Conversations are complex.
- They are complex because they are infinite – no one conversation looks identical to another.
- They are complex because there are a lot of them.
- They are complex because they are not structured.
The modern-day question that Customer Experience (CX) and Patient Experience (PX) leaders are faced with is, how can we leverage our existing customer conversations to drive insights and action for the business? How do we make sense of all these conversations?
The short answer: Accessible Machine Learning
Let’s define these terms individually:
- Machine Learning: Authenticx defines machine learning as a form of artificial intelligence that is capable of learning, predicting, and improving based on data received as an input.
- Accessible: Having access to a wider audience (marketing analysts, call center analysts, and business leaders).
When we put them together, Accessible Machine Learning puts the human in the loop and allows….
- The machine to be available to a wider audience of humans
- A wider audience of humans are available to the machine
Why is this important? Through the act of making the machine and humans accessible to each other, we improve the efficiency and effectiveness for each party.
- The machine continues to learn through a feedback loop from a diverse group of humans making the machine more inclusive and reliable.
- The humans become more efficient and effective, especially those in non-technical roles, because the machine is automating a portion of their work.
Here’s an example of Accessible Machine Learning in practice:
In 2022, Authenticx ingested and analyzed around 100 million conversations from patients, providers, payers, and pharmaceutical companies who are navigating the healthcare system. In these conversations we occasionally hear individuals who are stuck in their journey. We refer to this as The Eddy Effect.
From these 100 million conversations, humans who understand the context of the conversation have listened to a fraction of the calls – listening to every word spoken or exchanged via chat, tagging, labeling, and clipping segments of the calls.
When an Eddy is expressed in the interaction, humans tag it as having an Eddy, label elements of the call with additional perspective, such as what is causing the Eddy and what is the impact of the Eddy, and clip the corresponding segments of the call. These tags, labels, and clips provide the machine with high volumes of high quality training data – data that the machine uses to learn what an Eddy sounds like.
The machine can detect Eddies across 100 million conversations, with a high degree of confidence.
That is Accessible Machine Learning.
That is how humans are in the loop of machine learning.
That is the modern-day approach to voice of customer (VoC) at scale.
That is how to turn unused data into a rich source of insight for every part of the business.
Transform CX with Conversational Data
This guide provides the ultimate source of informations about unsolicited feedback and how it can be leveraged to transform your customer experience strategy.
ABOUT THE AUTHOR:
Leslie Pagel is the Chief Evangelist at Authenticx, a conversation analytics company dedicated to improving the way healthcare companies engage with patients. In this role, she creates awareness, across the healthcare industry, of more efficient and effective ways for healthcare organizations to deliver on their customer objectives. With over two decades of working with customer experience (CX) teams, Leslie helps clients actualize the voice of the patient to show how these voices prompt meaningful action.
Authenticx was founded to analyze and activate customer interaction data at scale. Why? We wanted to reveal transformational opportunities in healthcare. We are on a mission to help humans understand humans. With a combined 100+ years of leadership experience in pharma, payer, and healthcare organizations, we know first-hand the challenges and opportunities that our clients face because we’ve been in your shoes.
Want to learn more? Contact us!