Artificial intelligence is helping companies across every industry solve entrenched challenges and better engage with customers, presenting tremendous growth opportunities and competitive advantages. It’s also creating buyer and consumer expectations for customized experiences whenever they interact with a business.
As machine learning is used to improve processes for sales, marketing, and customer service, more people are becoming accustomed to a highly personalized experience with companies, one which assumes a baseline understanding of every individual’s relationship to the business and the context of their lives. Companies that aren’t keeping pace with these expectations will lose ground with customers and lag within the overall marketplace.
One of the most significant areas of AI integration surrounds customer service applications.
AI assesses data from a variety of sources to contextualize customers and help contact centers improve the experience for those individuals. Gartner Research predicts that by 2021, nearly one in six of all customer service interactions will be completely handled by AI, an increase of 400% from 2017.
The rise of chatbots and other digital forms has decreased the dependency on voice communication in the contact center. Gartner’s research shows that by 2021, the proportion of phone-based communication within contact centers will drop from 41% to 12% of overall customer service interactions.
But customers don’t want to interact exclusively with robots. A human agent will still be involved in 44% of all interactions, according to Gartner, and some people will only communicate with a contact center by phone.
The goal of AI integration should not be to replace humans altogether, but to make those personal interactions smarter and more impactful.
Zeroing in on the voice of the customer
One of the myriad ways that machine learning can improve contact centers is by extracting complete information from patient interactions to automate both program quality reporting and the understanding of the “voice of the customer.”
Here’s the current challenge:
Manufacturers of specialty medications are obligated to review operating support programs to ensure that the execution complies with the approved design and applicable regulations (such as HIPAA). The approach today is to conduct a periodic, manual review of a small, random sample of program interactions.
The problem? These general overviews provide a narrow window, with limited insights into the voice of the customer and program performance. A great deal of information from these interactions isn’t being extracted or analyzed.
Deeper insights into the voice of the customer could help manufacturers adjust training, staffing, and operations at contact centers to improve compliance, expenditures, program quality, and the patient experience.
Specialty medication patient support programs have access to enormous amounts of patient data, but the contact centers which support these drugs lag behind other fields when it comes to integrating AI. Machine learning tools such as natural language processing (NLP) and speech to text conversion allow contact centers to gain deep insights into the voice of the customer.
The tools are sophisticated enough to gauge not only the objective data, such as topics that are top of mind for customers, but also more subjective data, like how customers perceive the support program(s) and experience their individual therapy journey. Machine learning applications are able to mine all of this data to surface hidden insights and create a complete holistic view of every patient.
Instantly. In real time. And at scale.
Without these types of automated tools, it’s impossible for patient support contact centers to fully leverage their data to optimize interactions with patients.
But with AI technology, manufacturers can develop program-specific solutions that automatically measure and report on support program quality for all customer interactions.
In addition, AI tools can automatically extract customer insights captured during all of their support program operations. This includes nuances such as customer sentiment and emotion during the call, which helps manufacturers understand not just what patients are saying but how they’re feeling.
When manufacturers leverage the power of AI to plumb their data, they can:
- Determine if their support programs are operating in a manner consistent with the approved program design and program goals.
- Identify support program best practices, timely opportunities for customer follow up, quality and compliance remediation, and training opportunities.
- Gain a clear understanding of the voice of the customer, including what topics are top of mind and how customers feel about both the support program(s) and their individual therapy journey.
- Identify patient reported side effects.
- Generate customer specific and aggregate support program “Pseudo Net Promoter Scores” (NPS).
Treating each patient interaction as a precious moment
Given the high-touch nature of patient support services, machine learning is a logical next step within this sector.
Every interaction with a patient is a precious moment, from which significant data can be extracted and acted upon, but none of this can be done at scale by humans alone. Unstructured data, such as text or audio, is time-consuming to sift through, making deep post-call analysis by people unfeasible for organizations. An Avaya report noted that if the average person can process 50 items of unstructured data an hour, it would take nearly seven years for one contact center employee to read through one million text items, at a cost of around $145,000.
Machine learning can do the same work in real time at a fraction of the cost.
Automating the analysis of patient interactions can transform the potential of patient support contact centers to deliver high quality and operational efficiencies, just as AI has done for contact sectors across so many other industries.
Patients are people first. All of us are becoming accustomed to a superior degree of customization in our daily interactions with companies of every kind. When patient support contact centers integrate machine learning, they will achieve the high standards of service that patients have come to expect.