How AI can benefit pharma call centers and customer service

Apart from some voice recognition to route calls in particular ways, most pharma call centers run as they have for decades. But it doesn’t have to be this way. AI applications are already being used to take these interactions to a far more productive and successful level. Here is what’s working now in various aspects of the call center:

Sorting and routing enquiries

It’s easy to understand why most call centers these days use Interactive Voice Response (IVR) technology to route calls. You reps’ time is better spent engaged with callers rather than transferring them to other people. But standard IVR doesn’t eliminate the time and hassle of sorting and routing calls. It merely shifts the onus to callers.

No one wants to spend time working through multiple audio prompts and decision trees. They want their questions answered as quickly as possible. Because AI can sort more data and analyze spoken language better than traditional IVR technology, the process is more seamless. With an AI-powered IVR, callers can simply ask for what they want the same as they would if they were talking to a real person.

Messaging

Not all calls need a real live human. In fact, a good number of simple inquiries and repetitive interactions can be handled effectively by virtual agents without damaging customer relationship. Callers don’t mind. In fact, the technology can often handle these tasks faster, and that’s what callers really want.

That doesn’t mean that AI is ready to completely replace human agents, though. More complex and nuanced situations still require a human touch. In those situations AI can assist agents by proactively providing relevant information and recommending specific messaging for each particular caller to provide for faster resolution.

And with sentiment analysis, call centers can quickly determine from conversations what a caller is feeling (i.e. angry, frustrated, content, etc.), predict what that caller would like to happen next and serve those recommendations to reps in real time.

When done right, AI can seamlessly blend both of these solutions, so that a caller starts out with a virtual agent then is transitioned to an AI-assisted human when the situation call for it. Not only does this free up the human agents to focus on the tasks where they provide the most value, it provides a more positive experience for callers. The entire call is optimized to meet their needs without adding extra steps or barriers they find with most non-human interactions.

Call analysis

Call centers have all the data a pharma company needs to drive important decisions, including insights on what key complaints or calls about and how they are being handled. The problem has been capturing the relevant data, analyzing it and turning it into actionable intelligence.
Before AI advances, companies had to rely on tactics such as sifting through call recordings, holding focus groups and send out post-call surveys. But this gives far from a complete picture. There’s too much data to use – thousands of hours of recordings every day – and how many people can you expect to participate in surveys and focus groups?

Even if you got through all the recordings and got every caller to respond to surveys, it would take months to analyze and act on the information. By that time there could easily be new trends you aren’t even aware of.

New technologies solve these problems. It is now possible to capture massive amounts of information at the time of the call and feed it into analytics engines capable of uncovering patterns within the data. Just imagine the benefits this intelligence could deliver to areas such as resource planning, sales and marketing, in addition to customer service.

Best of all, with machine learning, AI solutions get “smarter” as they process and analyze data. That improves their ability to predict the outcomes of different courses of action and model best practices.