For example , one pharmaceutical company brought a new drug to market . Despite the company ’ s forecasts , it was blown away by the public ’ s overwhelming response : they received six times more calls than initially expected .
During the post-launch period , the organization used automated and manual methods to help keep its finger on the pulse of these conversations and gather conversation data . They pulled 500 calls from the middle of the funnel and used ML to understand the nature of those conversations . Analysts listened to an additional 100 calls and teams met weekly to discuss , evaluate , and address raised concerns .
This approach supported immediate change , such as offering agent coaching and developing proactive ways to address common pain points , resulting in reduced call volume . The middle of the funnel is about specific focus and using ML to understand the nature of some calls .
THE AMOUNT OF UNSTRUCTURED DATA GENERATED FROM CUSTOMER INTERACTIONS IS DAUNTING ... YET IT HOLDS TREMENDOUS VALUE .
SCENARIO THREE ( THE BOTTOM OF THE FUNNEL ) Human listening also occurs at the bottom of the funnel , where conversations become very immersive and highly contextual .
For example , one hospital network saw an increase in mental and behavioral health calls , noting much higher call volumes , especially for pediatric patients . They listened to both sides of the conversations — parents calling on behalf of their children — and nurses responding to those calls .
Further analysis found that the nurses ' responses or follow-up questions weren ’ t appropriate based on the information parents shared .
50 CONTACT CENTER PIPELINE
A parent might share a very emotional , urgent situation — such as a mother sharing that her daughter had tried to overdose — but the nurse responded according to their script . In this case , the nurse asked for the patient ’ s name and birth date .
Other instances showed where the nurses could have done further triaging by collecting additional information and asking more probing questions to fully understand the patients ' situations . Data gathered from the bottom of the funnel identified opportunities to train agents further and encourage empathy during customer interactions on an individualized level .
Examining data based on a customer ’ s journey in the funnel is one step to finding and using relevant insights to inform operational approaches , agent training and improvements to the CX .
OPPORTUNITIES TO IMPROVE CX
We learn a lot from listening to customers . The stories they share generate deeper insights into disruptions they experience along their journeys . They ’ re telling us about the hidden barriers they face , their frustrations , and what motivates them , in every interaction .
And yet , leadership teams aren ’ t immersed daily in the customer or patient experience . Storytelling and call montages provide good solutions for gathering and analyzing data , generating insights , and ultimately helping healthcare organizations understand their patients ' challenges .
By listening to and learning from our customers ’ voices , we ’ re better able to visualize their journeys and all their disruptions .
We can then hone in on critical moments , pain points , and unsolicited feedback and use that information to understand frustrations and develop strategies to prompt meaningful change and deliver a frictionless CX .
Unstructured data can help identify the common themes in healthcare gaps — confusion about insurance , claims , and prior authorizations , frustration with billing or insurance coverage , and lack of information about eligibility or program availability for financial aid and other support .
With this data , healthcare leaders can use the data to :
• Understand historical trends .
• Forecast the future .
HEALTHCARE CX
STORYTELLING AND CALL MONTAGES PROVIDE GOOD SOLUTIONS FOR GATHERING AND ANALYZING DATA , GENERATING INSIGHTS ...
• Uncover new strategies to deliver a better CX .
• Identify what to explore next .
Everything boils down to listening to patients . We gain insight into the patient journey by systematically listening to every word spoken in those interactions and using tools like ML help in a scalable way . What we glean from analyzing these interactions enable organizations to train agents to deliver authenticity and use the human touch , leveraging empathy to reassure patients their stories are heard . They ’ re not alone , they are understood , and there is a solution to resolve their frustrations .
Amy Brown is the founder and CEO of Authenticx – a platform that analyzes and activates patients ’ voices at scale to reveal transformational opportunities in healthcare . She built her career in the healthcare industry , advocating for and expanding coverage for underserved populations , and learned the nuance of corporate operations .