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Data Analytics: The Rx for CX

Data Analytics: The Rx for CX

/ Operations, Technology
Data Analytics: The Rx for CX

Challenges, opportunities in using data analytics.

Of all the lines of business contact centers serve, healthcare is arguably the most important, for peoples’ lives depend on it, but also at the same time challenges exist. About a quarter of healthcare customers become stuck along their journeys daily, according to our Customer Voices Report.

Many reasons contribute to such a high rate of disruption. The American healthcare system, with its providers, insurers, and pharmaceutical verticals, is quite complex. Each has its nuances, regulatory requirements, and roles.

But what these participants share is the responsibility of interacting with customers every day — hundreds of millions of them — who need their help.

When customers must navigate multiple systems and different branches within the healthcare system, they may find themselves trapped by the Eddy Effect — one issue leads to another issue which leads to another issue.

These roadblocks can include a lack of clarity about business language, frustration with billing or benefits explanations, and an inability to understand prescription tiers.

As a result, the time to resolution (TTR) can take too long. This metric is valuable in the customer experience (CX) for the agents, companies, and customers. No one wants to spend hours navigating a complex phone menu system to try and resolve problems.

About a quarter of healthcare customers become stuck along their journeys...

A customer journey disrupted by one or more of these factors leads to:

  • Poor CX and low customer satisfaction.
  • Negative impacts on customer retention, loyalty, and sentiment.
  • Gaps in patient care — and confusion about treatments, insurance (including Medicare), and other healthcare variables.
  • Wasted customer service resource hours trying to resolve preventable customer problems.

Disruptions from long wait times and delayed time to resolution can impact employee performance, morale, and lead to feelings of burnout and attrition—which is a direct impact on individuals’ health and wellbeing; interactions in healthcare are often more than simply transactional.

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.

NPS and CSAT scores share big picture insight into how a healthcare organization is performing and identify common customer issues...

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.

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.

The amount of unstructured data generated from customer interactions is daunting...yet it holds tremendous value.

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.

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.

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.

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.

Storytelling and call montages provide good solutions for gathering and analyzing data, generating insights...

With this data, healthcare leaders can use the data to:

  • Understand historical trends.
  • Forecast the future.
  • 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

Amy Brown

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.

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