Kevin Campbell - How We Measure Employee Experience to Drive Customer Outcomes

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Summary:

Kevin helps organizations acquire, develop, and retain their most valuable asset: their people. He also helps people manage an aspect of their lives that plays a key role in their happiness: their work. He’s spent the last decade of his career building leaders at every level and creating scores of engaged, high-performing, strengths-based teams. Prior to founding Lifted Leadership, Kevin served as a Lead People Scientist for Culture Amp where he helped organizations like Airbnb, Palo Alto Networks, and ServiceNow reinvent and optimize their performance management and employee engagement initiatives. 

In this episode, Kevin talks about how we measure employee experience to drive customer outcomes. 

Chapters:

[0:00 - 6:43] Introduction

• Welcome, Kevin!
• Today’s Topic: How measuring employee experience drives customer outcomes. 

[6:44 - 13:23] What is an Employee Experience Scientist? 

• How early interactions with management affect employee feelings of belonging
• Finding employee experience gaps and ways of closing them

[13:24 - 27:10] What are the first steps to connecting EX data to business and customer outcomes?

• Distinguishing causal and correlative data
• You don’t need to be a scientist to find a story in data

[27:11 - 36:14] What’s been learned form connecting EX data to customers?

• First, look at your data
• The benefits of labeling all scale points (and not just the extremes)

[36:15 - 39:09] Final Thoughts & Closing

• Humans value experiences; experiential data is where the magic happens
• Thanks for listening!

Quotes:

“A lot of times when we're trying to close experience gaps, we try to do it by improving the experience itself, which is absolutely warranted. But a lot of times the gap comes from the gap between expectations and experiences.”

“The very first thing is looking at your data. And I know that sounds almost too simple, but . . . just look at what the data are telling you before you even analyze the data before you even compare one data point or a set of data points to another.”

Contact:

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