need to explain the problem again.
Instead, maybe the same customer was simply not able to finish the engagement and, while in the process of working through a self-service system, had to hang up to take an important call. If she dials back 15 minutes later, an intelligent, proactive IVR system can identify the caller and recognize she left in the middle of an engagement, and ask her if she is calling for the same reason and would like to pick up where she left off previously, again saving time and a need to repeat information.
A common example where caller intent can be used to expedite customer service is the airline industry, with missed, delayed, or cancelled flights, or missing luggage. Because airlines have passenger and flight information at their disposal, they can instantly predict why a customer is calling, especially if they have access to self-service or live agents directly through a mobile app. As the customer initiates an engagement, the airlines automated system can let him know it is aware he has missed his flight and ask if he would like help rebooking on another flight from San Francisco to New York (or whatever the departure and arrival cities may be). Here again, the ability to know why the customer is calling saves time, but more importantly, gives the customer a sense of importance thanks the personalization and context made possible by data.
The examples are endless, but the data to provide context for automated IVR or live customer engagements is available for nearly every business, and the importance of putting that data to use cannot be overstated. The data can be used on an individual customer basis, but companies can also identify trend data related to specific product purchases or customer demographics to predict caller intent, such as a recurring problem with a specific product that has resulted in a high volume of calls.
The common thread is the ability to collect and use data across different engagement channels to predict customer intent and to deliver continuity of experience across those channels. Delivering an added layer of contextual continuity and personalization across interactions, not only are issues able to be resolved more efficiently, but customers gain a sense of importance, building loyalty and increasing future revenue opportunities.