Employees are then left to navigate outside social influences and the very personal and sometimes traumatic situations facing patients ( such as financial hardship , death , or job loss ).
A CUSTOMER DATA CONUNDRUM
The course of action to delivering an exceptional healthcare CX is diagnosing and understanding where customers get stuck .
Healthcare organizations generate an enormous amount of data about their customers , but the vast majority of that data goes unused . That ’ s where , and why , data analytics becomes such a powerful tool . It improves CX , providing actionable , structured insights to CX teams , and enabling companies to identify and fix the root causes of these frustrations .
Unsolicited feedback offers a huge opportunity to shift paradigms on the call center , turning them into " insight centers ", when leaders understand and apply the feedback throughout their organizations .
This customer data , however , generates its own challenges and opportunities .
Data exists in two types : structured and unstructured . Healthcare organizations rely on both to generate insights , including how to improve their customer service and the customer journey . But while each has value , there are downsides to consider as well .
STRUCTURED DATA NPS and CSAT scores share big picture insight into how a healthcare organization is performing and identify common customer issues , complaints , or requested service improvements .
These scores are easy to use and well known across the industry . They ’ re efficient at generating comparisons against the competition and providing management with a common language for classifying customers . NPS and CSAT commonly help with benchmarking .
However , there are a few disadvantages to this type of structured data , namely it tends to lack specificity and context . Further , both NPS and CSAT rely on solicited feedback and small sample sizes . And the time between surveys and results analysis is lengthy .
NPS AND CSAT SCORES SHARE BIG PICTURE INSIGHT INTO HOW A HEALTHCARE ORGANIZATION IS PERFORMING AND IDENTIFY COMMON CUSTOMER ISSUES ...
UNSTRUCTURED DATA Much customer data is unstructured — it ’ s generated from conversations with call center agents , chatbots , email responses , and social media . But this unstructured data offers an incredible wealth of insights once analyzed , leading to improved customer satisfaction and increased revenue .
First , the benefits . Because unstructured data comes in various formats , it yields more applications and use cases .
This data provides better insights and more opportunities to leverage it for a competitive advantage , offering healthcare organizations nearly limitless use . It ’ s also quick and easy to collect because it doesn ’ t have to be predefined . And companies can store it on-premise or in scalable cloud data lakes .
Now the downsides . The vast number of formats makes unstructured data difficult to analyze and use . And its large volume — plus the undefined formats — add to the difficulty of data management and make specialized tools a necessity .
TACKLING THE CHALLENGE OF DATA ANALYSIS
Customer interactions represent between 80-90 % of the world ’ s data . Ignoring it is bad business . Yet fewer than 20 % of enterprises use unstructured data meaningfully . The amount of unstructured data generated from customer interactions is daunting ( hence why some companies remain reluctant to use it ), yet it holds tremendous value .
Conversational data breaks the status quo by giving teams actionable insights into how to improve CX at all levels : from a broad 30,000-foot view to a zoomed-in , personalized analysis .
You can start by thinking of this analysis approach as a funnel : from the top , middle , and bottom . Here are three scenarios .
SCENARIO ONE ( THE TOP OF THE FUNNEL ) A Fortune 100 company noticed a spike in calls greater than 15 minutes , and its business leaders wanted to learn more about the nature of the calls and what was driving their increased volume and length . They used an unsupervised topic identification approach and learned :
• They were outbound calls to payers for benefits information .
• They included long hold times .
• They most commonly occurred on Fridays .
• Less experienced agents took longer to navigate the call and call length .
SCENARIO TWO ( THE MIDDLE OF THE FUNNEL ) The second scenario is about focus , determining where to focus and how to leverage machine learning ( ML ) to help predict consumer responses .
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